Proceedings of the 7th International Conference on Advances in Energy Research [1st ed.] 9789811559549, 9789811559556

This book presents selected papers from the 7th International Conference on Advances in Energy Research (ICAER 2019), pr

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Proceedings of the 7th International Conference on Advances in Energy Research [1st ed.]
 9789811559549, 9789811559556

Table of contents :
Front Matter ....Pages i-xxi
Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy (Shripad Kulkarni, Alex Bernard)....Pages 1-13
CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir (Pratiksha D. Khurpade, Somnath Nandi, Pradeep B. Jadhav, Lalit K. Kshirsagar)....Pages 15-24
Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels (Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, Sukanta Kumar Dash)....Pages 25-34
Decentralized Solid Waste Management for Educational-Cum-Residential Campus: A Pilot Study (Deepak Singh Baghel, Yogesh Bafna)....Pages 35-44
Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? (Subhanker Paul, Suparna Paul, Maria Fernandino, Carlos Alberto Dorao)....Pages 45-54
Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India (Niranjan Rao Deevela, Tara C. Kandpal)....Pages 55-69
Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India (Tarun Kumar Aseri, Chandan Sharma, Tara C. Kandpal)....Pages 71-79
Optical Modeling of Parabolic Trough Solar Collector (Anish Malan, K. Ravi Kumar)....Pages 81-89
Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit (Ahana Sarkar, Ronita Bardhan)....Pages 91-101
Development of an Improved Cookstove: An Experimental Study ( Himanshu, S. K. Tyagi, Sanjeev Jain)....Pages 103-109
Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load (Jayesh Priolkar, E. S. Sreeraj)....Pages 111-121
Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas ( Bharathi Raja, R. Sarathi, Ravikrishnan Vinu)....Pages 123-129
Thermo-Hydrodynamic Modeling of Direct Steam Generation in Parabolic Trough Solar Collector (Ram Kumar Pal, K. Ravi Kumar)....Pages 131-140
Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis of Pinewood Over Various Catalysts (Kavimonica Venkatesan, Parasuraman Selvam, Ravikrishnan Vinu)....Pages 141-148
Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver (Asmelash Haftu Amaha, Prabhu Ramachandran, Shivasubramanian Gopalakrishnan)....Pages 149-158
Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector (Mousami Prasad)....Pages 159-168
Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage (Jaynendra Kumar, Anshul Agarwal, Nitin Singh)....Pages 169-180
Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling (Sai Yagnamurthy, Dibakar Rakshit, Sanjeev Jain)....Pages 181-190
Experimental Evaluation of Common Rail Direct Injection Compression Ignition Engine with EGR Using Biodiesel (Suresh D. Mane, Chinna Bathulla)....Pages 191-199
Emission Measurement Considerations for Power Industry (A. Bekal, S. K. Karthick, Y. Rajeshirke, G. Balasubramaniam, M. Upadhyay, S. Bhandarkar et al.)....Pages 201-210
Impact of Growing Share of Renewable Energy Sources on Locational Marginal Prices (Leena Heistrene, Yash Shukla, Yaman Kalra, Poonam Mishra, Makarand Lokhande)....Pages 211-221
Performance Evaluation of Wind-Solar Hybrid System in Indian Context (Rahul Shityalkar, Ranjan Dey, Anagha Pathak, Niranjan Kurhe, Sandesh Jadkar)....Pages 223-230
Structural, Electrical and Cell Performance Study on Lithium Germanium Phosphate Glass Ceramics-Based Solid-State Li-Electrolyte (Anurup Das, Madhumita Goswami, P. Preetham, S. K. Deshpande, Sagar Mitra, M. Krishnan)....Pages 231-239
Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations (Kirti Gupta, Saumendra Sarangi)....Pages 241-251
Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications (Vishal D. Chaudhari, Govind N. Kulkarni, C. M. Sewatkar)....Pages 253-263
Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle (Kamaljyoti Talukdar)....Pages 265-273
Performance Study of an Anode Flow Field Design Used in PEMFC Application (S. A. Yogesha, Prakash C. Ghosh, Raja Munusamy)....Pages 275-284
Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine (Siddharth Ramachandran, Naveen Kumar, Mallina Venkata Timmaraju)....Pages 285-294
Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning (A. D. Kulkarni, K. S. Wani)....Pages 295-304
Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement (Anindita Roy, Rajarshi Sen, Rupesh Shete)....Pages 305-315
Saving Electricity, One Consumer at a Time (K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, Sumedha Malaviya)....Pages 317-326
Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed ( Saurabh, D. S. Murthy)....Pages 327-337
Integrated Thermal Analysis of an All-Electric Vehicle (Vinayak Kulkarni, Shankar Krishnan)....Pages 339-348
Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR (B. Anupreethi, Anurag Gupta, Umasankari Kannan, Akhilanand Pati Tiwari)....Pages 349-357
ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study (Saurabh Khobaragade, Priyanka Bhosale, Priya Jadhav)....Pages 359-369
Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector (Nagesh B. Balam, Tabish Alam, Akhilesh Gupta)....Pages 371-381
Rice Paddy as a Source of Sustainable Energy in India (Mohnish Borker, T. V. Suchithra)....Pages 383-392
Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra (Pankaj Kumar, Trupti Mishra, Rangan Banerjee)....Pages 393-402
Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying (Mallikarjun Pujari, P. G. Tewari, M. B. Gorawar, Ajitkumar P. Madival, Rakesh Tapaskar, V. G. Balikai et al.)....Pages 403-415
Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System (Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, Atal Bihari Harichandan)....Pages 417-425
Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process (M. B. Gorawar, S. V. Desai, V. G. Balikai, P. P. Revankar)....Pages 427-437
Desalination Using Waste Heat Recovery with Active Solar Still (Sandeep Kumar Singh, S. K. Tyagi, S. C. Kaushik)....Pages 439-447
Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage (Ammu Susanna Jacob, Rangan Banerjee, Prakash C. Ghosh)....Pages 449-462
Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application (M. R. Dhivyashree, M. B. Gorawar, V. G. Balikai, P. P. Revankar)....Pages 463-479
Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters (Sourish Ganguly, Subhrasish Pal, Ankur Bhattacharjee)....Pages 481-491
A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter (P. Sriramalakshmi, Sreedevi V. T.)....Pages 493-503
Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 (Gunjan Kumar, Biswajit Thakur, Sudipta De)....Pages 505-513
Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha (Sneha Swami, Subodh Wagle)....Pages 515-523
Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory (Neha Manjul, Mahiraj Singh Rawat)....Pages 525-536
Experimental Investigation of Solar Drying Characteristics of Grapes (S. P. Komble, Govind N. Kulkarni, C. M. Sewatkar)....Pages 537-546
Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling (Shipra Tiwari, Saumendra Sarangi)....Pages 547-557
Performance Assessment and Parametric Study of Multiple Effect Evaporator (Pranaynil Saikia, Soundaram Ramanathan, Dibakar Rakshit)....Pages 559-574
An Approach Towards Sustainable Energy Education in India (Pankaj Kalita, Rabindra Kangsha Banik, Samar Das, Dudul Das)....Pages 575-585
Simulation-Based Economic Optimization of Nuclear Renewable Hybrid Energy Systems with Reliability Constraints (Saikrishna Nadella, Anil Antony, N. K. Maheshwari)....Pages 587-597
Exergy Analysis and Cost Optimization of Solar Flat Pate Collector for a Two-Stage Absorption Refrigeration System with Water-Lithium Bromide as a Working Pair (Abhishek Verma, S. K. Tyagi, S. C. Kaushik)....Pages 599-610
Characterizing the Helical Vortex Frequency of HAWT (Ojing Siram, Niranjan Sahoo)....Pages 611-620
Design and Development of Concentrated Solar Cooker with Parabolic Dish Concentrator (Susant Kumar Sahu, Natarajan Sendhil Kumar, K. Arjun Singh)....Pages 621-631
Thermal and Electrical Performance Assessment of Elongated Compound Parabolic Concentrator ( Chandan, Sumon Dey, V. Suresh, M. Iqbal, K. S. Reddy, Bala Pesala)....Pages 633-643
Thermodynamic Analysis of a 500 MWe Coal-Fired Supercritical Thermal Power Plant Integrated with Molten Carbonate Fuel Cell (MCFC) at Flue Gas Stream (Akshini More, A. Pruthvi Deep, Sujit Karmakar)....Pages 645-654
Three-Dimensional Investigation on Energy Separation in a Ranque–Hilsch Vortex Tube (Nilotpala Bej, Pooja Chaubdar, Anish Pandey, K. P. Sinhamahapatra)....Pages 655-663
Bamboo Plant Intellect Deeds Optimization Algorithm for Solving Optimal Reactive Power Problem (Kanagasabai Lenin)....Pages 665-672
Actuator Fault Detection and Isolation for PEM Fuel Cell Systems Using Unknown Input Observers (Vikash Sinha, Sharifuddin Mondal)....Pages 673-683
Analysis of Heating and Cooling Energy Demand of School Buildings (Tshewang Lhendup, Samten Lhendup, Hideaki Ohgaki)....Pages 685-694
Thermodynamic Performance Analysis of Adsorption Cooling and Resorption Heating System Using Ammoniated Halide Salts (Rakesh Sharma, K. Sarath Babu, E. Anil Kumar)....Pages 695-705
Correlating Partial Shading and Operating Conditions to the Performance of PV Panels (S. Gairola, M. K. Sharma, J. Bhattacharya)....Pages 707-716
Engineering of O2 Electrodes by Surface Modification for Corrosion Resistance in Zinc–Air Batteries (Imran Karajagi, K. Ramya, Prakash C. Ghosh, A. Sarkar, N. Rajalakshmi)....Pages 717-723
Energy Farming—A Green Solution for Indian Cement Industry (Kapil Kukreja, Manoj Kumar Soni, B. N. Mohapatra, Ashutosh Saxena)....Pages 725-734
Energetic and Exergetic Performance Comparison of a Hybrid Solar Kalina Cycle at Solar and Solar Storage Mode of Operations (P. Bhuyan, P. Borah, T. K. Gogoi)....Pages 735-745
Assessment of Different Multiclass SVM Strategies for Fault Classification in a PV System (Rahul Kumar Mandal, Paresh G. Kale)....Pages 747-756
Performance Analysis of Double Glass Water Based Photovoltaic/Thermal System (Ajay Sharma, S. Vaishak, Purnanand V. Bhale)....Pages 757-766
Modeling Polarization Losses in HTPEM Fuel Cells (Vamsi Ambala, Anusree Unnikrishnan, N. Rajalakshmi, Vinod M. Janardhanan)....Pages 767-773
Effect of Diesel Injection Timings on the Nature of Cyclic Combustion Variations in a RCCI Engine (Ajay Singh, Rakesh Kumar Maurya, Mohit Raj Saxena)....Pages 775-784
Investigating the Impact of Energy Use on Carbon Emissions: Evidence from a Non-parametric Panel Data Approach (Barsha Nibedita, Mohd Irfan)....Pages 785-794
Studies on the Use of Thorium in PWR (Devesh Raj, Umasankari Kannan)....Pages 795-804
Coaxial Thermal Probe for High-Frequency Periodic Response in an IC Engine Test Rig (Anil Kumar Rout, Santosh Kumar Hotta, Niranjan Sahoo, Pankaj Kalita, Vinayak Kulkarni)....Pages 805-813
Effect of Injection Pressure on the Performance Characteristics of Double Cylinder Four-Stroke CI Engine Using Neem Bio-diesel (Sushant S. Satputaley, Iheteshamhusain Jafri, Gauravkumar Bangare, Rahul P. Kavishwar)....Pages 815-824
Experimental Study of a Helical Coil Receiver Using Fresnel Lens (Sumit Sharma, Sandip K. Saha)....Pages 825-835
Substrate-Assisted Electrosynthesis of Patterned Lamellar Type Indium Selenide (InSe) Layer for Photovoltaic Application (A. B. Bhalerao, S. B. Jambure, R. N. Bulakhe, S. S. Kahandal, S. D. Jagtap, A. G. Banpurkar et al.)....Pages 837-845
Optimization of Injector Location on the Cylinder Head in a Direct Injection Spark Ignition Engine (Srinibas Tripathy, Sridhar Sahoo, Dhananjay Kumar Srivastava)....Pages 847-855
Automated Cleaning of PV Panels Using the Comparative Algorithm and Arduino (Huzefa Lightwala, Dipesh Kumar, Nidhi Mehta)....Pages 857-868
Performance and Degradation Analysis of High-Efficiency SPV Modules Under Composite Climatic Condition (Shubham Sanyal, Arpan Tewary, Rakesh Kumar, Birinchi Bora, Supriya Rai, Manander Bangar et al.)....Pages 869-878
Energy Literacy of University Graduate Students: A Multidimensional Assessment in Terms of Content Knowledge, Attitude and Behavior (Divya Chandrasenan, Jaison Mammen, Vaisakh Yesodharan)....Pages 879-889
Waste-to-Energy: Issues, Challenges, and Opportunities for RDF Utilization in Indian Cement Industry (Prateek Sharma, Pratik N. Sheth, B. N. Mohapatra)....Pages 891-900
Predict the Effect of Combustion Parameter on Performance and Combustion Characteristics of Small Single Cylinder Diesel Engine (D. K. Dond, N. P. Gulhane)....Pages 901-912
Experimental Investigation of a Biogas-Fueled Diesel Engine at Different Biogas Flow Rates (Naseem Khayum, S. Anbarasu, S. Murugan)....Pages 913-921
Characteristics of an Indigenously Developed 1 KW Vanadium Redox Flow Battery Stack (Sreenivas Jayanti, Ravendra Gundlapalli, Raghuram Chetty, C. R. Jeevandoss, Kothandaraman Ramanujam, D. S. Monder et al.)....Pages 923-929
Dynamic Demand Response Through Decentralized Intelligent Control of Resources (M. T. Arvind, Anoop R. Kulkarni)....Pages 931-943
Transient Analysis of Pressurizer Steam Bleed Valves Stuck Open for 700 MWe PHWRs (Deepraj Paul, S. Pahari, S. Hajela, M. Singhal)....Pages 945-952
Transient Analysis of Net Load Rejection for 700 MWe IPHWRs (S. Phani Krishna, S. Pahari, S. Hajela, M. Singhal)....Pages 953-959
A Comparative Experimental Investigation of Improved Biomass Cookstoves for Higher Efficiency with Lower Emissions (Sandip Bhatta, Dhananjay Pratap, Nikhil Gakkhar, J. P. S. Rajput)....Pages 961-971
Assessment of Floating Solar Photovoltaic (FSPV) Potential in India (Ashish Kumar, Ishan Purohit, Tara C. Kandpal)....Pages 973-982
Effect of Non-Revenue Water Reduction in the Life Cycle of Water–Energy Nexus: A Case Study in India (Rajhans Negi, Vipin Singh, Munish K. Chandel)....Pages 983-990
Policy Intervention for Promoting Effective Adaptation of Rooftop Solar PV Systems (Sabreen Ahmed, C. Vijayakumar, Arjun D. Shetty)....Pages 991-1001
Improved Dispatchability of Solar Photovoltaic System with Battery Energy Storage (Sheikh Suhail Mohammad, S. J. Iqbal)....Pages 1003-1013
Numerical Investigation of the Performance of Pump as Turbine with Back Cavity Filling (Rahul Gaji, Ashish Doshi, Mukund Bade)....Pages 1015-1024
Mining Representative Load Profiles in Commercial Buildings (Kakuli Mishra, Srinka Basu, Ujjwal Maulik)....Pages 1025-1036
A Simplified Non-iterative Method for Extraction of Parameters of Photovoltaic Cell/Module (Kumar Gaurav, Neha Kumari, S. K. Samdarshi, A. S. Bhattacharyya)....Pages 1037-1047
Design, Analysis and Hardware Implementation of Modified Bipolar Solid-State Marx Generator (Neelam S. Pinjari, S. Bindu)....Pages 1049-1059
Viability Study of Stand-Alone Hybrid Energy Systems for Telecom Base Station (M. Siva Subrahmanyam, E. Anil Kumar)....Pages 1061-1070
Effect of Temperature and Salt Concentration on the Properties of Electrolyte for Sodium-Ion Batteries (Bharath Ravikumar, Surbhi Kumari, Mahesh Mynam, Beena Rai)....Pages 1071-1081
Carbon Deposition on the Anode of a Solid Oxide Fuel Cell Fueled by Syngas—A Thermodynamic Analysis (N. Rakesh, S. Dasappa)....Pages 1083-1090
Numerical Study on CO2 Injection in Indian Geothermal Reservoirs Using COMSOL Multiphysics 5.2a (Nandlal Gupta, Manvendra Vashistha)....Pages 1091-1102
Modification in the Rotor of Savonius Turbine to Reduce Reverse Force on the Returning Blade (J. Ramarajan, S. Jayavel)....Pages 1103-1111
Design and Fabrication of Grating-Based Filters for Micro-thermophotovoltaic Systems (M. V. N. Surendra Gupta, E. Ameen, Ananthanarayanan Veeraragavan, Bala Pesala)....Pages 1113-1119
A Systematic Investigation on Evaporation, Condensation and Production of Sustainable Water from Novel-Designed Tubular Solar Still (Mihir Lad, Nikunj Usadadia, Sagar Paneliya, Sakshum Khanna, Vishwakumar Bhavsar, Indrajit Mukhopadhyay et al.)....Pages 1121-1130
Novel Design of PV Integrated Solar Still for Cogeneration of Power and Sustainable Water Using PVT Technology (Nikunj Usadadia, Mihir Lad, Sagar Paneliya, Sakshum Khanna, Abhijit Ray, Indrajit Mukhopadhyay)....Pages 1131-1143
Cellulose Nanocrystals Incorporated Proton Exchange Membranes for Fuel Cell Application (Saleheen Bano, Asif Ali, Sauraj, Yuvraj Singh Negi)....Pages 1145-1153
Study of the Effect of Biomass-Derived N-Self Doped Porous Carbon in Microbial Fuel Cell (Saswati Sarmah, Minakshi Gohain, Dhanapati Deka)....Pages 1155-1163
Analysis of Nature-Inspired Spirals for Design of Solar Tree (Sumon Dey, Madan Kumar Lakshmanan, Bala Pesala)....Pages 1165-1173
Effective Use of Existing Efficient Variable Frequency Drives (VFD) Technology for HVAC Systems—Consultative Research Case Studies (Rahul Raju Dusa, Atulkumar Auti, Vijay Mohan Rachabhattuni)....Pages 1175-1184
Thermodynamic Analysis of a Combined Power and Cooling System Integrated with CO2 Capture Unit of a 500 MWe SupC Coal-Fired Power Plant (Rajesh Kumar, Goutam Khankari, Sujit Karmakar)....Pages 1185-1198
DFT Studies on Electronic and Optical Properties of Inorganic CsPbI3 Perovskite Absorber for Solar Cell Application (Abhijeet Kale, Rajneesh Chaurasiya, Ambesh Dixit)....Pages 1199-1206
Biowaste Derived Highly Porous Carbon for Energy Storage (Dinesh J. Ahirrao, Shreerang D. Datar, Neetu Jha)....Pages 1207-1214
Bio-Ethanol Production from Carbohydrate-Rich Microalgal Biomass: Scenedesmus Obliquus (Maskura Hasin, Minakshi Gohain, Dhanapati Deka)....Pages 1215-1224
Safety Analysis of Loss of NPP Off-Site Power with Failure of Reactor SCRAM (ATWS) for VVER-1000 (Manish Mehta, Sanuj Chaudhary, Anirban Biswangri, P. Krishna Kumar, Y. K. Pandey, Gautam Biswas)....Pages 1225-1236
P-type Crystalline Silicon Surface Passivation Using Silicon Oxynitride/SiN Stack for PERC Solar Cell Application (Irfan M. Khorakiwala, Vikas Nandal, Pradeep Nair, Aldrin Antony)....Pages 1237-1244
Pressure Propagation and Flow Restart in the Subsea Pipeline Network (Lomesh Tikariha, Lalit Kumar)....Pages 1245-1253
Electrodeposition of Cu2O: Determination of Limiting Potential Towards Solar Water Splitting (Iqra Reyaz Hamdani, Ashok N. Bhaskarwar)....Pages 1255-1264
Design and Development of an Economical and Reliable Solar-Powered Trash Compactor (Ridhi Lakhotia, Abu Fazal, Ajay Yadav, Ankur Bhattacharjee)....Pages 1265-1273
Performance and Emission Characteristics of CI Engine Fueled with Plastic Oil Blended with Jatropha Methyl Ester and Diesel (S. Babu, K. Kavin, S. Niju)....Pages 1275-1286
Performance Analysis of Hybrid Photovoltaic Array Configurations Under Randomly Distributed Shading Patterns (Vandana Jha)....Pages 1287-1296
Flow Improvement Aspect with Stagger Angle Variation of the Subsequent Rotor in Contra-rotating Axial Flow Turbine (Rayapati Subbarao)....Pages 1297-1307
Performance Assessment of Pelton Turbine with Traditional and Novel Hooped Runner by Experimental Investigation (Vimal K. Patel, Hemal N. Lakdawala, Sureel Dohare, Gaurang Chaudhary)....Pages 1309-1318
Evaluation of LVRT Control Strategies for Offshore Wind Farms (M. M. Kabsha, Zakir Rather)....Pages 1319-1330
An Experimental and CFD Analysis on Heat Transfer and Fluid Flow Characteristics of a Tube Equipped with X-Shaped Tape Insert in a U-Shaped Heat Exchanger (Sagar Paneliya, Sakshum Khanna, Jeet Mehta, Vishal Kathiriya, Umang Patel, Parth Prajapati et al.)....Pages 1331-1348
Single-Particle Analysis of Thermally Thick Wood Particles in O2, N2, CO2 Atmosphere (Shruti Vikram, Sandeep Kumar)....Pages 1349-1359
An Analysis for Management of End-of-Life Solar PV in India (Snehalata Pankadan, Swapnil Nikam, Naqui Anwer)....Pages 1361-1371
Localized Energy Self-sufficiency (Energy Swaraj) for Energy Sustainability and Mitigating Climate Change (Chetan Singh Solanki, Sayli Shiradkar, Rohit Sharma, Jayendran Venkateswaran, Nikita Lihinar, Harshad Supal et al.)....Pages 1373-1381
Pseudocapacitive Energy Storage in Copper Oxide and Hydroxide Nanostructures Casted Over Nickel-Foam (Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay, Abhijit Ray)....Pages 1383-1391
Validation of Computer Code Based on Nodal Integral Method Against KAPS-2 Phase-B Data (Manish Raj, Sherly Ray, A. S. Pradhan, Suneet Singh)....Pages 1393-1401
Bipolar DC Micro-Grid Based Wind Energy Systems (Dodda Satish Reddy, Suman Kumar, Bonala Anil Kumar, Sandepudi Srinivasa Rao)....Pages 1403-1413
Processing Thermogravimetric Analysis Data for Pyrolysis Kinetic Study of Microalgae Biomass (Pravin G. Suryawanshi, Vaibhav V. Goud)....Pages 1415-1424
Photovoltaic Thermal Collectors with Phase Change Material for Southeast of England (Preeti Singh, Rajvikram Madurai Elavarasan, Nallapaneni Manoj Kumar, Sourav Khanna, Victor Becerra, Sanjeev Newar et al.)....Pages 1425-1430
Modeling and Simulation of Hollow Fiber Biocatalyst Membrane Reactor (Nooram Anjum, Mohammad Danish, Sarah Anjum)....Pages 1431-1440
Efficient Alkaline Peroxide Pretreatment of Sterculia foetida Fruit Shells for Production of Reducing Sugar: Effect of Process Parameters on Lignin Removal (S. Sardar, A. Das, S. Saha, C. Mondal)....Pages 1441-1451
Performance Enhancement of Savonius Hydrokinetic Turbine with a Unique Vane Shape: An Experimental Investigation (Vimal K. Patel, Kushal Shah, Vikram Rathod)....Pages 1453-1463
Techno-economic Analysis for Production of Biodiesel and Green Diesel from Microalgal Oil (Swarnalatha Mailaram, Nitesh Dobhal, Sunil K. Maity)....Pages 1465-1475
Numerical Investigation on the Effect of EGR in a Premixed Natural Gas SI Engine (Sridhar Sahoo, Srinibas Tripathy, Dhananjay Kumar Srivastava)....Pages 1477-1487
Transitions in the Indian Electricity Sector: Impacts of High Renewable Share (Aishwarya V. Iyer, Rangan Banerjee)....Pages 1489-1500
Comparison of Physics Characteristics of Pressurized Water Reactor Type Advanced Light Water Reactors (L. Thilagam, D. K. Mohapatra)....Pages 1501-1511
Development of a Python Module “SARRA” for Refuelling Analysis of MSR Using DRAGON Code (A. K. Srivastava, Anurag Gupta, Umasankari Kannan)....Pages 1513-1519
The Effect of Concentration Ratio and Number of P-N Thermocouples on Photovoltaic-Thermoelectric Hybrid Power Generation System (Abhishek Tiwari, Shruti Aggarwal)....Pages 1521-1532
Evaluation of Annual Electrical Energy Through Semitransparent (Glass to Glass) and Opaque Photovoltaic Module in Clear Sky Condition at Composite Climate: A Comparative Study (Rohit Tripathi, Nitin K. Gupta, Deepak Sharma, G. N. Tiwari, T. S. Bhatti)....Pages 1533-1542
Current Practices and Emerging Trends in Safety Analysis of NPPs (K. Obaidurrahman)....Pages 1543-1549
Electrochemical Reduction of CO2 on Ionic Liquid Stabilized Reverse Pulse Electrodeposited Copper Oxides (Nusrat Rashid, Pravin P. Ingole)....Pages 1551-1558
Performance of Flux Mapping System During Spatial Xenon Induced Oscillations in PHWRs (Abhishek Chakraborty, M. P. S. Fernando, A. S. Pradhan)....Pages 1559-1570
Forecasting of Electricity Demand and Renewable Energy Generation for Grid Stability (Joel Titus, Urvi Shah, T. Siva Rama Sarma, Bhushan Jagyasi, Pallavi Gawade, Mamta Bhagwat et al.)....Pages 1571-1581
Platooning of Flat Solar-Panel-Mounted Mini Bus Model—A Numerical Investigation (Mohammad Rafiq B. Agrewale, R. S. Maurya)....Pages 1583-1593
Co-sensitization of Perovskite Solar Cells by Organometallic Compounds: Mechanism and Photovoltaic Characterization (Nisha Balachandran, Temina Mary Robert, Dona Mathew, Jobin Cyriac)....Pages 1595-1601
Nuclear Power Plants and Human Resources Development in South Asia (Firoz Alam, Rashid Sarkar, Akshoy Ranjan Paul, Abdulaziz Aldiab)....Pages 1603-1613
Highly Stable Pt/CVD-Graphene-Coated Superstrate Cu2O Photocathode for Water Reduction (Chandan Das, Akhilender Jeet Singh, K. R. Balasubramaniam)....Pages 1615-1621
Thermodynamic Studies on Steel Slag Waste Heat Utilization for Generation of Synthesis Gas Using Coke Oven Gas (COG) as Feedstock (M. Srinivasarao, Nilu Kumar, A. Syamsundar)....Pages 1623-1632
Reactivity-Initiated Transients for 700 MWe PHWR (Suresh Kandpal, M. P. S. Fernando, A. S. Pradhan)....Pages 1633-1643
Multi-field Solar Thermal Power Plant with Linear Fresnel Reflector and Solar Power Tower (Shridhar Karandikar, Irfan Shaikh, Anish Modi, Shireesh B. Kedare, Balwant Bhasme)....Pages 1645-1655
Experimental Investigation Using Enriched Biogas in S-I Engine for Stable Rural Electrification (Antony P. Pallan, Deepak Mathew, Yohans Varghese)....Pages 1657-1666
Solar Autoclave for Rural Hospitals Using Aerogel as Transparent Insulation Material (Manoj Kumar Yadav, Anish Modi, Shireesh B. Kedare)....Pages 1667-1677
Numerical Investigation on the Influence of Reactant Gas Concentration on the Performance of a PEM Fuel Cell (Brajesh Kumar Kanchan, Pitambar R. Randive, Sukumar Pati)....Pages 1679-1689
Energy Efficiency Analysis of a Building Envelope (M. Y. Khan, A. Baqi, A. Talib)....Pages 1691-1702

Citation preview

Springer Proceedings in Energy

Manaswita Bose Anish Modi   Editors

Proceedings of the 7th International Conference on Advances in Energy Research

Springer Proceedings in Energy

The series Springer Proceedings in Energy covers a broad range of multidisciplinary subjects in those research fields closely related to present and future forms of energy as a resource for human societies. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute comprehensive state-of-the-art references on energy-related science and technology studies. The subjects of these conferences will fall typically within these broad categories: – – – – – – –

Energy Efficiency Fossil Fuels Nuclear Energy Policy, Economics, Management & Transport Renewable and Green Energy Systems, Storage and Harvesting Materials for Energy

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More information about this series at http://www.springer.com/series/13370

Manaswita Bose Anish Modi •

Editors

Proceedings of the 7th International Conference on Advances in Energy Research

123

Editors Manaswita Bose Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

Anish Modi Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai, Maharashtra, India

ISSN 2352-2534 ISSN 2352-2542 (electronic) Springer Proceedings in Energy ISBN 978-981-15-5954-9 ISBN 978-981-15-5955-6 (eBook) https://doi.org/10.1007/978-981-15-5955-6 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The 7th International Conference on Advances in Energy Research (ICAER 2019) was organized by the Department of Energy Science and Engineering, Indian Institute of Technology Bombay between 10 and 12 December 2019 in Mumbai, India. The conference received around 350 submissions. Of these, around 165 submissions were accepted for oral presentation and 25 submissions were accepted for poster presentations after a rigorous peer review. The conference was attended by over 450 participants. This book is a compendium of selected papers presented at the conference. Two pre-conference workshops, the International Workshop on Hydrogen Storage and the Springer Author Workshop on Scientific Writing for Journals, were also organized on 09 December 2019. The conference had ONGC as the title sponsor, Coal India Limited as the gold sponsor, Department of Science and Technology (DST-SERB) as sponsor, Springer as best paper award sponsors, and ARCI, TCI Chemicals, NCPRE, and Energy Swaraj Foundation as exhibition stall partners. Prof. Ajit Kolar, IIT Madras sponsored three best paper awards. The pre-conference workshops and the conference hosted 31 invited lectures and presentations by academics and industry personnel from all over the world. Two special sessions on ‘Future of coal research’ and ‘Industry innovations in energy’ were also organized. The conference took several unique steps to ensure long term sustainability. These included minimizing the use of single-use plastic, registration kits made of cloth and notebooks made of recycled paper by local self-help groups, and planting of 100 medicinal plants to offset the carbon footprint from the flights of the invited speakers. The conference concluded with a panel discussion on energy transitions and energy security. Mumbai, India

Manaswita Bose Anish Modi

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Contents

Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy . . . . . . . . . . . . . . . . . . . . . . . . . Shripad Kulkarni and Alex Bernard CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pratiksha D. Khurpade, Somnath Nandi, Pradeep B. Jadhav, and Lalit K. Kshirsagar Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels . . . . . . . . . . . . . . . . . . . . . . . . . . Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, and Sukanta Kumar Dash Decentralized Solid Waste Management for EducationalCum-Residential Campus: A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . Deepak Singh Baghel and Yogesh Bafna Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? . . . . . . . . . . . . . . . . . . . . . . . . . . . Subhanker Paul, Suparna Paul, Maria Fernandino, and Carlos Alberto Dorao

1

15

25

35

45

Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India . . . . . . . . . . . . . . . . . . . . . Niranjan Rao Deevela and Tara C. Kandpal

55

Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tarun Kumar Aseri, Chandan Sharma, and Tara C. Kandpal

71

Optical Modeling of Parabolic Trough Solar Collector . . . . . . . . . . . . Anish Malan and K. Ravi Kumar

81

vii

viii

Contents

Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit . . . . . . . . . . . . . . . . . . . . . . . . . Ahana Sarkar and Ronita Bardhan Development of an Improved Cookstove: An Experimental Study . . . . Himanshu, S. K. Tyagi, and Sanjeev Jain

91 103

Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load . . . . . . . . . . . . . . . . . . . . . . . Jayesh Priolkar and E. S. Sreeraj

111

Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bharathi Raja, R. Sarathi, and Ravikrishnan Vinu

123

Thermo-Hydrodynamic Modeling of Direct Steam Generation in Parabolic Trough Solar Collector . . . . . . . . . . . . . . . . . . . . . . . . . . Ram Kumar Pal and K. Ravi Kumar

131

Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis of Pinewood Over Various Catalysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kavimonica Venkatesan, Parasuraman Selvam, and Ravikrishnan Vinu

141

Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmelash Haftu Amaha, Prabhu Ramachandran, and Shivasubramanian Gopalakrishnan Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector . . . . . . . . . Mousami Prasad Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaynendra Kumar, Anshul Agarwal, and Nitin Singh

149

159

169

Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Yagnamurthy, Dibakar Rakshit, and Sanjeev Jain

181

Experimental Evaluation of Common Rail Direct Injection Compression Ignition Engine with EGR Using Biodiesel . . . . . . . . . . . Suresh D. Mane and Chinna Bathulla

191

Emission Measurement Considerations for Power Industry . . . . . . . . . A. Bekal, S. K. Karthick, Y. Rajeshirke, G. Balasubramaniam, M. Upadhyay, S. Bhandarkar, D. Kuvalekar, and C. Mitra

201

Contents

Impact of Growing Share of Renewable Energy Sources on Locational Marginal Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leena Heistrene, Yash Shukla, Yaman Kalra, Poonam Mishra, and Makarand Lokhande Performance Evaluation of Wind-Solar Hybrid System in Indian Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Shityalkar, Ranjan Dey, Anagha Pathak, Niranjan Kurhe, and Sandesh Jadkar

ix

211

223

Structural, Electrical and Cell Performance Study on Lithium Germanium Phosphate Glass Ceramics-Based Solid-State Li-Electrolyte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anurup Das, Madhumita Goswami, P. Preetham, S. K. Deshpande, Sagar Mitra, and M. Krishnan

231

Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations . . . . . . . . . . . . . . . Kirti Gupta and Saumendra Sarangi

241

Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vishal D. Chaudhari, Govind N. Kulkarni, and C. M. Sewatkar

253

Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . Kamaljyoti Talukdar Performance Study of an Anode Flow Field Design Used in PEMFC Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. A. Yogesha, Prakash C. Ghosh, and Raja Munusamy Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine . . . . . Siddharth Ramachandran, Naveen Kumar, and Mallina Venkata Timmaraju Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. D. Kulkarni and K. S. Wani Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . Anindita Roy, Rajarshi Sen, and Rupesh Shete Saving Electricity, One Consumer at a Time . . . . . . . . . . . . . . . . . . . . K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, and Sumedha Malaviya

265

275

285

295

305 317

x

Contents

Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed . . . . . . . . . . . . . . . . . . . . Saurabh and D. S. Murthy Integrated Thermal Analysis of an All-Electric Vehicle . . . . . . . . . . . . Vinayak Kulkarni and Shankar Krishnan Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR . . . . . B. Anupreethi, Anurag Gupta, Umasankari Kannan, and Akhilanand Pati Tiwari

327 339

349

ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saurabh Khobaragade, Priyanka Bhosale, and Priya Jadhav

359

Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nagesh B. Balam, Tabish Alam, and Akhilesh Gupta

371

Rice Paddy as a Source of Sustainable Energy in India . . . . . . . . . . . . Mohnish Borker and T. V. Suchithra Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pankaj Kumar, Trupti Mishra, and Rangan Banerjee Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying . . . . . . . . . . . . . . . . . . . . . Mallikarjun Pujari, P. G. Tewari, M. B. Gorawar, Ajitkumar P. Madival, Rakesh Tapaskar, V. G. Balikai, and P. P. Revankar Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System . . . . . . . . . . . . . . . . . . . . . . Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, and Atal Bihari Harichandan Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. B. Gorawar, S. V. Desai, V. G. Balikai, and P. P. Revankar Desalination Using Waste Heat Recovery with Active Solar Still . . . . . Sandeep Kumar Singh, S. K. Tyagi, and S. C. Kaushik

383

393

403

417

427 439

Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ammu Susanna Jacob, Rangan Banerjee, and Prakash C. Ghosh

449

Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. R. Dhivyashree, M. B. Gorawar, V. G. Balikai, and P. P. Revankar

463

Contents

xi

Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters . . . . . . . . . . . . . . . . . . . . . Sourish Ganguly, Subhrasish Pal, and Ankur Bhattacharjee

481

A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Sriramalakshmi and Sreedevi V. T.

493

Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gunjan Kumar, Biswajit Thakur, and Sudipta De

505

Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha . . . . . . . . . . . . . . . . . . . . . . . . . . . Sneha Swami and Subodh Wagle

515

Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory . . . . . . Neha Manjul and Mahiraj Singh Rawat

525

Experimental Investigation of Solar Drying Characteristics of Grapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. P. Komble, Govind N. Kulkarni, and C. M. Sewatkar

537

Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling . . . . . . . . . . . . . . . . . Shipra Tiwari and Saumendra Sarangi

547

Performance Assessment and Parametric Study of Multiple Effect Evaporator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pranaynil Saikia, Soundaram Ramanathan, and Dibakar Rakshit

559

An Approach Towards Sustainable Energy Education in India . . . . . . Pankaj Kalita, Rabindra Kangsha Banik, Samar Das, and Dudul Das Simulation-Based Economic Optimization of Nuclear Renewable Hybrid Energy Systems with Reliability Constraints . . . . . . . . . . . . . . Saikrishna Nadella, Anil Antony, and N. K. Maheshwari Exergy Analysis and Cost Optimization of Solar Flat Pate Collector for a Two-Stage Absorption Refrigeration System with Water-Lithium Bromide as a Working Pair . . . . . . . . . . . . . . . . . Abhishek Verma, S. K. Tyagi, and S. C. Kaushik Characterizing the Helical Vortex Frequency of HAWT . . . . . . . . . . . Ojing Siram and Niranjan Sahoo Design and Development of Concentrated Solar Cooker with Parabolic Dish Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . Susant Kumar Sahu, Natarajan Sendhil Kumar, and K. Arjun Singh

575

587

599 611

621

xii

Contents

Thermal and Electrical Performance Assessment of Elongated Compound Parabolic Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . Chandan, Sumon Dey, V. Suresh, M. Iqbal, K. S. Reddy, and Bala Pesala Thermodynamic Analysis of a 500 MWe Coal-Fired Supercritical Thermal Power Plant Integrated with Molten Carbonate Fuel Cell (MCFC) at Flue Gas Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshini More, A. Pruthvi Deep, and Sujit Karmakar

633

645

Three-Dimensional Investigation on Energy Separation in a Ranque–Hilsch Vortex Tube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nilotpala Bej, Pooja Chaubdar, Anish Pandey, and K. P. Sinhamahapatra

655

Bamboo Plant Intellect Deeds Optimization Algorithm for Solving Optimal Reactive Power Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanagasabai Lenin

665

Actuator Fault Detection and Isolation for PEM Fuel Cell Systems Using Unknown Input Observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vikash Sinha and Sharifuddin Mondal

673

Analysis of Heating and Cooling Energy Demand of School Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tshewang Lhendup, Samten Lhendup, and Hideaki Ohgaki

685

Thermodynamic Performance Analysis of Adsorption Cooling and Resorption Heating System Using Ammoniated Halide Salts . . . . . Rakesh Sharma, K. Sarath Babu, and E. Anil Kumar

695

Correlating Partial Shading and Operating Conditions to the Performance of PV Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Gairola, M. K. Sharma, and J. Bhattacharya

707

Engineering of O2 Electrodes by Surface Modification for Corrosion Resistance in Zinc–Air Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imran Karajagi, K. Ramya, Prakash C. Ghosh, A. Sarkar, and N. Rajalakshmi Energy Farming—A Green Solution for Indian Cement Industry . . . . Kapil Kukreja, Manoj Kumar Soni, B. N. Mohapatra, and Ashutosh Saxena

717

725

Energetic and Exergetic Performance Comparison of a Hybrid Solar Kalina Cycle at Solar and Solar Storage Mode of Operations . . . . . . . P. Bhuyan, P. Borah, and T. K. Gogoi

735

Assessment of Different Multiclass SVM Strategies for Fault Classification in a PV System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Kumar Mandal and Paresh G. Kale

747

Contents

Performance Analysis of Double Glass Water Based Photovoltaic/ Thermal System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ajay Sharma, S. Vaishak, and Purnanand V. Bhale Modeling Polarization Losses in HTPEM Fuel Cells . . . . . . . . . . . . . . Vamsi Ambala, Anusree Unnikrishnan, N. Rajalakshmi, and Vinod M. Janardhanan

xiii

757 767

Effect of Diesel Injection Timings on the Nature of Cyclic Combustion Variations in a RCCI Engine . . . . . . . . . . . . . . . . . . . . . . Ajay Singh, Rakesh Kumar Maurya, and Mohit Raj Saxena

775

Investigating the Impact of Energy Use on Carbon Emissions: Evidence from a Non-parametric Panel Data Approach . . . . . . . . . . . Barsha Nibedita and Mohd Irfan

785

Studies on the Use of Thorium in PWR . . . . . . . . . . . . . . . . . . . . . . . . Devesh Raj and Umasankari Kannan Coaxial Thermal Probe for High-Frequency Periodic Response in an IC Engine Test Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anil Kumar Rout, Santosh Kumar Hotta, Niranjan Sahoo, Pankaj Kalita, and Vinayak Kulkarni

795

805

Effect of Injection Pressure on the Performance Characteristics of Double Cylinder Four-Stroke CI Engine Using Neem Bio-diesel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sushant S. Satputaley, Iheteshamhusain Jafri, Gauravkumar Bangare, and Rahul P. Kavishwar

815

Experimental Study of a Helical Coil Receiver Using Fresnel Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumit Sharma and Sandip K. Saha

825

Substrate-Assisted Electrosynthesis of Patterned Lamellar Type Indium Selenide (InSe) Layer for Photovoltaic Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. B. Bhalerao, S. B. Jambure, R. N. Bulakhe, S. S. Kahandal, S. D. Jagtap, A. G. Banpurkar, A. W. M. H. Ansari, Insik In, and C. D. Lokhande

837

Optimization of Injector Location on the Cylinder Head in a Direct Injection Spark Ignition Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Srinibas Tripathy, Sridhar Sahoo, and Dhananjay Kumar Srivastava

847

Automated Cleaning of PV Panels Using the Comparative Algorithm and Arduino . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huzefa Lightwala, Dipesh Kumar, and Nidhi Mehta

857

xiv

Contents

Performance and Degradation Analysis of High-Efficiency SPV Modules Under Composite Climatic Condition . . . . . . . . . . . . . . . . . . Shubham Sanyal, Arpan Tewary, Rakesh Kumar, Birinchi Bora, Supriya Rai, Manander Bangar, and Sanjay Kumar Energy Literacy of University Graduate Students: A Multidimensional Assessment in Terms of Content Knowledge, Attitude and Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . Divya Chandrasenan, Jaison Mammen, and Vaisakh Yesodharan Waste-to-Energy: Issues, Challenges, and Opportunities for RDF Utilization in Indian Cement Industry . . . . . . . . . . . . . . . . . . . . . . . . . Prateek Sharma, Pratik N. Sheth, and B. N. Mohapatra Predict the Effect of Combustion Parameter on Performance and Combustion Characteristics of Small Single Cylinder Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. K. Dond and N. P. Gulhane Experimental Investigation of a Biogas-Fueled Diesel Engine at Different Biogas Flow Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naseem Khayum, S. Anbarasu, and S. Murugan Characteristics of an Indigenously Developed 1 KW Vanadium Redox Flow Battery Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sreenivas Jayanti, Ravendra Gundlapalli, Raghuram Chetty, C. R. Jeevandoss, Kothandaraman Ramanujam, D. S. Monder, Raghunathan Rengaswamy, P. V. Suresh, K. S. Swarup, U. V. Varadaraju, Vasu Gollangi, and L. Satpathy

869

879

891

901

913

923

Dynamic Demand Response Through Decentralized Intelligent Control of Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. T. Arvind and Anoop R. Kulkarni

931

Transient Analysis of Pressurizer Steam Bleed Valves Stuck Open for 700 MWe PHWRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deepraj Paul, S. Pahari, S. Hajela, and M. Singhal

945

Transient Analysis of Net Load Rejection for 700 MWe IPHWRs . . . . S. Phani Krishna, S. Pahari, S. Hajela, and M. Singhal

953

A Comparative Experimental Investigation of Improved Biomass Cookstoves for Higher Efficiency with Lower Emissions . . . . . . . . . . . Sandip Bhatta, Dhananjay Pratap, Nikhil Gakkhar, and J. P. S. Rajput

961

Assessment of Floating Solar Photovoltaic (FSPV) Potential in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashish Kumar, Ishan Purohit, and Tara C. Kandpal

973

Contents

xv

Effect of Non-Revenue Water Reduction in the Life Cycle of Water–Energy Nexus: A Case Study in India . . . . . . . . . . . . . . . . . Rajhans Negi, Vipin Singh, and Munish K. Chandel

983

Policy Intervention for Promoting Effective Adaptation of Rooftop Solar PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sabreen Ahmed, C. Vijayakumar, and Arjun D. Shetty

991

Improved Dispatchability of Solar Photovoltaic System with Battery Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1003 Sheikh Suhail Mohammad and S. J. Iqbal Numerical Investigation of the Performance of Pump as Turbine with Back Cavity Filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015 Rahul Gaji, Ashish Doshi, and Mukund Bade Mining Representative Load Profiles in Commercial Buildings . . . . . . 1025 Kakuli Mishra, Srinka Basu, and Ujjwal Maulik A Simplified Non-iterative Method for Extraction of Parameters of Photovoltaic Cell/Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 Kumar Gaurav, Neha Kumari, S. K. Samdarshi, and A. S. Bhattacharyya Design, Analysis and Hardware Implementation of Modified Bipolar Solid-State Marx Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049 Neelam S. Pinjari and S. Bindu Viability Study of Stand-Alone Hybrid Energy Systems for Telecom Base Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 M. Siva Subrahmanyam and E. Anil Kumar Effect of Temperature and Salt Concentration on the Properties of Electrolyte for Sodium-Ion Batteries . . . . . . . . . . . . . . . . . . . . . . . . 1071 Bharath Ravikumar, Surbhi Kumari, Mahesh Mynam, and Beena Rai Carbon Deposition on the Anode of a Solid Oxide Fuel Cell Fueled by Syngas—A Thermodynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . 1083 N. Rakesh and S. Dasappa Numerical Study on CO2 Injection in Indian Geothermal Reservoirs Using COMSOL Multiphysics 5.2a . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Nandlal Gupta and Manvendra Vashistha Modification in the Rotor of Savonius Turbine to Reduce Reverse Force on the Returning Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1103 J. Ramarajan and S. Jayavel Design and Fabrication of Grating-Based Filters for Micro-thermophotovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1113 M. V. N. Surendra Gupta, E. Ameen, Ananthanarayanan Veeraragavan, and Bala Pesala

xvi

Contents

A Systematic Investigation on Evaporation, Condensation and Production of Sustainable Water from Novel-Designed Tubular Solar Still . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Mihir Lad, Nikunj Usadadia, Sagar Paneliya, Sakshum Khanna, Vishwakumar Bhavsar, Indrajit Mukhopadhyay, Devang Joshi, and Abhijit Ray Novel Design of PV Integrated Solar Still for Cogeneration of Power and Sustainable Water Using PVT Technology . . . . . . . . . . . . . . . . . . 1131 Nikunj Usadadia, Mihir Lad, Sagar Paneliya, Sakshum Khanna, Abhijit Ray, and Indrajit Mukhopadhyay Cellulose Nanocrystals Incorporated Proton Exchange Membranes for Fuel Cell Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145 Saleheen Bano, Asif Ali, Sauraj, and Yuvraj Singh Negi Study of the Effect of Biomass-Derived N-Self Doped Porous Carbon in Microbial Fuel Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1155 Saswati Sarmah, Minakshi Gohain, and Dhanapati Deka Analysis of Nature-Inspired Spirals for Design of Solar Tree . . . . . . . 1165 Sumon Dey, Madan Kumar Lakshmanan, and Bala Pesala Effective Use of Existing Efficient Variable Frequency Drives (VFD) Technology for HVAC Systems—Consultative Research Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175 Rahul Raju Dusa, Atulkumar Auti, and Vijay Mohan Rachabhattuni Thermodynamic Analysis of a Combined Power and Cooling System Integrated with CO2 Capture Unit of a 500 MWe SupC Coal-Fired Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1185 Rajesh Kumar, Goutam Khankari, and Sujit Karmakar DFT Studies on Electronic and Optical Properties of Inorganic CsPbI3 Perovskite Absorber for Solar Cell Application . . . . . . . . . . . . 1199 Abhijeet Kale, Rajneesh Chaurasiya, and Ambesh Dixit Biowaste Derived Highly Porous Carbon for Energy Storage . . . . . . . 1207 Dinesh J. Ahirrao, Shreerang D. Datar, and Neetu Jha Bio-Ethanol Production from Carbohydrate-Rich Microalgal Biomass: Scenedesmus Obliquus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215 Maskura Hasin, Minakshi Gohain, and Dhanapati Deka Safety Analysis of Loss of NPP Off-Site Power with Failure of Reactor SCRAM (ATWS) for VVER-1000 . . . . . . . . . . . . . . . . . . . 1225 Manish Mehta, Sanuj Chaudhary, Anirban Biswangri, P. Krishna Kumar, Y. K. Pandey, and Gautam Biswas

Contents

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P-type Crystalline Silicon Surface Passivation Using Silicon Oxynitride/SiN Stack for PERC Solar Cell Application . . . . . . . . . . . . 1237 Irfan M. Khorakiwala, Vikas Nandal, Pradeep Nair, and Aldrin Antony Pressure Propagation and Flow Restart in the Subsea Pipeline Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 Lomesh Tikariha and Lalit Kumar Electrodeposition of Cu2O: Determination of Limiting Potential Towards Solar Water Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1255 Iqra Reyaz Hamdani and Ashok N. Bhaskarwar Design and Development of an Economical and Reliable Solar-Powered Trash Compactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1265 Ridhi Lakhotia, Abu Fazal, Ajay Yadav, and Ankur Bhattacharjee Performance and Emission Characteristics of CI Engine Fueled with Plastic Oil Blended with Jatropha Methyl Ester and Diesel . . . . . 1275 S. Babu, K. Kavin, and S. Niju Performance Analysis of Hybrid Photovoltaic Array Configurations Under Randomly Distributed Shading Patterns . . . . . . . . . . . . . . . . . . 1287 Vandana Jha Flow Improvement Aspect with Stagger Angle Variation of the Subsequent Rotor in Contra-rotating Axial Flow Turbine . . . . . 1297 Rayapati Subbarao Performance Assessment of Pelton Turbine with Traditional and Novel Hooped Runner by Experimental Investigation . . . . . . . . . . 1309 Vimal K. Patel, Hemal N. Lakdawala, Sureel Dohare, and Gaurang Chaudhary Evaluation of LVRT Control Strategies for Offshore Wind Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1319 M. M. Kabsha and Zakir Rather An Experimental and CFD Analysis on Heat Transfer and Fluid Flow Characteristics of a Tube Equipped with X-Shaped Tape Insert in a U-Shaped Heat Exchanger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1331 Sagar Paneliya, Sakshum Khanna, Jeet Mehta, Vishal Kathiriya, Umang Patel, Parth Prajapati, and Indrajit Mukhopdhyay Single-Particle Analysis of Thermally Thick Wood Particles in O2, N2, CO2 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1349 Shruti Vikram and Sandeep Kumar An Analysis for Management of End-of-Life Solar PV in India . . . . . . 1361 Snehalata Pankadan, Swapnil Nikam, and Naqui Anwer

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Contents

Localized Energy Self-sufficiency (Energy Swaraj) for Energy Sustainability and Mitigating Climate Change . . . . . . . . . . . . . . . . . . . 1373 Chetan Singh Solanki, Sayli Shiradkar, Rohit Sharma, Jayendran Venkateswaran, Nikita Lihinar, Harshad Supal, and Swati Kalwar Pseudocapacitive Energy Storage in Copper Oxide and Hydroxide Nanostructures Casted Over Nickel-Foam . . . . . . . . . . . . . . . . . . . . . . 1383 Priyanka Marathey, Sakshum Khanna, Roma Patel, Indrajit Mukhopadhyay, and Abhijit Ray Validation of Computer Code Based on Nodal Integral Method Against KAPS-2 Phase-B Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1393 Manish Raj, Sherly Ray, A. S. Pradhan, and Suneet Singh Bipolar DC Micro-Grid Based Wind Energy Systems . . . . . . . . . . . . . 1403 Dodda Satish Reddy, Suman Kumar, Bonala Anil Kumar, and Sandepudi Srinivasa Rao Processing Thermogravimetric Analysis Data for Pyrolysis Kinetic Study of Microalgae Biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415 Pravin G. Suryawanshi and Vaibhav V. Goud Photovoltaic Thermal Collectors with Phase Change Material for Southeast of England . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425 Preeti Singh, Rajvikram Madurai Elavarasan, Nallapaneni Manoj Kumar, Sourav Khanna, Victor Becerra, Sanjeev Newar, Vashi Sharma, Jovana Radulovic, Rinat Khusainov, and David Hutchinson Modeling and Simulation of Hollow Fiber Biocatalyst Membrane Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1431 Nooram Anjum, Mohammad Danish, and Sarah Anjum Efficient Alkaline Peroxide Pretreatment of Sterculia foetida Fruit Shells for Production of Reducing Sugar: Effect of Process Parameters on Lignin Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1441 S. Sardar, A. Das, S. Saha, and C. Mondal Performance Enhancement of Savonius Hydrokinetic Turbine with a Unique Vane Shape: An Experimental Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1453 Vimal K. Patel, Kushal Shah, and Vikram Rathod Techno-economic Analysis for Production of Biodiesel and Green Diesel from Microalgal Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1465 Swarnalatha Mailaram, Nitesh Dobhal, and Sunil K. Maity Numerical Investigation on the Effect of EGR in a Premixed Natural Gas SI Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1477 Sridhar Sahoo, Srinibas Tripathy, and Dhananjay Kumar Srivastava

Contents

xix

Transitions in the Indian Electricity Sector: Impacts of High Renewable Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1489 Aishwarya V. Iyer and Rangan Banerjee Comparison of Physics Characteristics of Pressurized Water Reactor Type Advanced Light Water Reactors . . . . . . . . . . . . . . . . . . . . . . . . . 1501 L. Thilagam and D. K. Mohapatra Development of a Python Module “SARRA” for Refuelling Analysis of MSR Using DRAGON Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1513 A. K. Srivastava, Anurag Gupta, and Umasankari Kannan The Effect of Concentration Ratio and Number of P-N Thermocouples on Photovoltaic-Thermoelectric Hybrid Power Generation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1521 Abhishek Tiwari and Shruti Aggarwal Evaluation of Annual Electrical Energy Through Semitransparent (Glass to Glass) and Opaque Photovoltaic Module in Clear Sky Condition at Composite Climate: A Comparative Study . . . . . . . . . . . 1533 Rohit Tripathi, Nitin K. Gupta, Deepak Sharma, G. N. Tiwari, and T. S. Bhatti Current Practices and Emerging Trends in Safety Analysis of NPPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1543 K. Obaidurrahman Electrochemical Reduction of CO2 on Ionic Liquid Stabilized Reverse Pulse Electrodeposited Copper Oxides . . . . . . . . . . . . . . . . . . 1551 Nusrat Rashid and Pravin P. Ingole Performance of Flux Mapping System During Spatial Xenon Induced Oscillations in PHWRs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1559 Abhishek Chakraborty, M. P. S. Fernando, and A. S. Pradhan Forecasting of Electricity Demand and Renewable Energy Generation for Grid Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1571 Joel Titus, Urvi Shah, T. Siva Rama Sarma, Bhushan Jagyasi, Pallavi Gawade, Mamta Bhagwat, and Arnab De Platooning of Flat Solar-Panel-Mounted Mini Bus Model—A Numerical Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1583 Mohammad Rafiq B. Agrewale and R. S. Maurya Co-sensitization of Perovskite Solar Cells by Organometallic Compounds: Mechanism and Photovoltaic Characterization . . . . . . . . 1595 Nisha Balachandran, Temina Mary Robert, Dona Mathew, and Jobin Cyriac

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Contents

Nuclear Power Plants and Human Resources Development in South Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1603 Firoz Alam, Rashid Sarkar, Akshoy Ranjan Paul, and Abdulaziz Aldiab Highly Stable Pt/CVD-Graphene-Coated Superstrate Cu2O Photocathode for Water Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1615 Chandan Das, Akhilender Jeet Singh, and K. R. Balasubramaniam Thermodynamic Studies on Steel Slag Waste Heat Utilization for Generation of Synthesis Gas Using Coke Oven Gas (COG) as Feedstock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1623 M. Srinivasarao, Nilu Kumar, and A. Syamsundar Reactivity-Initiated Transients for 700 MWe PHWR . . . . . . . . . . . . . . 1633 Suresh Kandpal, M. P. S. Fernando, and A. S. Pradhan Multi-field Solar Thermal Power Plant with Linear Fresnel Reflector and Solar Power Tower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1645 Shridhar Karandikar, Irfan Shaikh, Anish Modi, Shireesh B. Kedare, and Balwant Bhasme Experimental Investigation Using Enriched Biogas in S-I Engine for Stable Rural Electrification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1657 Antony P. Pallan, Deepak Mathew, and Yohans Varghese Solar Autoclave for Rural Hospitals Using Aerogel as Transparent Insulation Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1667 Manoj Kumar Yadav, Anish Modi, and Shireesh B. Kedare Numerical Investigation on the Influence of Reactant Gas Concentration on the Performance of a PEM Fuel Cell . . . . . . . . . . . . 1679 Brajesh Kumar Kanchan, Pitambar R. Randive, and Sukumar Pati Energy Efficiency Analysis of a Building Envelope . . . . . . . . . . . . . . . 1691 M. Y. Khan, A. Baqi, and A. Talib

About the Editors

Manaswita Bose is an Associate Professor in the Department of Energy Science and Engineering at the Indian Institute of Technology Bombay, India, and has previously worked in Monash University (Australia), Reliance Industries (India), and Zeus Numerix Pvt Ltd (India). She has done her M.Sc. and Ph.D. from the Indian Institute of Science, Bangalore. Her research interests include the study of the flow of granular materials, solar thermal storage, and coal gasification. She has co-authored 23 research papers in reputed journals, proceedings, and published 1 book chapter and 25 papers in international conferences, having also filed 3 patents. Anish Modi is an Assistant Professor in the Department of Energy Science and Engineering at the Indian Institute of Technology Bombay, India and has previously worked at the Technical University of Denmark (DTU) and Alstom Projects India Limited. He pursued his M.Sc. from the Royal Institute of Technology (KTH), Sweden and Polytechnic University of Catalonia (UPC), Spain and his Ph.D. from DTU, Denmark. His research interests include thermal energy systems, solar thermal energy, concentrating solar power, polygeneration, and energy sustainability, and he has published 19 papers in reputed journals and international conferences.

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Determination of Steam Energy Factor for Wort Kettle as a Tool for Optimization of the Steam Energy Shripad Kulkarni and Alex Bernard

Nomenclature SSC WK DP V.L. H.R.L. C.L. F.L. B.E. HL ML S.E.

Specific steam consumption Wort kettle Degree Plato Vapor loss Heat recovery loss Condensate loss Flash loss Losses in brew energy Hectoliter Machine learning Losses through steam pressure variation

1 Introduction Stringent energy conservation laws have been driving new ways and methods to reduce energy demand either by developing energy efficient systems or by reducing the total energy consumption directly. A significant portion of the world’s energy requirement occurs in the form of steam. Steam is used in almost every industry ranging from manufacturing to process industries at different capacities and conditions. The steam requirement at different sectors may vary with regards to application S. Kulkarni · A. Bernard (B) Department of Research and Development, Forbes Marshall Pvt. Ltd, Pune, Maharashtra 411034, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_1

1

2

S. Kulkarni and A. Bernard

and capacity. Thus, steam distribution systems lack a generic structure which would have helped in optimizing steam networks. Process industries such as Food and Beverages (F&B) generally use substantial amounts of steam for their varied applications and often incorporate complex steam systems to meet their requirements which are both dynamic and highly critical. This makes the estimation of actual steam requirement a very arduous process [1]. The brewing industry is one of the fastest growing and also one of the most energy intensive sectors in the world. A typical brewing plant in India consumes more than 200 tons of steam each operational day. The Wort kettle within a Brewhouse consumes about 30% of the overall steam production in the plant. Thus, the steam saving potential for a Brewhouse can be quite substantial through improved evaluation methods for steam distribution networks. Energy and exergy analysis of the brewing industries has helped many energy auditors in understanding the various inefficiencies within the steam distribution system itself [2]. EINSTEIN is a thermal auditing tool that has been used to some extent in breweries [3]. The most common metric used in the present scenario for evaluating the steam distribution system is the SSC or the specific steam consumption. It is the energy consumption analysis with regards to the production data. It has so far been the basis of benchmarking in the brewing industry [4]. But it does not indicate the efficiency of the steam distribution network itself. This further inspired many other steam enthusiasts to come up with different strategies to evaluate steam consumption based on critical parameters such as enthalpy, fuel, work load [5]. Another method to evaluate steam consumption was to do a complete process modeling of the brewing facility. This allows to have a holistic view of the energy balance of the Brewhouse which would further help in analyzing the energy load of the facility as a whole [6]. Though all these methods brief about the actual steam requirement, the lack of coherence with the realistic scenario deduces to a lower practicality when it comes to saving steam. It becomes hard to trace the losses when systems exhibit high degree of complexity and flexibility. Not to forget the seasonal variation that occurs over time. This paper assesses steam energy requirement for a Wort kettle which forms the soul of the Brewhouse. The steam energy factor (SEF) is a proven metric for evaluation of steam consumption for various F&B industries and steam-intensive equipment. The following paper is an approach to determine the steam energy factor using a model based on losses and critical parameters assisted by machine learning. The list of all the critical parameters that were used to derive the SEF have been listed in Table 1.

2 Steam Consumption Steam is primarily used for two processes in the Brewhouse: i. Preheating

Determination of Steam Energy Factor for Wort Kettle …

3

Table 1 Process parameters Inlet wort volume

HL

Heat load of the water evaporated

KCal

Outlet wort volume

HL

Radiation Loss

KCal

Sweet wort percentage

%

Radiation loss of evaporated water KCal

Sparge volume percentage

%

Latent heat of steam at stipulated pressure

KCal/Kg

Amount of water evaporated

Kg

Total load

KCal

Pressure range

BarG

Steam consumption

Kg/Batch

Wort Temperature after mash filter

°C

Actual steam consumption (Flow meter)

Kg/Batch

Wort Temperature after whirlpool °C PHE

SSC (Steam required per Kg of product)

Kg/Kg

Heat recovery pump raises temp to

°C

Latent heat of Vaporization at pressure

KCal/Kg

Boiling Temperature

°C

Saturation Temperature at pressure

°C

Boiling time

mins

Energy in

KCal

Degree Plato inlet of WK

°P

Energy out

KCal

Degree Plato outlet of WK/

°P

Lasses

KCal

Difference in inlet DP and outlet DP

°P

Total Condensate Loss

KCal

Overall heat transfer coefficient

W/m2 K

Flash loss

KCal

Ambient temperature

°C

Condensate loss

KCal

Insulation efficiency

Unit

HRS gain

KCal

Area of Wort kettle

m2

Loss when HRS is off

KCal

Specific gravity of wort at inlet flow meter

Kg/m3

Evaporation loss

KCal

Specific gravity of wort at outlet flow meter

Kg/m3

Brew energy

KCal

Contraction factor

Unit

UAL

KCal

Specific heat of wort

KCal/KgK

Indirect efficiency

Unit

Heat load

KCal

Direct efficiency

Unit

ii. Evaporation The amount of water evaporated and the preheating time is purely an aspect of the recipe and will vary from one industry to another. Even though steam requirement could be fairly estimated only by monitoring these two parameters, it was found that there were various other factors which interact with the system that also affect the overall steam consumption. Hence, it became evident to track all the factors to analyze the impact each of these factors have on steam consumption. There are many methods that have been proposed in the past to estimate steam consumption such as enthalpy-based

4

S. Kulkarni and A. Bernard

steam consumption [4] and equivalent steam requirement through fuel consumption [1]. Both the approaches though very commendable, lack in capturing the steam requirement inherited by the dynamic nature of process itself. The process of increasing the Degree Plato of the cold wort entering the Wort kettle to the required set point as per recipe is done by evaporating the moisture content within the product. There are multiple factors that affect this process. The list of all the factors taken into account for developing steam energy factors have been listed below.

3 Process Flow Figure 1 represents the schematic of the flow of sweet wort after the mash filter and before the whirlpool cooler for different batch sizes. The inlet wort, before entering the Wort kettle, is preheated using a heat recovery system. The estimation for maximum possible efficiency has been calculated. The value of Degree Plato (°P) is set according to the recipe. All the accountable losses have been calculated while the other are taken as unavailable energy losses (UAL). Most of the parameters required for determining the steam energy factor are already monitored by the brewer. And likewise, the brewer also has control over all of these parameters. SEF helps the brewer understand the interaction of all these parameters with the equipment and the process variation and thus giving him a better insight regarding the system performance and the impact each of these parameters have on the overall steam consumption.

Fig. 1 Schematic of Wort kettle section

Determination of Steam Energy Factor for Wort Kettle …

5

4 Development of SEF The methodology used for the determination of steam energy factor is a crucial step in its development and its usefulness. It shall not only brief the brewer about his excess steam consumption but also help him negate the various losses that are identified using SEF for the process. The development of this metric was addressed by two methods out of which one was used for a very renowned brewing industry in Asia.

4.1 Method 1 In the first method, the steam energy factor is calculated directly by dividing the actual steam consumption with the theoretical steam consumption. The theoretical steam consumption is estimated by developing thermodynamic relations of the process variables with the steam load demand. In simple terms, this model theoretically evaluates the steam demand for the process and then compares it with the actual steam consumed for the same process measured through steam flow meters. SEF(model 1) =

Actual steam consumption Theoretical steam consumption

(1)

4.2 Method 2 Method 2, though it might be familiar to method 1, it is a fairly different approach taken to determine the steam energy factor. The steam energy factor in this case accounts for all the losses that occur during a process. In this approach, all the process parameters are monitored continuously for a certain period. This is followed by identifying the parameters which have a significant impact on steam consumption. This further helps in estimating all the losses that occur due to process variation. And all of this is captured by SEF. SEF(model 2) =

1 100 − Losses(%)

(2)

The losses that were included for estimating the SEF for Wort kettle have been listed in Fig. 2. The following relations were used to determine the losses: V.L. H.R.L. C.L. F.L.

f (E.R., Sweet wort %, sparge volume %, inlet DP) f (HRS temperature, scaling, choking) f (heat carried out by the hot condensate) f (Condensate flashing into vapors)

6

S. Kulkarni and A. Bernard

Fig. 2 Energy distribution in Wort kettle

B.E. S.E.

f (Inlet temperature of cold wort) f (Steam pressure variation)

This model, though not as effective as the former one in terms of the accuracy in estimating the losses, still manages to brief all the possible reductions that could be incorporated to reduce the overall steam consumption by identifying them. Machine learning algorithms were used for determining the specific gravity of wort at inlet and outlet. Twenty batches were used for training the model.

5 Optimization with SEF The ideal value of SEF is 1, and hence, the best value of SEF is when there is minimal loss. Most of the losses occurred because of the variations in the actual process. Initial study depicted the variations in consecutive batches during the same shift. This was reflected by a rise in the overall steam consumption. Figure 2 is an empirical energy distribution diagram flow a Wort kettle. There are multiple factors that give rise to a high evaporation ratio. This could be a result of a low mashing efficiency, a higher percentage of Sparge volume, the quality of malt and other ingredients, etc. The heat recovery system (HRS) also plays an important role in the reduction of steam consumption. The steam pressure, trap leakages, ambient conditions, poor insulation of steam lines are some of the other factors which are categorized under site conditions also affect the overall steam consumption.

Determination of Steam Energy Factor for Wort Kettle …

7

6 Results and Discussion The initial study was done to validate the difference between specific steam consumption and steam energy factor. Specific steam consumption or SSC is total steam consumed per unit quantity of product. SSC is a true reflection of the overall steam consumption, but it fails to capture the variations occurring in the process which account for the marginal excess steam consumption (Fig. 3). The readings were taken for 400 HL batches. For batch 5, the SSC is not a true indication of the excess steam consumption. It is indicated by a low SSC value of 18.45 against a high SEF value of 1.821. This helps the brewer identify the quality of batch 5 as it accounts for a higher steam energy loss. The following are the results obtained through SEF for an international beer production organization.

6.1 Before The data recorded for the brewing process before the implementation of the SEF indicated a high steam consumption when compared to the actual process demand. Wort kettle running at sweet wort composition of less than 34% having an SEF of 1.76 accounted for 27% evaporation loss, 8.2% condensate loss, 2.4% flash loss, 1.3% heat recovery loss and 6.4% miscellaneous loss (Figs. 4, 5, 6, 7, 8, 9 and 10).

6.2 After Through SEF, an efficient control strategy was applied. The data was collected again after 7 months, and the results are as follows in Figs. 11, 12, 13, 14, 15, 16 and 17. With the reduction in all these factors, the SEF was brought down. It was seen that steam consumption per batch reduced by more than 600 kg. For the brewing

Fig. 3 SEF versus SSC

8 Fig. 4 Initial SEF

Fig. 5 Evaporation ratio

Fig. 6 Inlet degree plato

S. Kulkarni and A. Bernard

Determination of Steam Energy Factor for Wort Kettle … Fig. 7 Sweet wort percentage

Fig. 8 Sparge volume percentage

Fig. 9 Temperature of heat recovery system

9

10 Fig. 10 Overall steam consumption

Fig. 11 Final SEF

Fig. 12 Final evaporation ratio

S. Kulkarni and A. Bernard

Determination of Steam Energy Factor for Wort Kettle … Fig. 13 Inlet degree Plato

Fig. 14 Sweet wort percentage

Fig. 15 Sparge volume percentage

11

12

S. Kulkarni and A. Bernard

Fig. 16 Temperature of heat recovery system

Fig. 17 Overall steam consumption per batch

industry that produces as much as 8 batches per day, the total savings would come to nearly 1.65 megatons per year. It is still possible to raise the HRS temperature to more than 90 °C. As much as 500 kg per batch of steam consumption could be further reduced through an optimized control strategy.

7 Conclusion An optimized tool for steam evaluation helps in understanding the various losses that occur within a process. Identification of these losses helps in determining the right control strategy for reduced steam consumption. SEF as a metric identifies these losses and also helps in benchmarking the best possible performance of the steam

Determination of Steam Energy Factor for Wort Kettle …

13

equipment. The steam savings generated through SEF for one brewing plant is close to 5TPD. There are about 55 brewing plants in India alone. The potential for overall energy savings is enormous. SEF as an improved methodology for evaluating steam consumption can be extended for other steam intensive equipments and processes. Our country relies on steam as a major source of mobilized heat energy. Tools for evaluating the steam distribution systems will bridge the gap between the theoretical steam demand and the actual steam consumed and they get better with more parameters and improved models.

References 1. Zhu, X.X.: Energy and Process Optimization for the Process Industries. Wiley, Hoboken, NJ (2014) 2. Fadare, D.A., Nkpubre, D.O., Oni, A.O., Falana, A., Waheed, M.A., Bamiro, O.A.: Energy and exergy analyses of malt drink production in Nigeria. Energy 35, 5336–5346 (2010). https://doi. org/10.1016/j.energy.2010.07.026 3. Schweiger, H. et al.: Guide for EINSTEIN Thermal Energy Audits, Barcelona, Spain (2012) 4. Energy Saver Tool, Campden BRI, Surrey, UK. Available from http://www.campdenbri.co.uk/ services/brewing-energy-saver.php 5. Fuller, D.A.: Alternative scale measures and the behavior of average costs in steam electric generation. Energy Econ. 13(1), 61–68 (1991). https://doi.org/10.1016/0140-9883(91)90057-7 6. Muster-Slawitscha, B., Hubmanna, M., Murkovic, M., Brunner, C.: Process modelling and technology evaluation in brewing (2014). https://doi.org/10.1016/j.cep.2014.03.010

CMG-Based Simulation Study of Water Flooding of Petroleum Reservoir Pratiksha D. Khurpade , Somnath Nandi , Pradeep B. Jadhav , and Lalit K. Kshirsagar

List of Symbols K z S Rs Bo μ MSTB P

Permeability (mD) Grid thickness (ft.) Phase saturation [Dimensionless (–)] Solution gas oil ratio (ft3 /bbl.) The formation factor (bbl./ft3 ) Viscosity (centipoise) Thousand stock tank barrels (MSTB) Pressure (psi)

Subscripts w o g c r x, y, z

Water Oil Gas Capillary Relative Directions

P. D. Khurpade · P. B. Jadhav · L. K. Kshirsagar Department of Petroleum and Petrochemical Engineering, Maharashtra Institute of Technology, Paud Road, Pune 411038, India e-mail: [email protected] S. Nandi (B) Department of Technology, Savitribai Phule Pune University, Ganeshkhind, Pune 411007, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_2

15

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P. D. Khurpade et al.

1 Introduction The increasing global demand for oil and gas and falling prices of crude oil, it is necessary to increase the productivity of hydrocarbons as efficiently and economically as possible. As most of the existing oil fields are approaching a mature stage, in turn, needs the secondary recovery methods to increase production from these fields [1]. Waterflooding is the oldest and widely used secondary recovery method in these reservoirs because of easy water availability, ease of injection and it is less expensive. Basically, waterflooding operation involves the injection of water into the reservoir to displace the oil through porous medium [2]. Successful waterflooding applications recover more oil than the primary recovery stage. However, percentage of water cut from many of the petroleum reservoirs become significantly high, and hence sometimes the process may become uneconomical. Common reasons which include channeling of water which by-passed most of the oil, low permeability which limits the injection rate of water into the reservoir and higher cost of infrastructure for deeper or offshore located reservoirs [3]. Various methods to improve the performance of waterflooding operation to get the maximum productivity of oil and minimum water production are reported in the literature. One of the methods for extenuating this problem is by employing a different combination of horizontal and/or vertical wells in waterflooding projects [4]. The concept of horizontal injector/producer well for waterflooding project was first introduced by Taber and Seright 1992 in East Texas [5]. They have reported the potential benefits of combination of horizontal injection and production wells for waterflooding. Their results showed that horizontal wells for injection and production in opposite direction have higher injectivity and sweep efficiency as compared to five-spot conventional patterns [5]. The main advantage of horizontal well is the larger reservoir contact area of a well than a vertical well which depends on the well length. Hence the productivity of horizontal well increases with an increase in well length by enhancing the reservoir contact area. However, the major drawback of horizontal wells is the well cost which is 1.4–3 times more than that of a vertical well. Published literature of horizontal well showed that multi-well rather than single well is preferred for economic benefits [6]. Broman et al. 1990 have shown that reduction in the cost of drilling and completion of 16 horizontal wells in Purdhoe Bay, Alaska in the initial phase and then remained constant over the last two years [7]. Water coning and gas coning are severe problems in petroleum reservoir as it affects oil productivity. The main reason for coning is pressure drawdown near the wellbore. The coning problems can be reduced by minimizing pressure drawdown which is achieved by drilling long horizontal well. It also enhances the production rates due to increases in contact area [6]. Popa et al. 1998 have proposed that the horizontal injector and producer well in toe to heel combination enhance better sweep efficiency. They have also pointed out that vertical injection well along with horizontal producer well have better flow distribution than horizontal injector [8].

CMG-Based Simulation Study of Water Flooding …

17

Reservoir simulation utilizes mathematical model of the reservoir and simulates numerically in order to draw important decisions like selection of best recovery method for reservoir, maximization of the economic recovery of hydrocarbons, sensitivity of model parameters, etc. Numerical simulation tool aids to predict the production performance like hydrocarbon recovery at low expenses and in short period of time [9]. In this study, CMG-IMEX (Computer Modeling Group) commercial reservoir simulator has been used to study the effect of various combinations of well pattern horizontal or vertical, its location, and length on the productivity of oil of waterflooding process.

2 Modeling and Simulation The main objective of this study is to analyze the performance of a waterflooding process which is influenced by the flooding pattern and well length. CMG-IMEX (Computer Modeling Group) commercial reservoir simulator has been used to build reservoir model in three dimensions [10]. IMEX numerical simulator that uses IMES (finite difference implicit pressure-explicit saturation) is the CMG’s threephase black oil model. IMEX is used to model primary and secondary oil recovery processes in conventional reservoirs. The problem under study and the data used in the simulation model were obtained from published literature [3, 5]. The porosity of the reservoir in their study is 0.2. Reservoir data are presented in Table 1. Relative permeability and capillary pressure data are reported in Table 2, and the fluid property data are given in Table 3. Our reservoir model is represented by 9 × 9 × 6 Cartesian grid system. The grid block dimensions in horizontal direction (x and y direction) are shown in Fig. 1 and reservoir thickness in vertical direction (z-direction) are reported in Table 1. One injection and one production well are provided in the reservoir model. Nine different flood patterns were considered in order to achieve maximum oil recovery: base case and first four cases are with parallel horizontal injection and production well. The injection well was located at the bottom layer of the reservoir where water saturation is highest and production layer was located at the top layer Table 1 Reservoir data taken from Nghiem et al. [3] Layer

Cent. of layer (ft.)

Δz (ft.)

K x and K y (mD)

K z (mD)

Poil (psi)

S o (–)

1 (top)

3600

20

300

30

3600

0.711

2

3620

20

300

30

3608

0.652

3

3640

20

300

30

3616

0.527

4

3660

20

300

30

3623

0.351

5

3685

30

300

30

3633

0.131

6 (bottom)

3725

50

300

30

3650

0.000

18

P. D. Khurpade et al.

Table 2 Relative permeability and capillary pressure data taken from Nghiem et al. [3] Sw

K rw

K row

Pcow

Sg

K rg

K rog

Pcog

0.22

0.00

1.0000

6.30

0.00

0.000

1.00

0.0

0.30

0.07

0.4000

3.60

0.04

0.000

0.60

0.2

0.40

0.15

0.1250

2.70

0.10

0.022

0.33

0.5

0.50

0.24

0.0649

2.25

0.20

0.100

0.10

1.0

0.60

0.33

0.0048

1.80

0.30

0.240

0.02

1.5

0.80

0.65

0.0000

0.90

0.40

0.340

0.00

2.0

0.90

0.83

0.0000

0.45

0.50

0.420

0.00

2.5

1.00

1.00

0.0000

0.00

0.60

0.500

0.00

3.0

0.70

0.812

0.00

3.5

0.78

1.000

0.00

3.9

Table 3 Fluid property data taken from Nghiem et al. 1991 [3] P (psi)

Rs (ft3 /bbl.)

Bo (bbl./ft3 )

Bg (bbl./ft3 )

μo (cp)

μg (cp)

400

165

1.0120

0.0059

1.17

0.0130

800

335

1.0255

0.00295

1.14

0.0135

1200

500

1.0380

0.00196

1.11

0.0140

1600

665

1.0510

0.00147

1.08

0.0145

2000

828

1.0630

0.00118

1.06

0.0150

2400

985

1.0750

0.00098

1.03

0.0155

2800

1130

1.0870

0.00084

1.00

0.0160

3200

1270

1.0985

0.00074

0.98

0.0165

3600

1390

1.110

0.00065

0.95

0.0170

4000

1500

1.120

0.00059

0.94

0.0175

4400

1600

1.130

0.00054

0.92

0.0180

4800

1676

1.140

0.00049

0.91

0.0185

5200

1750

1.148

0.00045

0.90

0.0190

5600

1810

1.155

0.00042

0.89

0.0195

Description

Values

Oil density and gas lb/ft.3 Water density

lb/ft.3

45, 0.0702 62.14

Oil compressibility for under-saturated oil

1 × 10−5 1/psi

Water compressibility

3 × 10−6 1/psi

Rock compressibility

4 × 10−6 1/psi

Water viscosity cp

0.96

CMG-Based Simulation Study of Water Flooding …

19

Fig. 1 Grid block dimension considered for the simulation study adapted from Nghiem et al. [3]

of the reservoir where saturation of oil is highest. The length of an injection well has been considered in decreasing order from 2700 to 600 ft. for these cases, keeping the length of production well constant, i.e., 900 ft. The length of production well was from 600 to 1800 ft. in stepwise manner with 900 ft. long injection well in cases 5, 6, and 7. The last two simulation cases were performed with vertical injection well close to the reservoir boundary and horizontal production well at the top layer of the reservoir and vice versa. The details of all the cases are as follows: • Base Case: Horizontal Injection well drilled in bottom layer (6) and completed with length 2700 ft.: (I, 5, 6), I = 1, 2, …, 9. Horizontal production well drilled in top layer (1) and completed with length 900 ft.: (I, 5, 1), I = 6, 7, 8. • Cases 1, 2, 3, and 4: Horizontal Injection well drilled in bottom layer (6) and completed with the lengths of (i) 1800 ft.: (I, 5, 6), I = 1, 2, 3, 4, 5, 6. (ii) 1200 ft.: (I, 5, 6), I = 1, 2, 3, 4. (iii) 900 ft.: (I, 5, 6), I = 1, 2, 3. (iv) 600 ft.: (I, 5, 6), I = 1, 2. Horizontal production well: same as case 1. • Case 5, 6, and 7: Horizontal Injection well drilled in bottom layer (6) and completed with the lengths 900 ft.: (I, 5, 6), I = 1, 2, 3. Horizontal production well drilled in top layer (1) and completed with the lengths (v) 600 ft.: (I, 5, 1), I = 7, 8. (vi) 1200 ft.: (I, 5, 1), I = 5, 6, 7, 8. (vii) 1800 ft.: (I, 5,1), I = 3, 4, …, 8.

20

P. D. Khurpade et al.

• Case 8 and 9: Vertical production well drilled from layer 1 to 6 and completed with the length 160 ft. (8, 5, K), K = 1–6, and horizontal injection well with length of 900 ft. drilled in bottom layer: (I, 5, 6), I = 1, 2, 3. Vertical Injection well drilled from layer 1–6 and completed with the length 160 ft. (1, 5, K), K = 1–6, and horizontal production well with length of 900 ft. drilled in top layer (1) as in case 1. For all the cases, injection well was operated with bottom-hole pressure of 3700 psi and production well was operated with constant surface liquid rate of 3000 bbl./day and bottom-hole pressure of 1500 psi.

3 Results and Discussion 3.1 Validation of Simulation Results of Base Case The predicted results of the base case are compared with published results of Nghiem et al. [3] for the same reservoir data by using different simulation tools and by different participants such as ECL Petroleum Technologies and Chevron Oil Company where they had used black oil simulator with special extension of local grid refinement and wellbore hydraulics. Fig. 2a represents the oil rate (bbl./day) and cumulative oil production (bbl.) and Fig. 2b shows the water–oil ratio after 1500 days of simulation time for base case. Table 4 provides comparison results of our simulation runs along with ECL and Chevron Company. As depicted in Table 4, fresh simulation performed

Fig. 2 Simulation results of base case. a Oil rate (bbl/day) and cumulative oil production (bbl) as a function of time. b Water–oil ratio variation with time

Table 4 Comparison of simulation runs for base case with Nghiem et al. [3] Base case

New simulation result

ECL result [3]

Chevron result [3]

Cum. oil production MSTB

749.38

753.6

741

CMG-Based Simulation Study of Water Flooding …

21

using CMG in this study is in good agreement with ECL results with an accuracy level of 99.44% and with Chevron results with accuracy level of 98.87%.

3.2 Simulation Studies for Enhancement of Oil Production Nine different cases were considered for examining the effect of well length and configuration patterns to analyze the waterflood performance in order to enhance the oil production. The trends in cumulative oil production, percentage water cut, and water–oil ratio were analyzed in detail. The best pattern to be considered which would give the highest cumulative oil production with an appreciable low amount of water. The predicted cumulative oil, percentage water cut, and water–oil ratio at the end of 1500 days for all the cases are reported in Table 5. It is to be noted that the enhancement of cumulative oil production and detraction in water–oil ratio is observed for cases 1–3 if the injection well length was decreased from 2100 to 900 ft. but further decrease in injection well length to 600 ft. (Case 4), cumulative oil production decreases. Case 3 indicates the maximum oil production with minimum water–oil ratio. This is due to displacement of more oil through injection of water into wider zones reflecting into higher production of water. The enhancement of cumulative oil production has been observed if the production well length increased from 600 to 1800 ft. but at the expense of increase in water–oil ratio (Cases 5, 6, and 7). This implies that availability of wider zones of the reservoir for production of oil but at the same time as the production well crossed the injection well, more zones are inline and close to injection well which allows faster movement of waterfront towards production well resulted in higher water–oil ratio. Figure 3a, b shows a detailed plot of effect of length of injection and production well on cumulative oil production and water–oil ratio. Only the representative cases were mentioned in Table 5. Two more scenarios (case 8, 9) were simulated in which use of one vertical production with one horizontal injection well, and vice versa were examined for the enhancement of oil production. It is observed that in case 8 with vertical production well and horizontal injection well has very low cumulative oil production due to exposure of production well to less oil-producing zones of the reservoir. Finally, case 9 ensures highest cumulative oil production amongst all the cases with vertical injection well and horizontal production well. This is because horizontal producer well has exposed to more zones of reservoir than vertical well results in better recovery with corresponding low water cut. As a representative case, detailed simulation results of case 9 with vertical injection well (which is the best one) is provided in Fig. 4. It represents the oil rate (bbl./day) and cumulative oil production (bbl.) (panel 4a) and the water–oil ratio after 1500 days of simulation time for case 9 (panel 4b). It is to be noted that water to oil ratio for base case was 10.872 which has been reduced drastically to 6.573 (kindly refer to Figs. 2b and 4b). As depicted in the figure, the increased oil production rate was due to horizontal production well communicating with more zones of

22

P. D. Khurpade et al.

Table 5 Simulation results of all nine flooding patterns Production well

Injection well

Base Case

Case 1

Case 2

Case 3

Case 4

Orientation:

H

H

H

H

H

Length (ft.)

900

900

900

900

900

Layer

1 (T)

1(T)

1(T)

1(T)

1(T)

STL (bbl/day)

3000

3000

3000

3000

3000

Min. BHP (psi)

1500

1500

1500

1500

1500

Orientation

H

H

H

H

H

Length

2700

1800

1200

900

600

Layer

6 (B)

6 (B)

6 (B)

6 (B)

6 (B)

Min. BHP (psi)

3700

3700

3700

3700

3700

749.38

812.75

882.86

901.44

892.09

Cum. oil prod. MSTB Water cut %

91.58

90.27

88.99

88.35

88.24

Water–oil ratio

10.872

9.272

8.079

7.581

7.500

Production well

Injection well

Case 5

Case 6

Case 7

Case 8

Case 9

Orientation:

H

H

H

V

H

Length (ft.)

600

1200

1800

160

900

Layer

1(T)

1(T)

1(T)

1–6

1(T)

STL (bbl/day)

3000

3000

3000

3000

3000

Min. BHP (psi)

1500

1500

1500

1500

1500

Orientation

H

H

H

H

V

Length

900

900

900

900

160

Layer

6 (B)

6 (B)

6 (B)

6 (B)

1–6

3700

3700

3700

3700

3700

Cum. oil prod. MSTB

Min. BHP (psi)

871.83

925.52

944.53

562.07

965.49

Water cut %

87.80

88.99

89.89

88.55

86.80

Water–oil ratio

7.194

8.079

8.891

7.731

6.573

T top, B bottom, H horizontal, V vertical, Min. minimum, STL surface liquid rate, BHP bottom-hole pressure

reservoir and of good vertical to lateral anisotropy, tendency of injected water using vertical well to flow near the bottom of the reservoir by gravity drainage and this bottom pressure provides support for the movement of oil upwards towards horizontal production well which has higher oil saturation. As a result, water coning tendency has also been reduced because of more exposure of production well to oil column due to its placement horizontally in higher oil saturation top layer. In order to increase the performance of waterflooding process and maximum recovery of oil with minimum water cut, good understanding of reservoir geology is essential to place the horizontal or vertical wells properly inside the reservoir.

CMG-Based Simulation Study of Water Flooding …

23

Fig. 3 Effect of well length on Cumulative oil production (MSTB) and water–oil ratio. a Injection well. b Production well

Fig. 4 Simulation results of Case 9. a Oil rate (bbl./day) and cumulative oil (bbl.) versus time. b Water–oil ratio versus time

4 Conclusion The production of oil by secondary recovery method namely water flooding is studied. Different well length and flooding patterns were analyzed to understand productivity. CMG-IMEX (Computer Modeling Group) commercial reservoir simulator was used for the numerical simulation study. Based on simulation results performed, it can be concluded that the well length and horizontal and/or vertical flooding pattern affects the productivity of oil from petroleum reservoir. Short length horizontal injection well and short horizontal production well (represented by case 3) indicated better performance than longer horizontal injection well (Cases 1 and 2). The producer well should be drilled in reservoir layer where oil saturation is highest. Also, vertical injection well and horizontal production well has highest productivity of oil in terms of cumulative production and reduced water cut as compared to both horizontal injector and producer flooding pattern. With this flooding configuration (represented by case 9), cumulative oil production increased by 28.83%, and water cut is decreased by 39.54% of the base case. Hence, the study clearly indicated that oil recovery can substantially be increased by utilizing different flooding patterns and appropriate

24

P. D. Khurpade et al.

length of injector and producer well and their location so as to enhance the sweep efficiency and to delay the water breakthrough.

References 1. Sarma, P., Aziz, K., Durlofsky, L.J.: Implementation of adjoint solution for optimal control of smart wells. Paper SPE 92864 presented at the 2005 SPE Reservoir Simulation Symposium held in Houston, Texas, 31 Jan–2 Feb 2005 2. Nwaozo, J.: Dynamic optimization of a water flood reservoir. M.Sc. thesis, University of Oklahoma Graduate College, Norman, Oklahoma, USA (2006) 3. Nghiem, L., Collins D., Sharma, R.: Seventh SPE comparative solution project: modelling of horizontal wells in reservoir simulation. SPE 21221, presented at 11th SPE Symposium on Reservoir Simulation held in Anaheim, California, 17–20 Feb 1991 4. Algharaib, M., Gharib, R.B.C.: A comparative analysis of waterflooding projects using horizontal wells. SPE 93743, presented at 2005 Middle East Oil Show held in Bahrain, 12–15 Mar 2005 5. Taber, J., Seright, R.S.: Horizontal injection and production wells for EOR or waterflooding. SPE 23952, presented at 1992 SPE Permian Basin Oil and Gas Recovery Conference held in Midland, Texas, March 18–20, (1992) 6. Joshi, S.D.: Horizontal Wells Technology. Penwell Publishing Company, Tulsa, Oklahoma (1991) 7. Broman, W.H., Stagg, T.O.: Horizontal wells performance evaluation at Prudhoe Bay. SPE 90-124, presented at SPE Annual Technical Meeting held in Calgary, Alberta, 10–13 June 1990 8. Popa, C.G., Chipea, M.: Improved water flooding efficiency by horizontal wells. SPE 50400, presented at the SPE International Conference on Horizontal Well Technology, Calgary, Alberta, Canada, 1–4 Nov 1998 9. Aziz, K., Settari, A.: Petroleum Reservoir Simulation. Applied Science Publisher Ltd., London (1979) 10. Gielisse, R.A.M.: Dynamic local grid refinement. M.Sc. thesis, Delft University of Technology, Netherlands (2016)

Exergy-Based Comparison of Two Gas Turbine Plants with Naphtha and Naphtha-RFG Mixture as Fuels Sankalp Arpit, Sagar Saren, Prasanta Kumar Das, and Sukanta Kumar Dash

Nomenclature cp Ex h m˙ P R s T W W˙

Specific heat at constant pressure (kJ/kg K) Exergy rate (kW) Specific enthalpy (kJ/kg) Mass flow rate (kg/s) Pressure (kPa) Gas constant (J/gK) Specific entropy (kJ/kg K) Temperature (K) Work (kJ) Power (kW)

Greek Letters η ξ

Efficiency Ratio of chemical exergy and lower heating value of fuel

S. Arpit (B) School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India e-mail: [email protected] S. Saren · P. K. Das · S. K. Dash Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_3

25

26

S. Arpit et al.

Subscripts a e g i o

Air Exit Exhaust gas Inlet Dead state

Abbreviations AC CC GT LHV

Air compressor Combustion chamber Gas turbine Lower heating value

1 Introduction Improvement in human lifestyle has caused a huge gap in energy demand and supply. In order to narrow demand and supply gap, various energy resources are being accessed which are either renewable or non-renewable. From the past, most of the energy demand is met by non-renewable sources of energy like coal and natural gas, processed in power plants. Hence, thermodynamic analysis of power plant is important to understand the underlying principle” More from less” principle. In recent decades, the exergy [1, 2] analysis has been adopted as more useful method in the design, optimization, and improvement of energy systems such as gas turbine power plants. In the present paper, two Gas Turbine Plants (GT1 and GT2) plant of 34.5 MW (Figs. 1 and 2) each has been taken up as a case study for thermodynamic analysis. GT1 is being charged by naphtha, whereas GT2 is charged by Residual fuel gas and Naphtha mixture. Thermodynamic analysis based on energy and exergy is performed using actual operating data. Technical specification is provided in Table 1. In GT1 and GT2, fresh air (109.2 kg/s) form outside environment (1) enters the compressor and compressed non-isentropically to higher temperature and pressure (2). The highpressure and high-temperature air is used to burn fuel (5) in case of GT1 (5 and 6), in case of GT2 inside the combustion chamber. The resulting high temperature and high pressure (3) combustion product enters the turbine, gets expanded nonisentropically (4) while producing power. A part of this turbine power is used to drive the compressor.

Exergy-Based Comparison of Two Gas Turbine Plants …

27

Fig. 1 Open cycle gas turbine power plant (GT1) with Naphtha (5) as fuel (case A)

Fig. 2 Open cycle diagram of gas turbine power plant (GT1) with Naphtha (5) and Residual fuel gas (6) as fuel (case B) Table 1 Raw data of GT1 and GT2 Parameters

GT2 (Naphtha and RFG)

GT1 (Naphtha)

Mass flow rate

110.2 kg/s

110.2 kg/s

CPD

8.9 bar

8.9 bar

CPT

366 °C

366 °C

Naphtha

0.8 kg/s, 19.6 bar, 34 °C

2.57 kg/s, 15.2 bar, 34 °C

Residual fuel gas

1.47 kg/s, 15.2 bar, 92 °C



Rated work

34.5 MW

34.5 MW

Actual work

29.98 MW

24.45 MW

28

S. Arpit et al.

2 Modelling of Proposed System Assumptions were taken in order to model the gas turbine unit: • Steady-state operating condition and ideal gas behaviour of air and combustion gases constituents. • Kinetic and potential energy of fluid streams is neglected. The dead-state conditions at 101.325 kPa and 303 K. • Molar air composition is 77.48% N2 , 20.59% O2 , 0.03% CO2 . • The naphtha has following composition: C (0.8392), H2 (0.1583), S (0.001). Lower heating value is 44,079 kJ/kg. • The residual fuel gas (Residual fuel gas) has following composition: H2 (0.3674), CO (0.0005), H2 S (0.0001), CH4 (0.4986), C2 H4 (0.0144), C2 H6 (0.0096), C3 H8 (0.0173), C3 H6 (0.0073). Lower heating value is 51,660 kJ/kg.

2.1 Energy Analysis The principle of mass conservation, energy conservation and exergy balance equation is applied for thermodynamic modelling of each and every component with possible heat interaction and work transfer. (a) Compressor: The energy balance for air compressor subsystem is given by

W˙ AC = m˙ a c pa (T2 − T1 ) 

T2s T1



 =

ηAC =

P2 P1

(1)

 γ −1 y

T2s − T1 T2 − T1

(2) (3)

Inlet and outlet of the compressor air temperature are indicated by T1 and T2 . ηAC is the isentropic efficiency of air compressor and γ is specific heat ratio. (b) Combustion chamber The energy balance for the combustion chamber subsystem for GT1 and GT2 is given by Eqs. (2) and (5). m˙ a h 2 + m˙ 5 LHV5 = m˙ g h 3

(4)

m˙ a h 2 + m˙ 5 LHV5 + m˙ 6 LHV6 = m˙ g h 3

(5)

Exergy-Based Comparison of Two Gas Turbine Plants …

29

(c) Gas turbine The energy balance equation for gas turbine is given by: W˙ GT = m˙ g (h 3 − h 4 ) 

T3 T4



 =

ηGT =

P3 P4

(6)

 γ −1 y

T3 − T4 T3 − T4s

(7) (8)

Mass flow rate of flue gas is denoted by m˙ g m˙ g = m˙ f + m˙ a

(9)

The net power can be expressed as W˙ net = W˙ GT + W˙ AC

(10)

T3 and T4 indicate temperature of gas at inlet and outlet of gas turbine. ηGT is isentropic efficiency of gas turbine; c pg is the specific heat of gas. Variation of specific heat of flue gas c pg considering the composition of the combustion products with temperature for GT1 and GT2 is given below c pg = 0.9840 + 0.0001262T + 0.000000146T 2 (For GT1) c pg = 1.031 + 0.0000858T + 0.000000195T 2 (For GT2) Realizing energy analysis of each and every component, first law analysis can be carried out by equations written below. W˙ net,GT1 m˙ 5 LHV5

(11)

W˙ net,GT2 m˙ 5 LHV5 + m˙ 6 LHV6

(12)

η I,GT = η I,GT =

2.2 Exergy Analysis Maximum useful work obtained from a system is represented by exergy and is a widely accepted tool for thermodynamic analysis. Physical exergy designates

30

S. Arpit et al.

maximum work potential of system while chemical exergy is related to change of chemical composition of a system from its equilibrium conditions. E˙ x,heat +



m˙ i ex,i =



m˙ e ex,e + E˙ x,w + I˙dest

(13)

e

i

  T0 × Q˙ i E˙ x,heat = 1 − Ti

(14)

E˙ x,W = W˙

(15)

In Eq. (13), E˙ x,heat represent the exergy flow due to heat transfer, i and e represent inlet and exit condition of energy systems. Further, E˙ x,W represents exergy flow due to work. In order to calculate physical exergy of water/steam phases, equation written below is used. ex = ex,physical + ex,chemical

(16)

ex,physical = (h − h 0 ) − T0 (s − s0 )

(17)

In Eq. (15) h 0 and s0 are enthalpy and entropy values of system at dead-state conditions. In the thermodynamic analysis, chemical exergy of fuel and combustion products have an important role. The chemical exergy of Naphtha is determined by Eq. (18) ζ =

ex,fuel LHV

(18)

ζ represents ratio of chemical exergy to lower heating value of the fuel. In order to calculate chemical exergy of gaseous fuel and combustion products below equation can be used.  n  n   ex,chemical = xi ex,chemical,i + RT0 xi ln(xi ) (19) i=1

i=1

In order to determine the chemical exergy of combustion gases, it is key to know the molar composition of it after combustion process. The molar fraction of combustion gases (Tables 2 and 3) produced in GT1 and GT2 is found by the chemical equation. The following equations can be used to calculate the exergy efficiency of the gas turbine power plant. η I i,GT =

W˙ net,GT1 E˙ xNaphtha

(20)

Exergy-Based Comparison of Two Gas Turbine Plants … Table 2 Flue gas composition in GT1 and GT2

Component CO2

Table 3 Thermodynamic property comparison between two fuels

Molar fraction (GT1)

31 Molar fraction (GT2)

ex,chemical

0.78

4.58

N2

83.72

83.72

25.71

O2

16.4

15.09

124.06

SO2

0.0051

0.0016

H2 O

3.6

7.7

442.73

313.4 9.5

Thermodynamic property

GT1

GT2

Isentropic efficiency AC (%)

79

79

Isentropic efficiency GT (%)

89.4

91

Turbine inlet temperature (K)

1089

1133

First law efficiency (%)

21.58

20.18

η I i,GT =

W˙ net,GT2 ˙ E xNaphtha + E˙ xRFG

(21)

3 Results and Discussions This section presents energy and exergy analysis of GT plant working on naphtha and naphtha and residual fuel gas with the same configuration.

3.1 Energy Analysis of GT2 and GT1 This section presents the comparison between GT2 and GT1 based on energy and exergy analysis. Table 3 presents the comparison of thermodynamic property between GT2 and GT1. Further, energy flow of both gas turbine (GT2 and GT1) is presented in form of Sankey diagram (Figs. 3 and 4), which depicts enthalpy flows across the various components. The flows are represented as arrows, and the width represents amount of energy. From the diagram, it can be inferred that 36.9 MW is provided by the turbine, and turbine produces 181.3 MW from which 29.98 MW is output as electrical energy and the remaining 114.97 MW is produced as waste heat in GT2.

32

S. Arpit et al.

Fig. 3 Sankey diagram of GT1

Fig. 4 Sankey diagram of GT2

3.2 Exergy Analysis of GT2 and GT1 This section presents exergy analysis of both gas turbine (GT2 and GT1). Table 4 presents the value of exergy destruction and exergy efficiency of GT2 and GT1. It can be seen that in case of GT2 exergy destruction is quite high because of high temperature of Residual Fuel gas as compared to Naphtha. Furthermore, exergy efficiency (Fig. 7) of GT2 (20.70%) is higher as compared to GT1 (20.17%) (Figs. 5 and 6).

Exergy-Based Comparison of Two Gas Turbine Plants …

33

Fig. 5 Grassman diagram of GT1

Fig. 6 Grassmann diagram of GT2 Table 4 Exergy destruction of GT1 and GT2 Equipment

Exergy destruction (GT1)

Exergy destruction (GT2)

AC

4300

5700

CC

58,700

78,200

GT

6320

2100

34

S. Arpit et al. 30

Fig. 7 Energy and Exergy efficiency of GT1 and GT2 Efficiency (%)

25 20

26 21.5

20.7

20.17

15 10 5 0

GT1 Energy Efficiency

GT2 Exergy Efficiency

4 Conclusion Energy and exergy analysis of two GT power plant configuration, case A Naphtha based, case B Naphtha and Residual Fuel gas have been performed. Some of the main conclusions that can be drawn from this study are mentioned below. (a) The combustion chamber is a high source of exergy destruction in gas turbines followed by air compressors and gas turbines. (b) In case of GT2, exergy destruction in combustion chamber is high as the temperature difference between naphtha and residual fuel gas high causing mixing. Second law analysis plays a crucial role in the evaluation of thermodynamic system. Some recommendations are as follows: • Due to additional fuel Naphtha–Residual fuel gas mixture, both energy and exergy efficiency of GT2 has increased as compared to GT1, but due to high exergy destruction in case of GT2 there is a negligible increase in exergy efficiency. • Some sort of preheating arrangement can be made in GT2 so as to reduce exergy destruction due to mixing losses between Naphtha and Residual fuel gas.

References 1. Rosen, M.A., Le, M.N., Dincer, I., Casas-Ledón, Y., Spaudo, F., Arteaga-Pérez, L.E., et al.: Exergoenvironmental analysis of a waste-based Integrated Combined Cycle (WICC) for heat and power production. Energy 32, 249–253 (2005) 2. Ersayin, E., Ozgener, L.: Performance analysis of combined cycle power plants: a case study. Renew. Sustain. Energy Rev. 43, 832–842 (2015)

Decentralized Solid Waste Management for Educational-Cum-Residential Campus: A Pilot Study Deepak Singh Baghel

and Yogesh Bafna

1 Introduction Appropriate handling of the anthropogenic waste is a great challenge faced by world to achieve goal of sustainable development [1]. All the garbage coming out of animal and human activities that possess no use is termed as solid waste. 1,41,064 metric tonnes per day of municipal solid waste (MSW) is generated in India [2] (see Fig. 1). Major contributing factors for this include urban population which is 31% of total population and rate of urbanization which is 2.4% during 2011–2015 [3]. About 80– 90% of total MSW is collected out of which only 22–27% is processed and treated [4, 5]. This huge waste generated requires a suitable locations and high treatment cost due to which municipalities implemented integrated waste management policy. This includes methods such as waste minimization, processing by reuse and recycling, treatment by composting, incineration, biomethanation, pyrolysis, gasification, and final disposal in landfill [6]. These methods are applicable based on the quality and quantity of waste produced [7]. To facilitate collection, segregation, storage, transportation, processing, and disposal of MSW, solid waste management (SWM) rules are circulated by MOEF [8]. The MSW possesses huge variations in terms of volume and quality at different locations and has correlation with economic status of people. Various Indian cities have limitations in the current practices used in municipal solid waste management (MSWM) which includes deficit manpower, machinery and finances, and lack of implementation [9]. Further, collection efficiency of waste is another problem due to thousands of waste which remain unhandled per day [10]. Transportation of MSW is another major issue which increases overall cost and causes environmental pollution. Large number of researches for optimizing total overall cost of MSWM including transportation has been carried out [11, 12]. Further, D. S. Baghel (B) · Y. Bafna MPSTME, NMIMS, Shirpur, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_4

35

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D. S. Baghel and Y. Bafna

Metals Glass and rags Paper and plastics Ash and Fine earth Food and vegetable waste Fig. 1 Composition of municipal solid waste [2]

MSWM at centralized level includes various risks in terms of planning and finance [13]. Also, site selection of these MSW facilities requires different considerations of geology, water supply resources, land use, sensitive sites, air quality, groundwater quality, etc. [14]. Decentralization of MSWM is solution to the problem [15]. This will increase collection efficiency of waste. MSW generated India consists mainly of biodegradable matter with high moisture content [2]. Hence, after segregation, processing of waste using composting and biomethanation can be adopted. However, after removing reusable and recyclable materials from remaining waste, disposal in engineered landfill is recommended [5]. Numerous innovations and methodologies on composting, biomethanation and sanitary landfill have been developed to provide efficient results. Few of these include specifying characteristics to represent degree of maturity of product using box composting [16], spectrometric analysis of organic matter transformation [17] during composting, pilot scale study of composting for waste minimization, resource recovery and increased crop yield [18], effect of leachate recirculation on acidification in biogas plant [19], behavior of metals and non-metals in a landfill leachate [20] and environmental impacts and operating cost of landfill [21], and use of synthetic waste for anaerobic digestion of restaurant waste to produce methane by varying input lipid [22]. Current work presents decentralized solid waste management using two systems viz composting and biomethanation for a closed campus.

2 Materials and Methods The study is performed in an educational-cum-residential campus located in Shirpur, Maharashtra. The campus consists of mess area and canteen for serving food to campus students and staff, hostels for students, and quarters for staff, laundry, and academic buildings. The campus has well-functioning waste collection facility. Food, paper, plastics, and polythenes are collected in separate dustbins, and wastes like rags, leaves, wood, etc., are collected separately. These wastes are further sent to local municipal corporation for disposal. Presently, the campus does not have its own any solid waste management facility. With the increasing population in campus,

Decentralized Solid Waste Management for Educational …

37

MSW

Seggregated collection

Quantification of waste

Analysis of waste

Sample collection and analysis

Monitoring

Design of biomethantion pilot plant

Design of compost

Fig. 2 Flowchart of overall methodology

the quantity of waste will rise. Hence, adequate management of waste by available techniques will help in improving campus scenario. The flowchart of overall methodology is depicted in Fig. 2.

2.1 Composting Composting is decomposition of organic matter to produce compost. It can be done either aerobically or anaerobically. Aerobic composting Two types of methods namely windrow composting and box composting are adopted. Windrow composting requires as set of long narrow piles arranged on a compost pad to receive waste one by one daily for 35 days (composting period). The composting pits are designed and constructed by brick masonry having a concrete base to prevent leachate percolation to soil. The depth of these aerobic composts is kept low to keep air circulation and facilitate turning up regularly. Rectangular shaped windrow and box composts are designed to receive daily biodegradable waste. The composts in both methods are filled in layers consisting chopped vegetables followed by cow dung and food waste (see Fig. 3a). Green and dry leaves are added to overcome high moisture content and C/N ratio of incoming waste (Table 1). A sample of sewage (activator) from aeration tank of sewage treatment plant in campus is added as seed to facilitate the process. Because of dry season, additional sprinkling of water and leachate recirculation is also required. To maintain aerobic environment, the composts are turned up regularly in 5 days and to analyze quality of compost, samples are taken on 14th, 21st, 28th, and 35th day. Anaerobic composting Box-type anaerobic compost is designed and constructed with capacity to receive one-day organic waste. Materials used are same as aerobic pit. Unlike aerobic method, the depth of these aerobic composts is kept higher. The compost is filled in layers similar to aerobic one, but the number of layers is increased because of greater depth. Also, a thick soil cover followed by plastic cover is applied at top to keep anaerobic environment. A leachate collection system at the bottom and methane escape vent at the top is provided.

38

D. S. Baghel and Y. Bafna

Fig. 3 Compost filling and sample

Table 1 Characteristics of biodegradable waste before composting S. No.

Parameter

Value

Required range (CPHEEO Manual on MSW, 2016)

1.

Moisture content (%)

63.4

55–60

2.

C/N ratio

32:1

25:1–30:1

2.2 Biomethanation The waste from kitchen consisting of chopped vegetables and food is also fed into the pilot waste-to-energy plant to get nutrient rich slurry and methane under anaerobic condition. The characteristics of waste are given in Table 2. The food waste is fed as slurry by mixing it water having temperature 45 °C and cow dung [23–25]. A sample of sewage and compost is added to maintain optimum C/N ratio in digester. The pH of sample is monitored to be between 6.5 and 7.5 [26, 27]. The slurry within the tank is also frequently stirred to prevent layer formation and continuously mix waste. The plant is operated for 30 days. Table 2 Characteristics of biodegradable waste before biomethanation

S. No.

Parameter

Value %

1.

Moisture content

68.4

2.

Total solids (TS)

31.6

3.

Total volatile solids (TVS)

79.7

Decentralized Solid Waste Management for Educational …

39

After which biomethane produced treated further to remove water vapor and hydrogen sulfide. A container having water at 20 °C is kept to remove water vapor by condensation and hydrogen sulfide which is soluble in water up to certain extent [28]. Biogas contains 50–85% CH4 (methane), 20–35% CO2 , and H2 , N2 , and H2 S form the rest [29]. Density of methane is 1.15 kg/m3 [30]. The treated methane gas is collected in Mylar balloon. By weighing the treated biomethane (H2 O removal and H2 S removal), the quantity of biogas per kg of total solids is estimated. Pilot waste-to-energy plant The circular cylinder diameter 1200 m and height 760 mm is taken as digester. It has an inlet of diameter 80 mm fitted with oneway cap to feed waste into digester. Five steel plate wings are fixed to plastic pipe through screws. A pipe above the base 240 mm is inserted to hold and manually rotating steel plate fans. The length of wings is 190 mm which helps to mix the inner contains. The material is filled up to height of 350 mm, and the above space is kept empty for gas collection. The upper part is covered with a dome-shaped lid for gas collection. An outlet gas pipe of diameter 190 mm and length 1000 mm is provided which is connected to another cylindrical container having water to remove H2 O and H2 S (see Fig. 4). The digester is provided with a slurry outlet of diameter 190 mm at bottom. An outlet for treated gas is provided at the top of water cylinder fitted with Mylar balloon to collect gas. Table 3 shows details of feedstock into the digester. The plant runs for 30 days with operating parameters as presented in Table 4.

Inlet

Clean Methane

Raw Methane

Digester

H S and H2O 2

removal

Slurry outlet Fig. 4 Pilot plant schematic

Table 3 Details of feedstock into digester

Material

Quantity

Water

200 L is poured

Food waste (from mess)

50 kg is poured

Cow dung

12.5 kg is poured

Total material filling

72.5 kga

aA

small quantity of sewage and compost is also mixed

40

D. S. Baghel and Y. Bafna

Table 4 Operating parameters of digester S. No.

Parameter

Value %

1.

Temperature

35–37 °C

2.

pH

6.5–7.5

3.

(C/N) ratio in anaerobic digesters

20:30

Table 5 Solid waste quantification and characteristics S. No.

Quantity(kg/day)

Source

Type of waste

Properties

1.

430

Mess and canteen

Food and leaves

Biodegradable

2.

280

Stationery, offices, canteen

Paper, wood, cardboard, glass, plastic

Recyclable/reusable

3.

100

Pavements, construction site, water boilers, WTP

Dust, silt, sand, ash

Land fillable

4.

190

Laundry and canteen Textile and wrappers

Recyclable

3 Data Collection The details of data collected for a total of 1000 kg/day waste produced is mentioned in Table 5.

4 Results and Discussion Details of composts designed by each method are presented in Table 6. Total area required for windrow facility of 35 days was 166 m2 . The total quantity of compost produced by aerobic method is 77 kg for box compost and 2350 kg for windrow compost. The collected aerobic box compost samples on 14th, 21st, 28th, and 35th day are dried in oven and analyzed to get parameters as presented in Table 7. Analysis of compost showed that • Compost is having high moisture which can be due to high moisture in feedstock sample, and more dry leaves needs to be added in it. • Also, higher moisture content resulted in brownish color of compost. • Nitrogen content is quite high which makes suitable for plants and crops. • All other parameters are within standards which makes compost suitable for application. The quantity of biomethane after weighing was 4.96 kg. The biomethane production was estimated to 0.162 m3 /kg of TS which is close to that of Bhattacharyya

Decentralized Solid Waste Management for Educational …

41

Table 6 Specifications of composts Density

Aerobic method

Anaerobic method

450 kg/m3 Windrow composting • Shape rectangular • Spacing = 1.5 m • Dimension = 1.27 m × 1.0 m × 0.75 m • Leachate tank = 1 m × 1 m × 0.5 m • Time = 35 days • Feedstock + cow dung + leaves

• Dimension = 1.0 m × 0.41 m × 2.30 m • Time = 120 days • Feedstock + cow dung + soil + leaves

Box composting • Dimension = 1.27 m × 1.0 m × 0.75 m • Time = 30 days • Feedstock + cow dung + leaves

Table 7 Compost quality S. No.

Parameter

Value

Organic compost (Fertilizer Control Order 2009)

Phosphate-rich organic manure (Fertilizer Control Order 2013)

1.

Total nitrogen (as N), percent by weight

1.2

0.8

0.4

2.

Total phosphate (as P205 ) 0.7 percent by weight

0.4

10.4

3.

Total Potassium (as K20 ), 1.0 percent by weight

0.4

4.

Moisture content

31.7%

15.0–25.0

25.0

5.

C/N ratio

18:1.

Less than 20:1

Less than 20:1

6.

Color

Brownish

Dark brown to black

7.

Odor

Absent

Absence of foul odor

8.

pH

7.2

6.5–7.5

(1:5 solution) maximum 6.7

et al. [19], Dhar et al. [31], and Kumar et al. [32]. Assuming 70% CH4 in biogas, the total quantity of methane produced is 0.12 m3 /kg of TS and 0.21 m3 /kg of VS which is near to Babaee and Shayegan [33], Cho et al. [34], and Dhar et al. [31]. The result shows that cleaning of biomethane by water was effective and provided better quality methane.

42

D. S. Baghel and Y. Bafna

5 Conclusion Current scenario of population growth and increasing waste has overloaded the waste management system in any city. The municipal corporations are struggling with handling the collection and disposal of such a huge amount of waste. The cost of collection and transportation itself contributes to huge percentage of overall cost for MSW. The present investigation for decentralized management of solid waste based on different techniques and their modifications by researchers presents feasible results. The results further depict that 2350 kg of compost can be generated by aerobic windrow composting which can suffice campus’s in-house nursery compost requirement and save manure cost. However, keen monitoring of compost is required because of higher moisture content and nitrogen present in it. The excess leachate produced can be sent to in-house sewage treatment plant for stabilization. The pilot waste-to-energy plant developed can be upgraded to full scale and result in production of 16 m3 of biomethane for total 430 kg daily food waste. The treated methane can be either stored or used directly for partial replacement of LPG for cooking purpose. Slurry produced in digester can be dried and being rich in nutrients can be used as manure. Campus has already attained status of zero liquid by recycling its wastewater after treatment. By adopting the MSWM methodology presented, waste-free campus status can be achieved. Engineered landfill not recommended because of location of campus and residential population. Moreover, the quantity of land fillable of waste is also less. However, it can be used for town of Shirpur because of low water table and availability of suitable sites for setting up landfill. These results can be utilized for solid waste management of the developing town of Shirpur under Swachh Bharat Mission. Further, this approach can be applied to similar localities in more efficient and automated manner to produce fuel and energy. Acknowledgements We are thankful to management of SVKM’s NMIMS for funding the project and SVKM’s NMIMS MPSTME, Shirpur campus for facilities rendered for the research work.

References 1. Anyaoku, C.C., Baroutian, S.: Decentralized anaerobic digestion systems for increased utilization of biogas from municipal solid waste. Renew. Sustain. Energy Rev. 90, 982–991 (2018) 2. Asefi, H., Lim, S.: A bi-objective optimization approach to a municipal solid waste management system. In: 15th International Conference on Environmental Science and Technology, Rhodes, Greece (2017) 3. Babaee, A., Shayegan, J.: Effect of organic loading rates (OLR) on production of methane from anaerobic digestion of vegetables waste. In: World Renewable Energy Congress, Sweden, pp. 411–417 (2011) 4. Belevi, H., Baccini, P.: Long-term behavior of municipal solid waste landfills. Waste Manage. Res. 7, 43–56 (1989) 5. Bhattacharyya, J.K., Kumar, S., Devotta, S.: Studies on acidification in two-phase biomethanation process of municipal solid waste. Waste Manage. 28, 164–169 (2008)

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6. Castaldi, P., Alberti, G., Merella, R., Melis, P.: Study of the organic matter during municipal solid waste composting aimed at identifying suitable parameters for the evaluation of compost maturity. Waste Manage. Res. 25, 209–213 (2005) 7. Census: Ministry of Home Affairs, India (2011) 8. Central Pollution Control Board: Waste Generation and Composition. Ministry Environment Forest and Climate Change, India (2017) 9. Central Pollution Control Board: The National Action Plan for Municipal Solid Waste Management. Ministry Environment Forest and Climate Change, India (2017) 10. Central Pollution Control Board: Selection Criteria for Waste Processing Technologies. Ministry Environment Forest and Climate Change, India (2016) 11. Central Pollution Control Board: Solid Waste Management (SWM) rules. Ministry of Environment Forest and Climate Change, India (2016) 12. Chefetz, B., Hatcher, P.G., Hadar, Y., Chen, Y.: Chemical and Biological Characterization of Organic Matter during Composting of Municipal Solid Waste. J. Environ. Qual. 25, 776 (1996) 13. Chen, Y., Cheng, J.J., Creamer, K.S.: Inhibition of anaerobic digestion process: a review. Bioresour. Technol. 99, 4044–4064 (2008) 14. Cho, J.K., Park, S.C., Chang, H.N.: Biochemical methane potential and solid state anaerobic digestion of Korean food wastes. Bioresour. Technol. 52, 245–253 (1995) 15. CPHEEO: Manual on Solid Waste Management. Ministry of Housing and Urban Affairs, India (2016) 16. Deublein, D., Steinhauser, A.: Biogas from Waste and Renewable Resources (2008) 17. Dhar, H., Kumar, P., Kumar, S., Mukherjee, S., Vaidya, A.N.: Effect of organic loading rate during anaerobic digestion of municipal solid waste. Bioresour. Technol. 2–7 (2015) 18. Ghiani, G., Laganà, D., Manni, E., Musmanno, R., Vigo, D.: Computers & operations research operations research in solid waste management: a survey of strategic and tactical issues. Comput. Oper. Res. 44, 22–32 (2014) 19. Johannessen, L.M., Boyer, G., Mikkel, L.: Observations of Solid Waste Landfills in Developing Countries: Africa, Asia, and Latin America. World Bank Rep. 47 (1999) 20. Jørgensen, P.J.: Biogas-green energy (2009) 21. Kumar, S., Bhattacharyya, J.K., Vaidya, A.N., Chakrabarti, T., Devotta, S., Akolkar, A.B.: Assessment of the status of municipal solid waste management in metro cities, state capitals, class I cities, and class II towns in India: an insight. Waste Manage. 29, 883–895 (2009) 22. Kumar, S., Mukherjee, S., Devotta, S.: Anaerobic digestion of vegetable market waste in India. World Rev. Sci. Technol. Sustain. Dev. 7, 217–224 (2010) 23. Lee, J.I., Mather, A.E.: Solubility of Hydrogen Sulfide in Water, Berichte der Bunsengesellschaft für physikalische Chemie (1977) 24. Li, J., Kumar Jha, A., He, J., Ban, Q., Chang, S., Wang, P.: Assessment of the effects of dry anaerobic co-digestion of cow dung with waste water sludge on biogas yield and biodegradability. Int. J. Phys. Sci. 6, 3723–3732 (2011) 25. Mata-Alvarez, J., Mac, S., Llabr, P.: Anaerobic digestion of organic solid wastes. An overview of research achievements and perspectives. Bioresour. Technol. 74, 3–16 (2000) 26. Mbuligwe, S.E., Kassenga, G.R., Kaseva, M.E., Chaggu, E.J.: Potential and constraints of composting domestic solid waste in developing countries: findings from a pilot study in Dar es Salaam, Tanzania. Resour. Conserv. Recycl. 36, 45–59 (2002) 27. Neves, L., Gonçalo, E., Oliveira, R., Alves, M.M.: Influence of composition on the biomethanation potential of restaurant waste at mesophilic temperatures. Waste Manage. 28, 965–972 (2008) 28. Oliveira, L.S.B.L., Oliveira, D.S.B.L., Bezerra, B.S., Silva Pereira, B., Battistelle, R.A.G.: Environmental analysis of organic waste treatment focusing on composting scenarios. J. Clean. Prod. 155, 229–237 (2017) 29. Pastorek, Z., Kára, J., Jeviˇc, P.: Biomasa - obnovitelný zdroj energie. FCC Public, Prague, p. 288 (2004) 30. Rajeshwari, K., Pant, D., Lata, K., Kishore, V.: Studies on biomethanation of vegetable market waste. In: Biogas Forum (1998)

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31. Sharholy, M., Ahmad, K., Mahmood, G., Trivedi, R.: Municipal solid waste management in Indian cities—a review. Waste Manage. 28, 459–467 (2008) 32. Sumathi, V.R., Natesan, U., Sarkar, C.: GIS-based approach for optimized siting of municipal solid waste landfill. Waste Manage. 28, 2146–2160 (2008) 33. Tan, S.T., Lee, C.T., Hashim, H., Ho, W.S., Lim, J.S.: Optimal process network for municipal solid waste management in Iskandar Malaysia. J. Clean. Prod. 71, 48–58 (2014) 34. Wei, Y., Li, J., Shi, D., Liu, G., Zhao, Y., Shimaoka, T.: Environmental challenges impeding the composting of biodegradable municipal solid waste: a critical review. Resour. Conserv. Recycl. 122, 51–65 (2017)

Does the Criteria of Instability Thresholds During Density Wave Oscillations Need to Be Redefined? Subhanker Paul, Suparna Paul, Maria Fernandino, and Carlos Alberto Dorao

1 Introduction Two-phase flow instabilities and in particular the density wave oscillations are commonly observed instabilities [1, 2] in flow boiling and condensation systems such as nuclear reactors and steam generators. These instabilities are found to be one among the major impediments in increasing the efficiency of aforementioned devices. Besides, these instabilities hinder the performance of solar thermal power production and are also seen in other systems ranging from turbine blades, rocket engines, chemical processes for hydrogen and metal production, cooling of avionics systems, hybrid vehicle power electronics, air conditioning, and space cooling technologies. The associated multifaceted ill-effects on the system performance (namely mechanical vibrations, thermal fatigue, and the heat-transfer deterioration) have asked the researchers to do research on the DWOs over a few decades. In particular, over last 80 years, several researchers have attempted to unveil the accurate mechanism of this instability, knowing which, it can be effectively controlled. Based on the intensive numerical and experimental evidence, so far three mechanisms have been postulated by the researchers as follows: 1. The DWOs can be attributed as the delayed feedback of the transient distribution of the pressure drop along the pipe [2, 3]. This feedback is caused by the difference in the densities between the subcooled liquid entering the channel and the two-phase mixture exiting. A pressure drop perturbation in the flow leads to a flow rate perturbation, which causes an enthalpy perturbation propagating throughout the pipe. This modifies the lengths of the single-phase and the two-phase regions [2] which alter the densities of the fluid in these regions. When a certain amount S. Paul (B) · S. Paul · M. Fernandino · C. A. Dorao Department of Energy and Process Engineering, Norwegian University of Science and Technology, NTNU, Trondheim, Norway e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_5

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of feedback is induced by the perturbations, a series of self-sustained oscillating high- and low-density fluid flow appears which is broadly known as DWOs. In general, the period of oscillations is double to the channel residence time. The above-said mechanism suggests that the dominant factor to trigger the instability is the density variation of the fluid along the length of the channel which was first proposed by Stenning [3] and later widely accepted. 2. In 1994, Rizwan-Uddin [4] based on his numerical investigations criticized the above mechanism. In this study, it was found that the alternate higher- and lowerdensity waves cannot be treated as the fundamental mechanism. Instead, the variation in the mixture velocity is dominant over the mixture density. It was concluded that the oscillations can persist even with very weak density waves. One strong argument made in this study is: the exit pressure drop changes strongly with the exit velocity instead of the exit density. In addition, it was also found that the period of oscillations was closer to four times the channel residence time. It should be noted that, although the above-mentioned mechanisms are widely accepted, they do not provide much details of the applicability of such mechanisms on controlling the amplitude and frequency of the DWOs. 3. In view of this knowledge gap, in the recent studies of O’Neill [5], a counter mechanism to the above-said mechanisms of DWO is presented. Based on a number of experiments and numerical investigations, it was found that, instead of the conventionally accepted feedback effects of pressure drop, flow rate and flow enthalpy change, the body force acting on the system plays a dominant role to define the characteristics of the DWOs. It is mentioned that the body force, acting on the liquid and the vapor phase separately, creates an accumulation of the liquid in the inlet of the channel. The accumulation of the liquid at the channel inlet forms a high-density front (HDF) which travels along the channel. This HDF during its travel along the channel re-wets the liquid film and thus re-establish annular co-current flow. These mechanisms being contradictory to each other suggest that further research is needed to attain an agreement on the accurate mechanism that controls the amplitude and frequency of the DWOs. The hunt for the mechanism along-with the control of the multifaceted ill-effects of the DWOs has motivated several researchers to perform multiple numerical [4, 6–8] and experimental studies [9–13]. In particular, the experimental studies on DWOs were focused on identifying the linear instability thresholds. It should be noted that the linear stability threshold is only valid for small perturbation in the system. However, with considerably large perturbation, similar systems can show multiple strange behaviors as evident from the numerical investigations [14–21]. Since in real operating situations, one does not have much control on the amount of perturbation, the linear stability threshold does not provide the complete stability characteristics of the system. In particular, the studies by Paul and Singh [22] suggest that at some operating conditions (namely subcritical Hopf bifurcation region), the system can exhibit growing oscillations after the exposure to a large perturbation in the linearly stable region. The above-mentioned numerical investiga-

Does the Criteria of Instability Thresholds During Density Wave …

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tions raise a conflict to the conventional definition of stable and unstable operating conditions in real-world applications. Hence, in this study, an attempt is made to experimentally investigate the instability thresholds by using the concept of limit cycle oscillations across the linear stability boundaries.

2 Experimental Setup and Procedure The experimental facility is a closed loop (Fig. 1) consisting the segments namely a main tank, a pump, a conditioner, a heated test section, a visualization glass, an adiabatic test section, and a condenser. A magnetically coupled gear pump is used to drive the working fluid (R134a) through the loop. The main tank is used to control the working pressure of the system at the saturation conditions. The inlet temperature of the fluid is adjusted with the help of the pre-heater that is a shell and tube type heat exchanger with glycol in the shell side. At the inlet of the heated section, a Coriolis flow meter is installed to measure the flow rate of the fluid entering the heated section. Ten thermocouples and seven pressure taps are distributed along the length of the test section. All the variables are logged using a National Instruments NI RIO data acquisition system and were acquired at a frequency of 10 Hz. The test section is a stainless steel tube of length 2035 mm with 5 mm I.D and 8 mm O.D. A manually operated valve (K i = 10.65) before the test section and an orifice plate at the exit (K e = 2.63) are installed to control the flow which are known to be the important tools to trigger and control the DWOs.

Fig. 1 Sketch of the test facility

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Table 1 Table of experimental conditions Pressure 650 Mass flux Channel orientation Channel length Channel diameter Inlet loss coefficient Exit loss coefficient

300 Horizontal 2035 5 10.65 2.63

(kPa) (kg/m2 s) – (mm) (mm) – –

2.1 Experimental Method For all the experiments, the inlet pressure of the fluid was kept constant at 650 kPa. Before recording the data for each point, extreme care was taken to assure that steadystate conditions were established. Steady-state conditions were declared when the variation in the time-averaged values of both the mass flux (300 kg/m2 s) and pressure varied less than 6% for about 200 s. In this experiment, for a given inlet subcooling, the power was increased in small steps till sustained flow oscillations are observed. It is necessary to allow enough time between successive increments in order to observe the true nature of the flow. The average amplitude of the flow variation at each applied power is recorder over a time interval of 200 s. The experimental conditions are shown in Table 1.

3 Results and Discussions In the first step of the experiments, the linear instability thresholds are found using the conventional technique. The typical approach to identify the instability thresholds is as follows: 1. Fixing all the operating parameters (namely operating pressure, inlet–exit loss coefficients, and flow rate), at a fixed inlet subcooling, the applied power is increased in small steps and the flow behavior is observed (Fig. 2). It is found very difficult to pinpoint the threshold of the instability at a certain power by visual observation of the flow behavior. This is resolved by plotting the average amplitude of the flow at different applied heat fluxes for a given inlet subcooling (say Nsub = 2.93) (Fig. 3). 2. In a typical power versus amplitude plot Fig. 3, two distinct variations in the oscillation amplitudes can be observed. These two distinct variations are fitted with two linear curves (shown by black dotted lines), and the point at which the two curves cross each other is noted as the instability threshold.

Does the Criteria of Instability Thresholds During Density Wave … i

sub

= 22.4K

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G=302[kg/m s] P =653[kPa] q"=38[kW/m ] T

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G=302[kg/m s] P i=646[kPa] q"=29[kW/m ] T sub = 22.4K

2

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Gi [kg/m 2 s]

Gi [kg/m 2 s]

G=302[kg/m2 s] P =646[kPa] q"=29[kW/m2 ] T 400

49

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G=298[kg/m s] P i=653[kPa] q"=35[kW/m ] T sub = 22.5K

320 300 280

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(b)

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Time [s]

(a) Fig. 2 a Oscillations viewed with same axes range. b Oscillations with zoomed view E D

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Fig. 3 Average amplitude of the flow oscillations with applied power

100 Linear stability threshold 50 A

B

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0 30

32

34

36

38

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Npch

Similar experiments are carried out to observe the flow behavior for six different inlet subcoolings at different powers. A total 38 operating conditions are considered which are shown in a non-dimensional parameter plane of Npch − Nsub (Fig. 4a). Following the same procedure as described before, for all inlet subcoolings, the instability thresholds are plotted in Npch − Nsub plane (Fig. 4b).

4 Experimental Nonlinear Stability Behavior Following the rich literature on the numerical study of nonlinear stability behavior of the DWOs, further investigations are done to identify various limit cycle oscillations and characterizing the bifurcation phenomena. To do so, the flow behavior at each applied power is studied for both low and high perturbation on a fixed inlet subcooling

S. Paul et al. 8

8

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Nsub

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2 25

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P=650[kPa], G=300[kg/m 2 s]

Stable

35

Unstable

40

45

Npch

Npch

(a)

(b)

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55

Fig. 4 a Operating points at which the flow behavior is observed. b Stability map in the Npch − Nsub plane showing the linear instability thresholds

Linear stability threshold Unstable limit cycle

2.9 A

B

C

32.0 35.5 38.8

Stable side

D E 40.1 40.3

Unstable side

Fig. 5 Subcritical Hopf

(Nsub = 2.93). One such behavior is explained in Figs. 5, 6, and 7. In this study, low perturbation is considered by applying no external perturbation to the flow, whereas high perturbation is applied by increasing and decreasing the pump speed suddenly, thus mimicking a sudden change in the flow rate. It is observed from Figs. 6 and 7 that the flow returns to the stable state after exposing to large perturbations at points A and B. The flow at point C is stable with no external perturbation applied. However, when a large perturbation is applied at

20

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P i=651[kPa] q"=31.8[kW/m 2 ] T s ub = 10.0K 600 400 200 0

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G i [kg/m 2 s]

G i [kg/m 2 s]

Does the Criteria of Instability Thresholds During Density Wave …

1000

51

P i=654[kPa] q"=28.7[kW/m 2 ] T s u b = 10.2K

500 0 0

10 20 30 40 50 60 P i=653[kPa] q"=31.8[kW/m 2 ] T s u b = 9.9K

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500 0

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] T s ub = 10.0K

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G i [kg/m 2 s]

G i [kg/m 2 s]

G i [kg/m 2 s]

Fig. 6 Case of subcritical Hopf bifurcation: flow behavior with low (left side) and high perturbations (right side) P i=654[kPa] q"=28.7[kW/m 2 ] T s u b = 10.2K 500 0

0

0.15

0.2

P i=653[kPa] q"=36.6[kW/m

0.25 2

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G i [kg/m 2 s] G i [kg/m 2 s] G i [kg/m 2 s]

500

0.2

0.25

500 0

2

P i=652[kPa] q"=35[kW/m ] T s u b = 10.0K

0.15

P i=653[kPa] q"=31.8[kW/m 2 ] T s u b = 9.9K

0.15 0.2 0.25 P i=653[kPa] q"=35[kW/m 2 ] T s u b = 10.0K

500 0

0.15

0.2

0.25

] T s u b = 10.0K

500 0

0.15

0.2

0.25

P i=655[kPa] q"=38.1[kW/m 2 ] T s u b = 10.1K 500 0

0.15

0.2

0.25

Fig. 7 Evolution of the flow variables on the phase plane of pressure drop versus mass flux (ΔP − Gi )

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the point C, the flow does not return to stable state, instead it approaches a large amplitude stable limit cycle. The flow at points D and E is unstable even without any perturbation. This phenomenon can be attributed by the existence of an unstable limit cycle (marked by red dashed line in Fig. 5) at the point C which repels the trajectories toward the large amplitude stable limit cycle (marked by the green solid lines in Fig. 5). Hence, instead of damped oscillations, a constant amplitude oscillation is observed. These characteristics of the flow at this inlet subcooling (Nsub = 2.93) provide sufficient evidence of the occurrence of the subcritical Hopf bifurcation. It is also observed that these characteristics of the flow appear at very narrow region (Fig. 5) between the stable and the unstable side of the stability map and is called as meta-stable region. In addition, the above-mentioned evidence suggests that the operating points A and B are globally stable where the system is stable for any amount of perturbation.

5 Proposed Method to Detect Nonlinear Stability Boundary It should be noted that the linear instability threshold is detected by using the conventional technique by plotting the average amplitude of oscillations with different powers as shown in Fig. 3. The instability threshold thus obtained is only valid for small perturbation in the system. However, with significantly large perturbation, the system can be unstable even before this threshold is shown by the point D in Fig. 6. Since in the real-world applications, one does not have much control on the amount of perturbation, the instability threshold predicted by the conventional technique does not hold good to predict the overall system behavior, and hence the point D should be treated as the onset of instability of the system. Due to the existence of the meta-stable region between the globally stable (point A, and B) and the instability threshold (point C in Fig. 3), the authors postulate that it is improper to find the instability threshold using the approach mentioned in Sect. 3 and hence the authors proposed another approach to predict the instability threshold which can provide global instability limits of the system. The proposed approach to find the nonlinear instability threshold is as follows: • It is worth noting that the proposed method to find the nonlinear stability threshold is similar to the linear stability threshold presented in the previous section with a few additional steps. Allowing sufficient time after increasing the power in small steps to observe the flow behavior, a large perturbation should be applied to the system. Again, allowing sufficient time after applying the perturbation, the average amplitude of the flow variation should be recorded. • Allowing sufficient time after increasing the power, a large perturbation should be applied to the system. Again, allowing sufficient time after applying the perturbation, the average amplitude of the flow variation should be recorded.

Does the Criteria of Instability Thresholds During Density Wave …

3.5

C 100

Nsub

Average Amplitude

4

E D

150

53

Global stability limit

Nonlinear stability limit

3

E A

50 2.5

Globaly stable

A 32

C B Metastable region

D Unstable

B

0 30

Linear stability limit

34

36

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(a)

38

40

42

2 30

32

34

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40

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(b)

Fig. 8 a Average amplitudes with large perturbation showing global stability limit. b Visualization of the meta-stable region

• The amplitude of the flow vs applied power after exposure to a large perturbation should be plotted similar to Fig. 3. The point of intersection between the two distinct variations in the amplitudes should then be treated as the instability threshold as shown in Fig. 8a, b .

6 Conclusions In this work in addition to the usual growing and damped oscillatory behavior of the DWOs, two types of limit cycle oscillations are shown, namely stable limit cycle and unstable limit cycle. The subcritical Hopf bifurcation is observed to appear across the stability threshold. In addition, due to the appearance of the subcritical Hopf bifurcation, a narrow region of meta-stable characteristics is found between the stable and the unstable region. Inside the meta-stable region, dual nature of the system (both stable and unstable) is observed which is primarily determined by the amount of perturbation. Thus, due to the existence of a meta-stable region, a method is proposed to identify the nonlinear stability thresholds. Acknowledgements Funding for this work from the Research Council of Norway under the FRINATEK project 275652 is gratefully acknowledged. The authors also gratefully acknowledge the European Unions Horizon 2020 research and innovation programme to receive funding from the Marie Skodowska-Curie Actions Individual Fellowship grant (Dr. Subhanker Paul) for the project HisTORIC (No 789476).

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References 1. Boure, J., Bergles, A., Tong, L.: Review of two-phase flow instability. Nucl. Eng. Des. 25(2), 165–192 (1973) 2. Kakac, S., Bon, B.: A review of two-phase flow dynamic instabilities in tube boiling systems. Int. J. Heat Mass Transf. 51(02), 399–433 (2008) 3. Stenning, A.H.: Instabilities in the flow of a boiling liquid. J. Basic Eng. 86(2), 213–217 (1964) 4. Rizwan-Uddin: On density-wave oscillations in two-phase flows. Int. J. Multiph. Flow 20(4), 721–737 (1994) 5. O’Neill, L.E., Mudawar, I.: Mechanistic model to predict frequency and amplitude of density wave oscillations in vertical upflow boiling. Int. J. Heat Mass Transf. 123, 143–171 (2018) 6. Rizwan-Uddin, Dorning, J., : Some nonlinear dynamics of a heated channel. Nucl. Eng. Des. 93(1), 1–14 (1986) 7. Liang, N., Shuangquan, S., Tian, C., Yan, Y.: Two-phase flow instabilities in horizontal straight tube evaporator. Appl. Therm. Eng. 31(2), 181–187 (2011) 8. Narayanan, S., Srinivas, B., Pushpavanam, S., Bhallamudi, S.M.: Non-linear dynamics of a two phase flow system in an evaporator: the effects of (i) a time varying pressure drop (ii) an axially varying heat flux. Nucl. Eng. Des. 178(3), 279–294 (1997) 9. Liu, H.T., Kakac, S.: An experimental investigation of thermally induced flow instabilities in a convective boiling upflow system. Wärme - und Stoffübertragung 26(6), 365–376 (1991) 10. Wang, Q., Chen, X., Kakaç, S., Ding, Y.: An experimental investigation of density-wave-type oscillations in a convective boiling upflow system. Int. J. Heat Fluid Flow 15(3), 241–246 (1994) 11. Chiapero, E.M., Doder, D., Fernandino, M., Dorao, C.: Experimental parametric study of the pressure drop characteristic curve in a horizontal boiling channel. Exp. Therm. Fluid Sci. 52, 318–327 (2014) 12. Dorao, C.A.: Effect of inlet pressure and temperature on density wave oscillations in a horizontal channel. Chem. Eng. Sci. 134, 767–773 (2015) 13. Fukuda, K., Kobori, T.: Classification of two-phase flow instability by density wave oscillation model. J. Nucl. Sci. Technol. 16(2), 95–108 (1979) 14. Dokhane, A., Rizwan-Uddin, Chawla, R.: BWR stability and bifurcation analysis using reduced order models and system codes: identification of a subcritical Hopf bifurcation using RAMONA. Ann. Nucl. Energy 34(10), 792–802 (2007) 15. Paul, D., Singh, S., Mishra, S.: Interaction of density wave oscillations and flow maldistribution for two-phase flow boiling parallel channels. Int. J. Therm. Sci. 145, 106026 (2019) 16. Paul, D., Singh, S., Mishra, S.: Impact of system pressure on the characteristics of stability boundary for a single-channel flow boiling system. Nonlinear Dyn. 96, 175–184 (2019) 17. Rahman, M.E., Singh, S.: Flow excursions and pressure drop oscillations in boiling two-phase channel. Int. J. Heat Mass Transf. 138, 647–658 (2019) 18. Rahman, M.E., Singh, S.: Non-linear stability analysis of pressure drop oscillations in a heated channel. Chem. Eng. Sci. 192, 176–186 (2018) 19. Singh, M.P., Singh, S.: Non-linear stability analysis of supercritical carbon dioxide flow in inclined heated channel. Progr. Nucl. Energy 117, 103048 (2019) 20. Singh, M.P., Paul, S., Singh, S.: Development of a novel nodalized reduced order model for stability analysis of supercritical fluid in a heated channel. Int. J. Therm. Sci. 137, 650–664 (2019) 21. Singh, M.P., Emadur, M.E., Singh, S.: Nodalized reduced ordered model for stability analysis of supercritical fluid in heated channel. In: ASME 2018 Power Conference collocated with the ASME 2018 12th International Conference on Energy Sustainability and the ASME 2018 Nuclear Forum, vol. 137, Paper No. POWER2018-7366 (2018) 22. Paul, S., Singh, S.: Linear stability analysis of flow instabilities with a nodalized reduced order model in heated channel. Int. J. Therm. Sci. 98, 312–331 (2015)

Solar Energy for Meeting Service Hot Water Demand in Hotels: Potential and Economic Feasibility in India Niranjan Rao Deevela and Tara C. Kandpal

1 Introduction Hotels are key element in travel and tourism industry. For example, in India, there has been a continued increase in the number of hotels. With a substantial demand for service hot water in these hotels, energy consumption and consequent greenhouse emissions have gradually increased substantially for the same. Harnessing solar energy for meeting the service hot water demand in hotels can contribute significantly toward reduction of fossil fuel consumption and consequent environmental emissions. Large-scale deployment of solar water heating systems (SWHS) in hotels in India would essentially depend upon (i) availability of unshaded space (usually on rooftop) for installation of SWHS, (ii) solar resource assessment, and (iii) financial feasibility of the incremental investment on the SWHS, which would directly depend upon the fraction of total annual useful thermal energy demand for water heating contributed by the SWHS. This study is an attempt to assess the suitability of SWHS in hotels in India from the above-mentioned three perspectives. The solar fraction for SWHS at 27 locations in different climatic zones of the country has been estimated. Also, roof area available with 18 hotels in India for installing solar water heating systems has been estimated. Finally, the levelized (unit) cost of useful thermal energy has been estimated for the SWHS at 27 locations along with the corresponding values for several conventional water heating systems used by hotels.

N. R. Deevela (B) · T. C. Kandpal Center for Energy Studies, Indian Institute of Technology Delhi, New Delhi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_6

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2 Service Hot Water in Star Category Hotels One of the major energy-consuming areas in hotels is service hot water generation. Currently, service hot water is generated through many ways like conventional boilers to the latest alternative options such as heat pumps, waste heat recovery-based systems, and solar water heaters [1]. Majority of the hotels are using conventional boilers to generate service hot water. These boilers are energy-intensive and generate significant carbon emissions. Hotels with piped natural gas (NG) connection are using gas boilers. As per revised guidelines by Ministry of Tourism, hotels are classified under (i) heritage (grand, classic, and heritage); (ii) star category hotels (five-star deluxe and five-star to one-star); (iii) bread and breakfast establishments; (iv) guest house; and (v) apartment hotels. By the end of January 2019, around 1117 approved classified hotels with capacity of 91,140 rooms and 343 unclassified hotels with capacity of 22,604 rooms are available in India [2]. Classified hotels are fully dominated by star category hotels [i.e., 1059 hotels (94.8%) with 89,266 rooms]. Out of star category hotels, 3-star hotels are 374 with 13,160 rooms (14.74%), 4-star hotels are 318 with 16,183 rooms (18.12%), 5-star hotels are 343 with 59,170 rooms (66.28%), and remaining 24 hotels are categorized into 2-star and 1-star hotels with 753 rooms [2]. An attempt was made to collect data available in public domain as well as through a questionnaire-based survey with selected hoteliers (3-, 4-, and 5-star category). From each hotel, data related to the number of rooms, occupancy, energy consumption, average service hot water requirement per person, mode of service hot water generation, temperature of hot water, buildup area, and rooftop area were collected. As mentioned earlier, it was found that hotels are using conventional boilers as well as heat pumps and solar water heaters for service hot water generation. The estimated service hot water requirement of four-star and above category hotels is in the range of 150–220 L/room/day, and for three-star hotels, it is in the range of 100–150 L/room/day. It is worth mentioning that the service hot water consumption among hotels may vary significantly due to climatic conditions, hotel type, and habits of end users. Typical service hot water generating approaches followed in star category hotels are listed in Table 1. Table 1 Some of the service hot water generating approaches followed in star category hotels in India Energy source

Approach(es) based on

Electricity

Heat pumps (air to water, water to water)

Fossil

fuelsa

Renewable energy

Hot water generators/boilers Solar water heaters based on FPC and ETC Biomass boilers using rice husk, fuelwood, wood chips, biomass pellets as feedstocks

Waste heat a Coal,

Waste heat recovery units (often based on HVAC system)

diesel, furnace oil, and natural gas

Solar Energy for Meeting Service Hot Water Demand in Hotels …

57

Table 2 Comparison of some service hot water generating approaches in star category hotels in India Attribute

Water heating approach based on Electric geysers

Fossil fuel boilers

Solar water heaters

Waste heat recovery water heaters

Heat pump water heaters

Biomass boilera

Storage of water

Optional

Essential

Essential

Optional

Essential

Essential

Capital cost

Low

Medium

High

Medium

Very high

Medium

O&M cost

High

High

Low

Medium

Low

High

Useful life (years)

10–15

10

20

10–15

10–15

10

Commercialization status in India

Very good

Very good

Very good

Moderate

Moderate

Very Good

Efficiency (%)/CoP 90–95

70–85

40–60

60–70

3.0–4.5a

60–75

Greenhouse gas emissions

Medium to Very low high

Very low

Medium

Very low

a COP

High

of the heat pump

Most of the hotels in 3–5-star category hotels are using boilers based on fossil fuels such as diesel, furnace oil, and natural gas. Very few hotels are using biomass-based gasifiers (most of them located in the states of Kerala and Karnataka). Some hotels are using SWHS and electric heat pumps as a supplement to the main boiler. Hotels with SWHS use boilers as backup during peak load and cloudy/rainy days. Service hot water is usually supplied for end use between 45 and 50 °C and stored between 50 and 60 °C. Hotels with conventional boilers only supply service hot water at 45 °C. Based on the information compiled from the result of the questionnaire-based survey, a generic comparison of various service hot water generating approaches being used by star category hotels is presented in Table 2.

3 Methodology In view of the substantial climate variations across the country, 27 locations across five climatic zones were selected. While the annual average ambient temperature at these locations varies from 15 to 29 °C, the annual average daily value of Global Horizontal Irradiance (GHI) varies between 4.86 and 5.82 kWh/m2 . In order to assess the availability of adequate roof area for installation of SWHS, the utilizable roof area for 18 hotels at 6 locations (Delhi, Jaipur, Udaipur, Chennai, Bangalore, and Hyderabad) was estimated with the help of Google EarthTM version 7.3.2. Also, the required solar collector area was estimated using RETScreen4® software for the service hot water demand estimated on the basis of the findings of the questionnaire-based survey.

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N. R. Deevela and T. C. Kandpal

The levelized (unit) cost of useful thermal energy delivered by different service hot water generating approaches in hotels has been estimated with due consideration of the capital cost, cost of operation and maintenance, cost of finance, expected useful life of the system as well as the expected annual useful thermal energy delivered. Finally, the values of several measures used for assessing the financial attractiveness of incremental investments in SWHS (discounted payback period, net present values, and internal rate of return) were estimated using standard formulae of engineering economics. The formulae used for estimation of the levelized (unit) cost of useful thermal energy and other measures of financial performance are presented in Appendix 1. The values of various input parameters used in the estimation of potential and financial feasibility of FPC-based and ETC-based SWHS in hotels in India are listed in Table 6 in Appendix 2.

4 Results and Discussion Using the methodology outlined in Sect. 3, calculations have been made to estimate (i) the fraction of service hot water demand that can be met through solar energy; (ii) available roof area with some hotels; (iii) the levelized (unit) cost of useful thermal energy delivered by some of the conventional hot water generating approaches; and (iv) the values of discounted payback period, net present values, and internal rate of return for an incremental investment on SWHS. The collectors used in the SWHS are assumed to be tilted at an angle equal to the latitude of the location and facing south. Based on the results obtained from RETScreen4® software, the collector area required for the locations considered in the study is varying in the range from 258 to 407 m2 for systems based on FPC and from 237 to 373 m2 for systems based on ETC with average solar fraction of 0.55 and 0.65, respectively, for 30,000 lpd system. The values obtained for 27 locations are summarized in Table 4 in Appendix 2. With the help of Google EarthTM version 7.3.2., 18 hotels’ roof area was calculated and it is varying from 578 to 6985 m2 . However, complete available roof area cannot be used for installation of solar water heating systems due to several factors such as shading (approximately accounts in the range of 0.16–0.3 of the roof area) due to the neighboring structures, walls, trees [3–5], and installation of utilities like generators, cooling towers, air conditioning systems, and water tanks. Sample hotel building screenshots are presented in Fig. 1. In this study, as a conservative estimate, a value of 0.3 [6–8] is assumed as utilizable roof area for installation of SWHS in hotels. The details of utilizable roof area for installation of SWHS on sample hotels are presented in Table 5 in Appendix 2. From the analysis, it is observed that, in all of the hotels, utilizable roof area is sufficient to install SWHS. Estimated values of the levelized (unit) cost of useful thermal energy delivered by some of the service hot water generating approaches being followed in hotels in India at selected locations are presented in Table 3.

Solar Energy for Meeting Service Hot Water Demand in Hotels …

59

Fig. 1 Sample pictures obtained from Google EarthTM for roof area measurement

It is worth mentioning that the estimates presented in Table 3 do not consider the effect of any variation in the local price of the fuel and variation in the size/capacity of the system used for meeting service hot water demand in the hotels. It may also be noted from Table 3 that, as expected, LUCte delivered by different hot water generating approaches for meeting the service hot water demand in star category hotels in India varies considerably with the energy source-technology combination. The levelized (unit) cost of useful thermal energy delivered LUCte is much higher for diesel, LPG, furnace oil, natural gas, and other petroleum fuels as compared to the other potential options presented in Table 3. Since the estimated value of LUCte is relatively much lower for heat pump-based system, there is an increasing trend in the country toward their use. Wherever feasible, waste heat recovery-based water heating is likely to be the least cost option. The levelized (unit) cost of useful thermal energy delivered by SWHS based on FPC and ETC is found to vary between 0.85–1.05 Rs./MJ and 0.68–0.80 Rs./MJ, respectively. Measures of financial viability such as discounted payback period, net present value, and internal rate of return for an investment in SWHS at selected locations are presented in Figs. 2, 3, and 4 assuming the saving of diesel, natural gas (NG), LPG, and electricity with the installation of FPC-based and ETC-based

60

N. R. Deevela and T. C. Kandpal

Table 3 Values of input parameters used in the analysis or even in calculations Parameter

Values

Hot water requirement

4-star and above

150 L/day

3-star Service hot water outlet temperature

Parameter

Values

Capital cost of hot water generator (Rs.)

Hot water generator (1 lakh Kcal/h)

1,395,000

125 L/day

Solar water heater

200/L

60 °C

Waste heat recovery (1 lakh Kcal/h)

600,000

heat pump (6 TR/Hr)

320,000

Hot water generator (1 Lakh Kcal/h)

3–15 based on fuel

Solar water heater

4 3

Gross calorific Diesel 11,840 value of each fuel (Kcal/kg or Natural gas 12,000 kWh) Furnace oil 10,050

Cost of fuel (Rs./Kg or kWh)

Maintenance cost of hot water generator (% of capital cost)

Coal

4500

Waste heat recovery (1 lakh Kcal/h)

Biomass

3200

Heat pump (6 TR/h)

LPG

12,500

electricity

860

Diesel

67.1

Efficiency of hot water Generator (%)

Diesel

85

Natural gas

85

Furnace oil

80

Natural gas 48.75

Coal

70

Furnace oil 42

Biomass

70

Coal

4.1

LPG

85

Biomass

6

Electricity

92

LPG

60

Solar energy

44

Electricity

8

Thermal energy

70

Electricity for heat pump

460

Hot water distribution system 10% losses Annual capacity utilization factor

80%

systems. Financial viability of SWHS at two locations in cold climatic zone is also presented in Table 7 in Appendix 2. From the results of financial analysis of SWHS at different locations in India as presented in Figs. 2, 3, and 4, it appears that investment in such systems is financially attractive. For example, for the locations considered in the study, the payback periods of SWHS are found to be very small compared to their expected useful life. At all 27

Solar Energy for Meeting Service Hot Water Demand in Hotels …

61

Fig. 2 Discounted payback period of 30,000 LPD capacity FPC and ETC SWHS under substituting different fuels

Fig. 3 Net present worth of 30,000 LPD capacity FPC and ETC SWHS substituting different fuels

Fig. 4 Internal rate of return (%) of 30,000 LPD capacity FPC and ETC SWHS under substituting different fuels

locations considered in the study, ETC-based SWHS are financially more attractive than FPC-based systems.

62

N. R. Deevela and T. C. Kandpal

Appendix 1: Mathematical Expressions Used for Financial Feasibility Estimation  LUCte =

Co

d(1+d)n (1+d)n −1



+ Com

Aued

(1)

where Co the capital cost of hot water generator (includes auxiliary and installation cost), d the discount rate in fraction, n the useful life of the hot water generator, and Com the annual cost of operation and maintenance of the hot water generator. In case the hot water generation approach consumes fuel, the annual cost of operation would also include the cost of purchasing the fuel and the same can be estimated from the annual useful thermal energy delivered, the calorific value of the fuel used, the efficiency of fuel utilization in the hot water generator, the unit price of fuel used, and Aued represents the annual amount of useful energy delivered. Tdp =

ln(B − Com ) − ln[(B − Com ) − dCo ] ln(1 + d)

(2)

where B is annual monetary benefit accrued as a result of fuel savings achieved with use of solar water heating systems. It is assumed that B is constant throughout the life of the solar water heating systems.   E aq Fup B= (Fcv )(ηb ) 

(3)

where E aq the annual quantity of useful energy delivered, Fup the unit price of the fuel replaced, Fcv the calorific value of the fuel replaced, and ηb the efficiency of existing hot water generator.  NPV = (B − Com )

 (1 + d)n − 1 − Co d(1 + d)n

(4)

Internal rate of return (IRR) is defined as the discount rate at which the net present value (NPV) of the investment is zero. The value of IRR can be estimated from the following equation: 

 (1 + IRR)n − 1 − Co = 0 (B − Com ) IRR(1 + IRR)n

(5)

Solar Energy for Meeting Service Hot Water Demand in Hotels …

Appendix 2: Results See Tables 4, 5, 6, and 7.

63

Vijayawada

Visakhapatnam

Goa

Kolkata

Mumbai

Pune

Guwahati

Agra

10

11

12

13

14

15

16

17

Lucknow

Thiruvananthapuram 0.55

9

Composite

Cochin

8

0.55

0.56

0.56

0.56

0.54

0.53

0.56

0.56

0.53

0.53

0.52

Coimbatore

7

0.55

0.52

0.55

0.56

0.57

0.59

Chennai

18

Evacuated tubular collector

311

322

329

288

278

299

271

278

283

281

285

285

265

294

269

283

299

267

530

541

542

541

475

482

496

487

466

491

500

474

465

473

488

497

531

526

0.65

0.66

0.66

0.65

0.63

0.64

0.66

0.66

0.63

0.65

0.62

0.61

0.65

0.63

0.65

0.66

0.66

0.68

283

294

301

262

253

274

248

253

258

258

260

260

242

269

246

258

271

244

624

639

643

628

561

576

582

573

554

581

587

562

550

565

573

585

621

611

(continued)

Solar fraction Solar collector Annual useful Solar fraction Solar collector Annual useful area (m2 ) thermal energy area (m2 ) thermal energy delivered (GJ) delivered (GJ)

Flat plate collector

Solar water heating systems using

6

Warm and humid Bhubaneswar

Vadodara

4

5

Surat

3

Jodhpur

Udaipur

Hot and dry

1

Location

2

Climatic zone

S. No.

Table 4 Performance of 30,000 LPD capacity SWHS at 27 locations across five climatic zones in India

64 N. R. Deevela and T. C. Kandpal

Jaipur

Hyderabad

Khajuraho

New Delhi

Bangalore

Temperate

Cold

21

22

23

24

25

26

Shimla

Mussoorie

Ludhiana

27

Moradabad

Location

20

Climatic zone

19

S. No.

Table 4 (continued) Evacuated tubular collector

0.58

0.59

0.52

0.57

0.54

0.51

0.59

0.59

0.57

407

386

283

294

304

283

258

322

313

828

780

505

544

517

468

550

603

575

0.65

0.66

0.61

0.67

0.64

0.61

0.67

0.69

0.67

373

352

260

269

278

258

237

294

285

931

879

594

635

612

555

634

700

668

Solar fraction Solar collector Annual useful Solar fraction Solar collector Annual useful area (m2 ) thermal energy area (m2 ) thermal energy delivered (GJ) delivered (GJ)

Flat plate collector

Solar water heating systems using

Solar Energy for Meeting Service Hot Water Demand in Hotels … 65

66

N. R. Deevela and T. C. Kandpal

Table 5 Solar collector area required and utilizable roof area available with few hotels for installation of solar water heating system Start category

Number of rooms

Location

Hot water demand (lpd)

Solar collector area required (m2 ) FPC

3

87

4

141

4

72

4

58

5

Utilizable roof area(m2 )

ETC

Hyderabad

10,875

103

93

383

Jaipur

21,150

182

167

173

10,800

93

85

431

8700

85

78

211

119

17,850

175

160

376

5

523

78,450

770

704

2096

5

261

39,150

384

351

982

5

216

32,400

318

291

779

5

403

60,450

593

542

924

5

250

37,500

368

336

924

5

87

Jaipur

13,050

112

103

861

5

211

31,650

272

250

1245

5

141

Udaipur

21,150

211

191

1519

5

171

Chennai

25,650

227

207

446

4

85

12,750

113

103

398

3

42

5250

46

42

239

3

104

13,000

123

117

362

5

115

17,250

163

155

256

New Delhi

Bangalore

Table 6 Estimated value of levelized (unit) cost of useful thermal energy for different energy resource technologies for a 30,000 lpd system Energy source

Technology

Levelized (unit) cost of useful thermal energy (Rs./MJ) Delhi

Diesel

Srinagar

Bangalore

Udaipur

1.741

1.792

1.744

1.772

1.767

1.473

1.516

1.475

1.498

1.494

Furnace oil

1.448

1.517

1.452

1.489

1.483

Natural gas

1.265

1.308

1.268

1.291

1.287

Coal

0.475

0.514

0.479

0.493

0.488

LPG

Boiler (hot water generator)

Chennai

Electricity

Heat pump

0.606

0.608

0.609

0.595

0.592

Electricity and waste heat

Hybrid heat pump

0.298

0.415

0.305

0.367

0.357

Waste heat at 120°C

Waste heat recovery

0.062

0.063

0.064

0.057

0.055

5.77

4.30

Discounted payback period (years)

Net present worth (million Rs.)

Mussoorie

1.92

9.27

3.26

6.89

10.35

2.99

Electricity (FPC)

33

5.80

3.71

33

5.47

3.71

5.47

3.71

ETC LPG (FPC)

41

11.03

2.96

40

10.35

2.99

Diesel (ETC)

NG (FPC)

20

3.51

6.81

20

3.26

6.89

Diesel (FPC)

16

2.08

9.14

16

1.92

9.27

FPC

23

Internal rate of return (%)

Parameter

4.61

Net present worth (million Rs.)

Location

5.71

23

Internal rate of return (%)

Discounted payback period (years)

4.30

Net present worth (million Rs.)

Shimla

5.77

Discounted payback period (years)

Mussoorie

ETC Electricity (FPC)

Diesel (ETC)

LPG (FPC)

Diesel (FPC)

NG (FPC)

FPC

Parameter

Location

Table 7 Values of financial parameters of two different locations in cold climatic zone

3.26

5.46

NG (ETC)

19

1.68

7.04

24

3.26

5.46

NG (ETC)

4.69

4.31

LPG (ETC)

24

2.67

5.42

29

4.69

4.31

LPG (ETC)

12.24

2.05

(continued)

Electricity (ETC)

47

7.88

2.48

56

12.24

2.05

Electricity (ETC)

Solar Energy for Meeting Service Hot Water Demand in Hotels … 67

Shimla

Location

5.71

4.61

23

Net present worth (million Rs.)

Internal rate of return (%)

23

16

2.08

9.14

16

20

3.51

6.81

20

41

11.03

2.96

40

33

5.80

3.71

33

ETC Electricity (FPC)

Diesel (ETC)

LPG (FPC)

Diesel (FPC)

NG (FPC)

FPC

Discounted payback period (years)

Internal rate of return (%)

Parameter

Table 7 (continued)

19

1.68

7.04

24

NG (ETC)

24

2.67

5.42

29

LPG (ETC)

47

7.88

2.48

56

Electricity (ETC)

68 N. R. Deevela and T. C. Kandpal

Solar Energy for Meeting Service Hot Water Demand in Hotels …

69

References 1. Wang, W., Guo, P., Zhang, H., Yang, W., Mei, S.: Comprehensive review on the development of SAHP for domestic hot water. Renew. Sustain. Energy Rev. 72, 871–881 (2017) 2. GOI, Government of India: Ministry of Tourism, Classification, approval and occupancy of hotels, New Delhi (2019). https://hotelcloud.nic.in/MOT/AllindiaRpt.aspx. Last accessed 2 Feb 2019 3. Khanna, R.K., Rathore, R.S., Sharma, C.: Solar still an appropriate technology for potable water need of remote villages of desert state of India -Rajasthan. Desalination 220, 645–653 (2008) 4. Nguyen, H.T., Pearce, J.M.: Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Sol. Energy 86, 1245–1260 (2012) 5. Izquierdo, S., Rodrigues, M., Fueyo, N.: A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Sol. Energy 82, 929–939 (2008) 6. Pillai, I.R., Rangan Banerjee, R.: Methodology for estimation of potential for solar water heating in a target area. Sol. Energy 81, 162–172 (2007) 7. Singh, R., Banerjee, R.: Estimation of Roof-top Photovoltaic Potential Using Satellite Imagery and GIS. IEEE (2013). 978-1-4799-3299-3/13 8. Singh, R., Banerjee, R.: Estimation of rooftop solar photovoltaic potential of a city. Sol. Energy 115, 589–602 (2015)

Techno-economic Feasibility of Condenser Cooling Options for Solar Thermal Power Plants in India Tarun Kumar Aseri , Chandan Sharma , and Tara C. Kandpal

1 Introduction Increasing climate change concerns and uncertainty about the price and availability of fossil fuels have generated significant interest in renewable energy-based electricity generation options [1]. Solar power generation is being promoted across the globe as an environmentally sustainable renewable energy option [2]. High annual direct normal irradiance (DNI), large land area (usually wastelands) and sufficient water availability are the primary conditions that needs to be evaluated before deployment of a solar thermal power plant [3, 4]. The first two conditions are more likely to prevail in arid regions. However, the arid regions may not always have adequate water availability due to little or negligible rainfall [5, 6]. At such locations, a solar thermal power plant with wet cooling technology may not be feasible as the same requires substantial amount of water (3.5–4.0 m3 /h per MW) for condenser cooling purpose in the power block [7]. Hence, it is imperative to explore and consider alternative condenser cooling options that are water conservative. One of the possible approaches to reduce the water requirement in solar thermal plants is to use the dry cooling technology [8], sometimes also referred to as “air-cooling system” or “air-cooled condenser” (ACC). Another water conservative condenser cooling option that partially combines the desirable features and characteristics of both wet and dry cooling is hybrid cooling technology [9]. However, the use of dry cooling or hybrid cooling technology in place of the wet cooling technology has its own implications on techno-economic performance and other relevant aspects of solar thermal power plants. In the present study, an attempt has been made T. K. Aseri (B) · T. C. Kandpal Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, India e-mail: [email protected] C. Sharma Mechanical Engineering Department, Engineering College, Ajmer, Rajasthan, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_7

71

72

T. K. Aseri et al.

Fig. 1 Schematic of a solar thermal power plant with two-tank (indirect) thermal energy storage

to assess techno-economics of a 50 MW nominal capacity parabolic trough collector (PTC) based plant with different condenser cooling technologies at four potential locations in India.

2 Solar Thermal Power Plant A schematic of a solar thermal power plant with two-tank (indirect) thermal energy storage is shown in Fig. 1. A solar thermal power plant can be divided into three systems, namely solar energy collection system, thermal energy transfer system and power generation system [10]. Solar energy collection system comprises of an array of solar collectors that are continuously tracked. The heat collected by the solar energy collection system can be either transferred to a power generation system or can be stored to generate electricity beyond sunshine hours or during periods of intermittent sunlight. Steam obtained from the thermal energy transfer system is expanded in a turbine (usually in a Rankine cycle) in power generation system. The turbine exhaust steam is condensed and converted into the water using a condenser with the help of cooling technology. In the condenser, the heat is transferred to the available cooling medium.

3 Techno-economic Feasibility Analysis of Different Condenser Cooling Technologies A techno-economic feasibility assessment of different available cooling technologies for a 50 MW PTC based solar thermal power plant (without thermal energy storage) at four potential locations in India has been undertaken. The analysis has

Techno-economic Feasibility of Condenser Cooling Options …

73

been undertaken using System Advisor Model (SAM), a software developed by a National Renewable Energy Laboratory, USA [11].

3.1 Technical and Economic Parameters The details of locations selected for the analysis, their ambient conditions and corresponding annual DNI availability are presented in Table 1. The National Solar Radiation Database (NSRDB) has been used for weather and solar irradiance data [12]. For reference, the technical data of an operational 50 MW wet-cooled PTC based solar thermal power plant (Megha solar plant) located at Anantapur, Andhra Pradesh, India (Table 2) has been considered [13]. The design parameters for wet-cooled, dry-cooled and hybrid-cooled power block are presented in Table 3. To reduce the annual water requirements by 50% in a solar thermal power plant, a technology with 50% hybridization of wet cooling and dry cooling technologies in parallel mode has also been considered. The dry cooling is relatively less effective than the wet cooling leading to a significant reduction in thermal to electric conversion efficiency. Furthermore, the dry-cooled plant delivers less net electricity due to its higher parasite requirements. To compensate this, more collector area needs to be installed for the plant with the same nominal capacity (Table 2). Table 1 Ambient conditions and annual DNI availability of the locations selected for the analysis [12] Location, State

Latitude (°N)

Longitude (°E)

Annual average dry-bulb temperature (°C)

Annual DNI (kWh/m2 )

Kutch, Gujarat

22.58

69.66

28.29

1909

Jaisalmer, Rajasthan

26.91

70.95

28.65

1883

Nashik, Maharashtra

20.00

73.79

25.55

1908

Mandsaur, Madhya Pradesh

24.09

75.25

26.50

1843

Table 2 Design parameters used in the analysis of a 50 MW PTC based solar thermal power plant Parameter

Unit

Value

Nominal capacity

MW

50

Parabolic trough collector



AlbiasaTrough AT150

Heat collection element



Siemens UVAC 2010

Heat transfer fluid



Therminol VP-1

Design value of DNI

W/m2

700

Solar collector area (for wet- and hybrid-cooled plants)

m2

366,240

Solar collector area (for dry-cooled plant)

m2

392,400

74

T. K. Aseri et al.

Table 3 Design parameters of the power blocks used in the analysis Parameter

Unit

Condenser cooling technology Wet/Hybrid

Dry 32.1

Rated power block efficiency

%

34.2

Ambient temperature at design condition



Annual average of the location

Boiler operating pressure

bar

100

100

Turbine inlet temperature

°C

373

373

Initial temperature difference at design condition

°C



18

Cooling tower range

°C

10



Cooling tower approach

°C

5



Condenser terminal temperature difference

°C

3



In the condenser cooling system, several parameters affect the performance of cooling tower considerably as they decide the operating pressure and temperature of condenser. The same is expected to affect the efficiency of power cycle. These parameters include approach temperature, temperature range, initial temperature difference and condenser terminal temperature difference [14]. The approach temperature represents the temperature difference between the circulating water at the condenser inlet (or cooling tower outlet) and the ambient temperature of the surrounding. In case of wet cooling technology, the wet-bulb temperature is the surrounding temperature and dry-bulb temperature will act as a surrounding temperature in case of dry cooling technology. The temperature range of any cooling tower is temperature gain by the circulating cooling water across the condenser. The terminal temperature difference is the temperature difference between the steam inlet temperature and outlet temperature of circulating water at the condenser. The sum of all three temperatures is known as initial temperature difference. The initial temperature difference is widely used in dry-cooled plant. A dry-cooled solar thermal plant is reportedly 4–10% [15, 16] costlier as compared to a wet-cooled plant owing to its lower power block efficiency and higher parasitic requirements. Since capital cost of a 50 MW dry-cooled PTC based plant was not available, it is assumed (on the conservative side) that dry-cooled solar thermal power plant shall be 10% costlier (INR 18.66 crore per MW) than wet-cooled solar thermal power plant (i.e., INR 16.96 crore per MW) [13, 17]. Further, in hybrid cooling, the size of the dry section is relatively smaller in comparison to plant with only dry cooling technology. Accordingly, the capital cost of the hybrid-cooled plant has been assumed as INR 17.8 crore per MW. The cost of water is not considered in the study. The annual electricity output and water requirements have been obtained from SAM. To analyse the cumulative effect of annual electricity output and capital cost of the plant with various condenser cooling technologies, the financial metric, levelized cost of electricity (LCOE) can be estimated using following expression:

Techno-economic Feasibility of Condenser Cooling Options …

LCOE =

 Capital cost ×

d(1+d)n (1+d)n −1



75

+ Annual O&M cost

Net annual electricity output

where d represents the discount rate and n the useful life of the plant. In the present study, a discount rate of 10%, a useful life of 25 years and annual operation and maintenance (O&M) cost of 2% of the capital cost have been assumed.

3.2 Results Figure 2 presents the variation in annual electricity output of PTC based plants for the three cooling technologies at the selected locations. Due to decrease in power block efficiency and increase in parasitic consumption, the plants with dry and hybrid cooling technologies generate approximately 5% and 3% less annual electricity, respectively, in comparison to the plants with the wet cooling technology. This can be attributed to the fact that efficiency of any power cycle depends on magnitude of the temperature gradient between source and sink. In a wet-cooled plant, the sink temperature is wet-bulb temperature of ambient air whereas dry-bulb temperature shall be the sink temperature in dry-cooled plant. The dry-bulb temperature is significantly higher than the wet-bulb temperature resulting in reduced temperature gradient between source and sink, and hence, a significant reduction in power cycle efficiency is observed. The monthly variation in electricity output and annual parasitic requirements for a plant with wet, dry and hybrid cooling technologies for the location of Kutch, Gujarat, is shown in Figs. 3 and 4, respectively. As expected, the monthly electricity output follows the trend of available direct normal irradiation at

Annual electricity output (GWh)

104 Wet

102

Hybrid

Dry

100 98 96 94 92 90 88 86 84 Kutch

Jaisalmer

Nashik

Mandsaur

Fig. 2 Annual electricity output of a 50 MW PTC based plant with three different condenser cooling options at the selected locations

76

T. K. Aseri et al.

Net Electricity output (GWh)

275 10.0 225

8.0 Wet Dry Hybrid DNI

6.0 4.0 2.0

175 125

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Direct Normal Irradiance (W/m²)

12.0

75

Fig. 3 Estimated values of monthly net electricity output from a PTC based solar thermal power plant with different condenser cooling technologies at Kutch, Gujarat 60%

Parasitic load (%)

Wet

Dry

Hybrid

50% 40% 30% 20% 10% 0% Boiler feed pump

Cooling system

Solar field

HTF pump

Fixed load of the plant

Fig. 4 Annual parasitic consumption in different activities for a 50 MW PTC based solar thermal power plant at Kutch, Gujarat

the location (Fig. 3). The parasitic consumption in components other than cooling technology (such as boiler feed pump, solar system and HTF pump) does not have significant effect of cooling technology used in plant. The total parasitic load per year for wet-cooled, dry-cooled and hybrid-cooled plant is estimated to be in the range of 7–8%, 12–13% and 9–10%, respectively, of total electricity delivered at four potential locations. From the result obtained, it is also observed that annual power requirements to operate condenser cooling technologies (wet/dry/hybrid) are in the range of 40–60% of the total parasitic load. A comparison of monthly water requirements for the three condenser cooling technologies along with corresponding saving (compared to wet cooling) in water requirements is presented in Table 4. It is observed that a dry-cooled plant requires 97% less water annually as compared to a wet-cooled plant. Considering the water requirements for mirror washing for dry-cooled (16,151 m3 per year for dry and hybrid plants), the overall water saving for dry-cooled and hybrid-cooled plants were observed to be 94% and 48%, respectively.

Techno-economic Feasibility of Condenser Cooling Options …

77

Table 4 Monthly water consumption and water saving for a 50 MW PTC based plant in Jaisalmer, Rajasthan Month

Monthly amount of water requirement by the power block (m3 )

Monthly saving in water requirement (%)

Wet-cooled

Wet → dry

Wet → Hybrid

677

13,427

97.5

50.9

33,490

874

16,663

97.4

50.2

46,678

1224

23,422

97.4

49.8

April

52,770

1410

26,706

97.3

49.4

May

56,924

1523

28,895

97.3

49.2

June

54,575

1462

27,675

97.3

49.3

January

27,395

February March

Dry-cooled

Hybrid-cooled

July

52,089

1380

26,246

97.4

49.6

August

39,554

1031

19,827

97.4

49.9

September

42,583

1135

21,476

97.3

49.6

October

42,555

1108

21,355

97.4

49.8

November

34,184

840

16,918

97.5

50.5

December

28,719

677

14,025

97.6

51.2

511,516

13,341

256,635

97.2

49.8

Annual water requirements

The effect of the choice of condenser cooling technology on the LCOE is shown in Fig. 5. Among the three condenser cooling options, LCOE of a dry-cooled plant is observed to be highest due to comparatively lower electricity output and higher capital cost. The LCOE of the dry-cooled plant is around 16% higher than that of a wet-cooled plant. Further, LCOE of the hybrid-cooled plant is approximately 8% higher than that of a wet-cooled plant but 7% lower than that of a dry-cooled

LCOE (INR/kWh)

14.0

Wet

Hybrid

Dry

12.0

10.0

8.0

6.0 Kutch

Jaisalmer

Nashik

Fig. 5 Effect of the choice of condenser cooling technology on LCOE

Mandsaur

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T. K. Aseri et al.

plant. For example, at Kutch, the LCOE for a wet-cooled plant is estimated as INR 10.8 per kWh, whereas the same for a dry-cooled plant is INR 12.5 per kWh and for a hybrid-cooled plant is INR 11.6 per kWh.

4 Concluding Remarks An attempt has been made to study the techno-economics feasibility of three condenser cooling options for parabolic trough collector based solar thermal power plants in India. It was observed that cost of electricity delivery with dry cooling option is expected to increase by 15.7% (from INR 10.8 per kWh for wet-cooled plant to INR 12.5 per kWh for dry-cooled plant at Kutch). However, the same also resulted in 94% saving in water requirement. The results obtained for hybrid-cooled plant shows relatively less penalty (in terms of performance and hence LCOE) than that with dry-cooled plant. It is also worth mentioning that there is reasonably high penalty (in terms of LCOE) of using dry cooling in a solar thermal power plant (as against a wet-cooled plant). However, as most of the locations suitable for solar thermal power generation are likely to be in arid areas, dry cooling would have to be adopted. This is likely to adversely affect the competitiveness of solar thermal power plants as against PV plants. Only with desirable success in cost reduction and integration of thermal storage, there could be some improvement in the competitiveness of solar thermal power generation.

References 1. Edenhofer, O., Madruga, R.P., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., et al.: Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change (2015). https://doi.org/10.1017/cbo9781139151153 2. U.S. Department of Energy (DOE): Concentrating Solar Power Commercial Application Study : Reducing Water Consumption of Concentrating Solar Power Electricity Generation. Report to Congress, Washington DC (2009) 3. Sundaray, S., Kandpal, T.C.: Preliminary feasibility evaluation of solar thermal power generation in India. Int. J. Sustain. Energy 33, 461–469 (2014). https://doi.org/10.1080/14786451. 2013.770395 4. Sharma, C., Sharma, A.K., Mullick, S.C., Kandpal, T.C.: Assessment of solar thermal power generation potential in India. Renew. Sustain. Energy Rev. 42, 902–912 (2015). https://doi.org/ 10.1016/j.rser.2014.10.059 5. Xu, X., Vignarooban, K., Xu, B., Hsu, K., Kannan, A.M.: Prospects and problems of concentrating solar power technologies for power generation in the desert regions. Renew. Sustain. Energy Rev. 53, 1106–1131 (2016). https://doi.org/10.1016/j.rser.2015.09.015 6. Aseri, T.K., Sharma, C., Kandpal, T.C.: Assessment of water availability for wet cooling at potential locations for solar thermal power generation in India. Int. J. Ambient Energy 1–16 (2018). https://doi.org/10.1080/01430750.2018.1507926

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7. CEA: Report on Minimisation of Water Requirement in Coal Based Thermal Power Stations. Central Electricity Authority, New Delhi, India, pp. 1–52 (2012). Homepage, http://www.cea. nic.in/reports/others/thermal/tetd/min_ofwater_coal_power.pdf. Last accessed 4 Mar 2018 8. Wagner, M.J., Kutscher, C.: The impact of hybrid wet/dry cooling on concentrating solar power plant performance. In: Proceedings of 4th International Conference on Energy Sustainability. ES2010, Arizona, USA, pp. 1–8 (2010) 9. Hu, H., Li, Z., Jiang, Y., Du, X.: Thermodynamic characteristics of thermal power plant with hybrid (dry/wet) cooling system. Energy 147, 729–741 (2018). https://doi.org/10.1016/j.ene rgy.2018.01.074 10. Praveen, R.P., Baseer, M.A., Awan, A.B., Zubair, M.: Performance analysis and optimization of a parabolic trough solar power plant in the Middle East region. Energies 11(4), 741 (2018). https://doi.org/10.3390/en11040741 11. SAM-2018. System Advisor Model. Version, 2018.11.11. National Renewable Energy Laboratory, Alliance for Sustainable Energy, LLC for Department of Energy, USA (2018). Homepage https://sam.nrel.gov/download. Last accessed 25 Dec 2018 12. NREL-NSRDB. The National Solar Radiation Database (NSRDB). National Renewable Energy Laboratory, USA (2018). Homepage https://nsrdb.nrel.gov/. Last accessed 21 Nov 2018 13. SolarPACES: Concentrating Solar Power Projects. National Renewable Energy Laboratory (2018). Homepage https://solarpaces.nrel.gov/. Last accessed 12 July 2018 14. EPRI: Comparison of Alternate Cooling Technologies for U.S. Power Plants: Economic, Environmental, and Other Tradeoffs. Electric Power Research Institute, California, USA (2002) 15. Poullikkas, A., Hadjipaschalis, I., Kourtis, G.: A comparative overview of wet and dry cooling systems for Rankine cycle based CSP plants. Trends Heat Mass Transf. 13, 27–50 (2013) 16. Turchi, C.: Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM), TP550-47605. National Renewable Energy Laboratory, pp. 1–112 (2010). www.nrel. gov/docs/fy10osti/47605.pdf 17. UNFCCC-CDM: Project Design Document Form (CDM PDD): Solar Thermal Power Plant by Godawari Green Energy Limited, Project 7379, pp. 1–8 (2012). https://cdm.unfccc.int/Pro jects/DB/KBS_Cert1348206450.84/view. Last accessed 20 Apr 2018

Optical Modeling of Parabolic Trough Solar Collector Anish Malan and K. Ravi Kumar

1 Introduction Parabolic tough solar collector (PTSC) is one of the most proven commercially available concentrated solar collectors to harness the energy from sun [1]. It is a line focus collector consists of a reflector, receiver and tracing system. Reflector concentrates the solar radiation on the absorber tube placed at the focal axis of the collector. The levelised cost of electricity (LCoE) with commercially available PTSC is ~0.20 e/kWh [2]. The LCoE is not attractive as compared to other conventional and renewable energy power generation systems and there is need to mitigate the capital cost to make it more economical. IRENA, 2012 addresses the avenues to decrease the cost of the PTSC [3]. It is reported that the cost of solar field which is nearly 52% of total capital cost of the plant can be reduced by diminishing the cost of different segments of the PTSC field like support structure, foundation, reflectors and receivers. It is expected that the LCoE may be reduced to 0.13 e/kWh by 2020 [4]. The performance of the PTSC is evaluated mainly based on three aspects, i.e., optical, thermal and structural analysis. Out of three aspects, optical analysis is the most important among them because the result of the optical analysis is used as an input for the thermal analysis of the PTSC. From engineering point of view, the measurement of flux in real-time condition is very complex, so mainly the analytical method is used for the optical analysis of the PTSC [5]. The researches have considered various approaches to study the flux distribution on the absorber surface of the PTSC such as analytical formulation [6, 7], flux mapping [8], photogrammetry [9, 10] and ray tracing techniques like MCRT [11–18], finite volume method [19, 20], inverse MCRT [18], and reverse ray tracing [21]. The optical software has also been used by the researchers such as Zemax [22] and SolTrace [23, 24], etc. Most of the A. Malan · K. R. Kumar (B) Indian Institute of Technology Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_8

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studies for the flux distribution are performed by considering sun as a uniform source but in real-time condition, the intensity of the incoming solar radiation is greater at center and falls toward the limbs. This effect is also known as limb darkening effect. Jose provided the analytical formulation for the flux distribution in the incoming solar cone including limb darkening effect [25]. The optical analysis of the PTSC including limb darkening effect provides more realistic results. To reduce the computation time, the complete PTSC problem is converted into two-dimensional model. It helps to provide the results with same accuracy in short time period. In this study, the focus is to provide the flux distribution including limb darkening effect. For this study, a model is developed for the flux distribution on the absorber surface of a PTSC using MATLAB [26].

2 Methodology The schematic of ideal PTSC highlighting the important parameters like aperture, rim angle, focal length, receiver and the incoming and reflected solar radiation with sun subtended angle of 32 is shown in Fig. 1. Receiver consists of the absorber tube and glass cover placed at the focal axis of the PTSC. Glass cover is provided to reduce the convective and radiative losses from the absorber surface. The methodology used in this work is based on certain assumption such as (a) the parabola is perfect and continuous, (b) effect of the glass cover is not considered, (c) analysis is carried out at the middle section of the PTSC, and (d) the tracking system is perfect and continuous. The algorithm used for the flux distribution analysis is shown in Fig. 2. The program is divided into two parts based on the rays reached the absorber surface:

32ʹ

32ʹ

Receiver

Rim angle

Aperture

Focal length

Fig. 1 A schematic of the parabolic trough solar collector

Optical Modeling of Parabolic Trough Solar Collector Fig. 2 Flow chart of the algorithm used for the flux distribution analysis

83

Start Definition of geometry Sun source model Initialization photon distribution

Y

Shadowed by absorber?

N Reach concentrator surface Reflected from concentrator Conical scattering of ray into child rays Energy distribution into child rays Child rays hit on absorber surface

Rays absorbed on absorber Identification of Rays hitting position Count photon distribution

End

i. Rays that directly reach the absorber surface ii. Rays that first reflected form the concentrator and then reach the absorber surface. The rays are differentiated based on the size of the absorber surface such as if the ray originated point is within the size of the absorber, then it directly absorbed on the absorber surface and if not, then it will be absorbed after reflected from concentrator.

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Ro Perpendicular plane Fig. 3 Schematics of the intersection of the reflected ray on a perpendicular plane

The limb darkening effect is considered in the incoming radiation from the sun using the below formulation [24]:  Ro + 1.5641 Ro2 − r 2 Io I = 2.5641Ro Io =

Energy of the incoming ray 2.5027 × Ro2

(1) (2)

where I is the intensity, I o is the intensity at the center of the image, Ro is image radius of the solar radiation on the perpendicular plane and r is arbitrary radius of the reflected radiation image. The ray is divided in the number of child rays forming the complete ray with each ray at the edge of the arbitrary radius. The schematics of the intersection of reflected solar cone are shown in Fig. 3. The energy distribution for various radial diameters is calculated by selecting the circular ring of interest. Energy in outmost circular element (r = Ro ): Energy =

  1 Io × π × R02 − r12 2.5641

(3)

The energy in the preceding circular element:

Energy =

 Ro + 1.5641 R02 − r 2 2.5641R0

  Io × π × r12 − r22

(4)

where r 1 , r 2 , … is the internal radii of the circular image. The above formulation can only be used if all reflected ray falls on one perpendicular plane but in case of the circular receiver, the perpendicular plane for each child ray is different. Therefore, for each child ray, a different perpendicular plane has to be considered as shown in Fig. 4 (for representation, only two planes are considered).

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Fig. 4 Schematics of projection of child rays on perpendicular planes

For the representation point of view, only ten child rays have been considered but in actual model, 200 numbers of child rays are used. If plane 1 is considered the point of interest for this particular plane is C 1 , energy is calculated for the elemental ring between C 1 and C 2 and provided to the region between C 1 and C 2  . Similarly, for plane 2, R o is image radius of the solar radiation and point of interest is C 2  , energy is calculated for the elemental ring between C 2  and C 3  and so on. Once the energy distribution for one parent ray is known, then it is converted to the flux/LCR, and at the end of the simulation, it is summed to get the distribution of LCR on the absorber surface.

3 Results and Discussion The results are illustrated in terms of local concentration ratio (LCR). It is defined as ratio of the solar flux at a point on the absorber to the solar flux incident on the aperture of the PTSC. The developed model is compared with the results provided in the Jeter model [6] for 4.5 mrad sun shape, 90° rim angle, 5.77 m aperture and 100% intercept factor as shown in Fig. 5. The Jeter [6] presented results by considering sun as a uniform source. Hence, for validation point of view, results are compared for the same. There is significant difference in LCR if sun is considered including

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A. Malan and K. R. Kumar 160

Jeter [6]

140

Present model

120 LCR

100 0⁰

80 60

270⁰

90⁰

40

180⁰

20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

320

360

Fig. 5 Comparison of the LCR of present model with Jeter model [6]

limb darkening effect. Hence, sun including limb darkening results should be used for the optical analysis of the PTSC model. All the subsequent results are obtained considering sun with limb darkening effect. The effect of change of aperture width on the LCR for same geometrical concentration ratio (GCR) is shown in Fig. 6. GCR is defined as the ratio aperture area to the absorber area (π * absorber diameter * length of the collector). The results are obtained for 26.23 GCR (which is in the case of the commercially available euro trough collector), 4.5 mrad sun shape, 80° rim angle and 100% intercept factor. There is no effect in the LCR for same GCR on the lower half of the absorber but for small aperture, little variation in there in upper half of the absorber due to few rays falls directly on the absorber. The effect of rim angle () on the LCR for 5.77 m aperture, 100% intercept factor and 4.5 mrad sun shape is shown in Fig. 7. The rim angle is varied from 70° to 120° in step size of 10°. The LCR is more concentrated in lower half of the receiver for Aperture = 2 m

60

Aperture = 5 m

LCR

50

Aperture = 5.77 m

40

Aperture = 7.5 m

30

Aperture = 10 m

20 10 0 0

40

80

120 160 200 240 280 Circumference of the absorber (deg)

Fig. 6 Effect of variation of aperture width on the LCR for same GCR

320

360

Optical Modeling of Parabolic Trough Solar Collector 180

Ψ = 70°

160

Ψ = 80°

140

Ψ = 90°

120 LCR

87

Ψ = 100°

100

Ψ = 110°

80

Ψ = 120°

60 40 20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

320

360

Fig. 7 Effect of change of rim angle on the LCR

lower rim angle and unformitivity improves toward the larger rim angle. But with increase of the rim angle, the surface area of the concentrator also increases that results in more material cost and structural load. Errors in the manufacturing of the PTSC play significant effect in the flux distribution as shown in Fig. 8. The error can be in form of slope error, tracking errors, misalignment error, etc. The effect of the error is considered in widening of the reflected solar radiation cone itself by adding it into the half of the sun subtended angle, i.e., 4.5 mrad. The results are obtained for the increasing the error from 0 to 8 mrad in step size of 2 mrad. The LCR become more uniform with increase in errors, but these errors are not intentionally introduced in the manufacturing. These errors are unavoidable due to the lack of the robustness in the manufacturing. The results are obtained for 5.77 m aperture, 80° rim angle and 100% intercept factor. The error influences the absorber size of the PTSC; to have a better intercept factor, the receiver size has to be made in accordance with the size of the reflected 160 Error = 0 mrad Error = 2 mrad Error = 4 mrad Error = 6 mrad Error = 8 mrad

140 120 LCR

100 80 60 40 20 0 0

40

80 120 160 200 240 280 Circumference of the absorber (deg)

Fig. 8 Effect of error in the manufacturing on the LCR

320

360

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solar radiation. However, with lager diameter of the absorber, the thermal heat losses also increase which decreases the overall performance of the PTSC.

4 Conclusions The work focuses on the flux distribution of the PTSC by considering limb darkening effect in the solar radiation. The two-dimensional model is developed to decrease the computation time of the CPU without compromising with the accuracy in the results. If the GCR is same, there is no effect in the LCR irrespective of the aperture size. The consideration of the limb darkening effect in the sun shape provides more realistic results. There is significant difference in the LCR if sun is considered as a uniform source and including limb darkening effect. The LCR distribution become more uniform with increase of the rim angle but also the receiver size and the surface area of the concentrator increases which results in increase in heat losses from the receiver. The optimum rim angle is 90° if the trade-off is made between surface area and the receiver diameter. Slope error plays a significant effect on the LCR; hence, these should be controlled if one has to go for large aperture PTSC. It is desirable to upgrade the manufacturing standards for the manufacturing of the large aperture PTSC to improve the GCR of the collector. If the current practices have been used for the manufacturing, the size of the receiver will also increase with increase of the aperture size and so the heat losses from the receiver which decreases the performance of the PTSC.

References 1. Bellos, E., Tzivanidis, C.: Alternative designs of parabolic trough solar collectors. Prog. Energy Combust. Sci. 71, 81–117 (2019) 2. Timilsina, G.R., Kurdgelashvili, L., Narbel, P.A.: Solar energy: markets, economics and policies. Renew. Sustain. Energy Rev. 16(1), 449–465 (2012) 3. International Energy Agency (IEA): Renewable energy essentials: concentrating solar thermal power at https://www.iea.org/publications/freepublications/publication/CSP_Essent ials.pdf (2009) 4. International Renewable Energy Agency (IRENA): Renewable energy technologies: Cost analysis series. Concentrating Solar Power at https://www.irena.org/publications/2012/Jun/Renewa ble-Energy-Cost-Analysis–Concentrating-Solar-Power (2012) 5. Song, J., Tong, K., Li, L., Luo, G., Yang, L., Zhao, J.: A tool for fast flux distribution calculation of parabolic trough solar concentrators. Sol. Energy 173, 291–303 (2018) 6. Jeter, S.M.: Calculation of the concentrated flux density distribution in parabolic trough collectors by a semifinite formulation. Sol. Energy 37(5), 335–345 (1986) 7. Khanna, S., Kedare, S.B., Singh, S.: Analytical expression for circumferential and axial distribution of absorbed flux on a bent absorber tube of solar parabolic trough concentrator. Sol. Energy 92, 26–40 (2013) 8. Lüpfert, E., Pottler, K., Ulmer, S., Riffelmann, K.J., Neumann, A., Schiricke, B.: Parabolic trough optical performance analysis techniques. J. Sol. Energy Eng. 129(2), 147–152 (2007)

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9. Schiricke, B., Pitz-Paal, R., Lüpfert, E., Pottler, K., Pfänder, M., Riffelmann, K.J., Neumann, A.: Experimental verification of optical modeling of parabolic trough collectors by flux measurement. J. Sol. Energy Eng. 131(1), 011004 (2009) 10. Ulmer, S., Heinz, B., Pottler, K., Lüpfert, E.: Slope error measurements of parabolic troughs using the reflected image of the absorber tube. J. Sol. Energy Eng. 131(1), 011014 (2009) 11. Roesle, M., Coskun, V., Steinfeld, A.: Numerical analysis of heat loss from a parabolic trough absorber tube with active vacuum system. J. Sol. Energy Eng. 133(3), 031015 (2011) 12. Hachicha, A.A., Rodríguez, I., Capdevila, R., Oliva, A.: Heat transfer analysis and numerical simulation of a parabolic trough solar collector. Appl. Energy 111, 581–592 (2013) 13. Wang, Y., Liu, Q., Lei, J., Jin, H.: A three-dimensional simulation of a parabolic trough solar collector system using molten salt as heat transfer fluid. Appl. Therm. Eng. 70(1), 462–476 (2014) 14. Cheng, Z.D., He, Y.L., Du, B.C., Wang, K., Liang, Q.: Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm. Appl. Energy 148, 282–293 (2015) 15. Zhao, D., Xu, E., Yu, Q., Lei, D.: The simulation model of flux density distribution on an absorber tube. Energy Procedia 69, 250–258 (2015) 16. Houcine, A., Maatallah, T., El Alimi, S., Nasrallah, S.B.: Optical modelling and investigation of sun tracking parabolic trough solar collector basing on Ray Tracing 3Dimensions-4Rays. Sustain. Cities Soc. 35, 786–798 (2017) 17. Liang, H., Fan, M., You, S., Zheng, W., Zhang, H., Ye, T., Zheng, X.: A Monte Carlo method and finite volume method coupled optical simulation method for parabolic trough solar collectors. Appl. Energy 201, 60–68 (2017) 18. Zou, B., Dong, J., Yao, Y., Jiang, Y.: A detailed study on the optical performance of parabolic trough solar collectors with Monte Carlo Ray Tracing method based on theoretical analysis. Sol. Energy 147, 189–201 (2017) 19. He, Y.L., Xiao, J., Cheng, Z.D., Tao, Y.B.: A MCRT and FVM coupled simulation method for energy conversion process in parabolic trough solar collector. Renew. Energy 36(3), 976–985 (2011) 20. Cheng, Z.D., He, Y.L., Cui, F.Q., Xu, R.J., Tao, Y.B.: Numerical simulation of a parabolic trough solar collector with nonuniform solar flux conditions by coupling FVM and MCRT method. Sol. Energy 86(6), 1770–1784 (2012) 21. Leutz, R., Annen, H.P.: Reverse ray-tracing model for the performance evaluation of stationary solar concentrators. Sol. Energy 81(6), 761–767 (2007) 22. Islam, M., Karim, M.A., Saha, S.C., Miller, S., Yarlagadda, P.K.: Development of empirical equations for irradiance profile of a standard parabolic trough collector using Monte Carlo ray tracing technique. Adv. Mater. Res. 860, 180–190 (2014) 23. Wang, Y., Liu, Q., Lei, J., Jin, H.: Performance analysis of a parabolic trough solar collector with non-uniform solar flux conditions. Int. J. Heat Mass Transf. 82, 236–249 (2015) 24. Mwesigye, A., Huan, Z., Bello-Ochende, T., Meyer, J.P.: Influence of optical errors on the thermal and thermodynamic performance of a solar parabolic trough receiver. Sol. Energy 135, 703–718 (2016) 25. Jose, P.D.: The flux through the focal spot of a solar furnace. Sol. Energy 1, 19–22 (1957) 26. MATLAB and Statistics Toolbox Release: The MathWorks Inc. Natick, MA, USA (2017)

Cooling Energy-Saving Potential of Naturally Ventilated Interior Design in Low-Income Tenement Unit Ahana Sarkar

and Ronita Bardhan

1 Introduction Indoor design, albeit a subject of individual predilection and societal regime, tends to possess spinoff repercussions on household-level environmental quality and energy demand [1]. Particularly, in low-income settlements where space-restraints tie with social milieu leading to the inferior indoor environment, this becomes exigent. With unprecedented urbanization levels, people are transiting indoors gradually and spending 90% time inside. Hence, energy-efficient interior designs turn integrally crucial. The adverse climate in tropical regions has led to inefficient indoor temperature and airflow performance within living areas. This forces the occupants to rely on electro-mechanical ventilation for reaching acceptable thermal comfort. This phenomenon advertently increases the cooling energy demand up to 6.7% of the total world energy consumption. A significant amount of researches have analyzed the energy requirement and consumption pattern [2], available energy sources, energy transition and overall energy situation of tropical India, to investigate the potential of renewable energy implementation [3]. Identical findings have been reported for other developing nations [4] like China [5] and tropical regions like Mexico with similar climatic influences. While accounting the background of sustainable space cooling technologies, researchers have addressed natural ventilation to be an effectual alternative in delivering acceptable thermal comfort conditions while reducing energy consumption by 2.35% [6, 7].

A. Sarkar (B) · R. Bardhan Centre for Urban Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 700046, India e-mail: [email protected] R. Bardhan Department of Architecture, University of Cambridge, Cambridge CB2 1PX, UK © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_9

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Natural ventilation, as a passive building cooling strategy, has been less explored in India [8]. The energy-saving capabilities of natural ventilation in Indian lowincome buildings have been studied a few times [9], to the best of authors’ knowledge. However, specific occupant behaviour, detailed interior designs and numerical analyses on micro-level cooling energy-saving potential were not considered in the afore-mentioned study [9]. Mumbai’s residential sector records to 3386 cooling degree days. It contributes to two-third of the electrical load, with air-conditioning (30%) which is going to surge up to 73% by 2030. Considering a coincidence factor 0.7, the highest demand contribution of air-conditioned rooms has augmented from 5 Gigawatt (GW) in 2010 to 46 GW in 2020 and 143 GW in 2030 [10]. Additionally, owing to progressive slab rates of electricity charges, the tariff varies from INR 3.36/kWh for 0–100 units to INR 9.50/kWh for over 1000 units [11]. Among end-use components, occupant behaviour and indoor built environment design affect the energy demand maximum. Thus, with the excessive usage of energy-intensive appliances, the electricity charges tend to sprout to higher slabs, generating economic burden, especially for the low-income population. Mumbai, the financial capital of India, by attracting huge in-migration has transformed into the largest slum agglomeration. Currently, national affordable housing programs like ‘Housing for All 2022’ and slum improvement strategies provided by Mumbai City Development Plan 2005–2025 deliver affordable multi-rise rehabilitation units with habitable space of 24.6 m2 to the slum population [12, 13]. However, these completely ignore the livability parameters thus providing the occupants lesser degree of freedom in interior layout. These space-restrained compact multi-rise low-income tenement units suffer from poor indoor airflow and hightemperature zones owing to poor cross-ventilation. Consequently, these units turn energy-intensive because of lack of energy-concerning awareness and extensive low-cost energy-intensive cooling equipment usage despite economic, social and cultural-regime constraints. The novelty of this study lies in the utilization of cross-sectional methodology to evaluate the natural ventilation potential under warmer conditions using lowincome tenement of Mumbai as a case example. Objectively, this study attempted to identify the nat-vent effective iterated interior design solution and compare its energy-saving potential with respect to the existing case. This study also elucidated on the impact of optimized furniture location in delivering comfortable experiential indoor air velocity. This design is expected to save cooling energy consumption by reducing active cooling techniques usage. Owing to lack of literature regarding the nexus of low-income space-constrained tenement unit design, occupant behaviour and micro-level energy consumption pattern, this is the first study of its kind regarding the investigation of micro-level cooling energy reducing the possibility of nat-vent effective low-income tenement units design of Mumbai.

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2 Data and Methods 2.1 Case Study The slum rehabilitated multi-rise colony of Lallubhai compound was selected as the study area. Each tenement unit (21.42 m2 ) within this compound, stacked alongside a common double-loaded corridor, consisted of multipurpose living space with attached toilet and unsegregated kitchen. The major interior parameters included a window (air-inlet), a high-level air-outlet (0.3 m × 0.3 m) and a single-bed (an item of furniture). To identify the comfort-efficiency of the unit’s airflow performance, a ‘monitoring point’ was designated at 1.2 m above (human height during sitting) at the mid-bed position. The rationale behind the selection of ‘monitoring point’ can be attributed to the most observed occupant behaviour, where inhabitants spend most of the time near the bed due to high living space restriction. The aim would be to offer effective thermal comfort levels over this monitoring point, connoted further as ‘active zone’. From the housing survey, the unsegregated kitchen was noted to be detrimental to adverse indoor environmental conditions. Hence, partition wall and bed location were introduced as interior design parameters while generating iterated hypothetical cases.

2.2 Mixed-Mode Method A transverse stepwise methodology coupled with a housing survey and in-situ environmental sensor deployment (Testo 480® vane-metre, temperature sensors) was adopted here. The aim was to assess the nat-vent efficient indoor design with the highest cooling energy reduction possibility. The methodology started with the formulation of environmentally sustainable optimal design layouts (see Fig. 1).

Fig. 1 Floor plan of the iterated scenarios (change in bed location and partition wall design)

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These layouts were generated by utilizing ‘random sampling—computational fluid dynamics (CFD) simulations in nat-vent conditions—multi-objective optimization’-based approach adapted from [1]. The ten scenarios differed in partition wall design, its distance from the window, orientation, and height and bed location. CFD simulations Next, the indoor air velocity performance of the layouts was tested with hourly CFD simulations utilizing commercially available CFD tool of ANSYS Fluent. Both nat-vent and mech-vent enabled conditions using the ceiling fan and air-conditioning (AC) were simulated. For most of the indoor pollution simulators, the ambient air is assumed to be infinitely large volume. Thus, exhaust fan vented to the outdoors especially to the double-loaded closed corridors would degrade the environment of the neighbours. The vented air would also get reverted inside due to ceilings and parapets trapping the air. Hence, the exhaust fan was not considered for this particular space-constrained case study. The input boundary conditions for CFD models were retrieved from the vane-metre-based recordings. The models used fine tetrahedral mesh along with refinement for window (air-inlet), fan, and AC-inlet. The steady-state model settings included RANS k−ε turbulence model for nat-vent and AC and rotating frame model for ceiling fan. SIMPLE algorithm was utilized in CFD simulations for all cases for solving velocity–pressure coupling. Identification of nat-vent effective design scenario The afore-mentioned CFD simulated air velocity for natural ventilation, ceiling fan and AC represented airspeed values over the CFD monitoring point. Furthermore, owing to the stochastic nature of wind-driven natural ventilation, the holistic indoor airflow performance for a longer duration could not be captured in the nat-vent scenario. Hence, three major indicators were further utilized to identify the final design harnessing highest cooling energy reducing possibility due to nat-vent effectiveness. First, sensor-based hourly averaged outdoor wind speed at the window was reckoned for eight consecutive hours of a day in August 2018. The occupants’ reservations concerning privacy, the reveal of information and unwillingness regarding sensor installation for longer duration restricted the authors to install indoor sensors for eight hours only during daytime. Yet it beheld the impression of uncertainties linked with natural ventilation. The design scenarios were then simulated for these eight hours to retrieve the indoor air velocity values over the active zone in nat-vent condition. The nat-vent profiles for all scenarios were compared with persistent experiential air velocity values from the ceiling fan and AC. In this study, the hypothesis considered was—‘the design scenario which would deliver maximum hours of comfortable indoor air velocity, i.e. within the range of 0.2–1.08 m/s solely with wind-driven natural ventilation strategies would be selected primarily as nat-vent effective design. Another proxy measure of ‘total percentage of breathing zone (area of bed) with thermally acceptable indoor air velocity’ was accounted for additional screened selection in order to capture the holistic indoor airflow characteristics. The scenarios were also compared against average airspeed values over monitoring point due to natural ventilation, ceiling fan and AC in order to identify the most appropriate design scenario with maximum nat-vent enabled comfortable air velocity along with mech-vent strategies.

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The design solution which was observed to exhibit the maximum number of natural ventilation hours, which could deliver comfortable wind-driven indoor air velocity with constant set-point temperature and experience maximum percentage of area over the active zone (bed in this case) with comfortable air velocity in nat-vent conditions, would be finalized as the most nat-vent efficient indoor design solution. Estimating Cooling Energy Reducing Possibility A step-wise empirical analysis was adopted to calculate the cooling energy reducing possibility utilizing natural ventilation for the most nat-vent effective design layout. Nonetheless, to estimate the energy-saving potential, it is vital to compute the cooling demand with or without mech-vent modes while maintaining a fixed tolerable indoor temperature. The simulated airspeed values for all scenarios in nat-vent and mech-vent context were initially simulated keeping the temperature constant at 300 K (26.85 °C). This was designated as this value offers genuine comfort measure for indoor settings and is considered appropriate for warm-humid climate throughout the year. However, due to increased temperature, the thermal comfort levels tend to vary especially during warmer seasons of the tropical climate of Mumbai, which advertently forces the occupants to shift to mech-vent strategies from sole natural ventilation. In a response to this context, the monthly average temperature profile was retrieved from the Indian Meteorological Department (IMD) Mumbai to identify the total number of months the ‘best design scenario’ would deliver thermally comfortable indoor air velocity in nat-vent conditions. In order to estimate the energy-reducing potential, the AC and ceiling fan demand without natural ventilation was calculated. An alternative approach of estimating the energy saving is utilizing the thermal energy balance equation. Here, the air changes per hour that need to be delivered to the zone to cool it down can be estimated by aggregating the required cooling power and the difference between indoor and ambient temperature [7]. The estimation for the warmer months was carried out based on the energy balance model where heat taken from the room was assumed equal to the heat removed by the AC (refer to Eq. 1). (Q) = C p nt = τ W

(1)

where Q = amount of heat to be removed from the room, C p = heat coefficient of air at 1 atm pressure, n = moles of air to be cooled within the room, t = Difference in temperature, τ = Time taken by the AC to reduce the temperature by t, W = power of the air-conditioner in Kilowatt. On attainment of the desirable ambient condition, i.e. 300 K (26.85 °C), the ceiling fan was then assumed operated to uphold the thermally comfortable ventilation levels. However, nat-vent effective strategies have not been considered for warmer months

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in order to lessen the complexity concerning the uncertainties of natural ventilation. Thus, the total energy consumed by the unit for delivering thermally comfortable ventilation levels for warmer months was the aggregate of energy consumed by AC and ceiling fan (see Eq. 2).   E sav = E AC + E ceiling fan − E nat-vent

(2)

The total energy consumption owing to the air-conditioning and ceiling fan utilization for the summer months was calculated for best design scenario as well as the basecase scenario. This best indoor design layout would thus harness maximum cooling energy-reducing potential by increasing natural ventilation utility and minimizing mech-vent usage in winter months.

3 Results 3.1 Interior-Based Airflow Simulations The CFD simulated indoor airspeed values for natural ventilation scenarios (see Fig. 2a) over the active zone were observed between 0.10 m/s and 0.68 m/s. This indicates the significance of interior design and elucidates that by altering design elements like partition wall designs and bed location, comfortable indoor air velocity levels can be achieved in nat-vent spaces. Among ten scenarios, scenario 1 recorded maximum air velocity value of 0.69 m/s, when outdoor air velocity at air-inlet (here window) was measured 0.98 m/s. On contrary, scenarios 4, 5 and 6 recorded lowest air velocity values of 0.13 m/s, 0.16 m/s and 0.12 m/s, respectively. This can be attributable to the increased distance of bed from the window (air-inlet) for scenario 5 and 6, while, in scenario 4, the bed is located in the low-velocity zone thus creating a spatial gap between the bed position and airflow path. Despite same bed locations for scenario 1 and 4, the partition wall design also significantly modified the indoor airflow pattern. Thus, appropriate bed location selection with respect to optimized partition wall design is necessary to deliver comfortable indoor ventilation levels over the ‘active zone’. The room was considered an enclosed volume without any inlet and outlet while predicting for ceiling fan induced ventilation performance (see Fig. 2b). Here, the ceiling fan, accounted as a momentum source, was modelled with a diameter of 120 cm and a separation of 254 mm from the ceiling [14]. The velocity contours show that ceiling fan induced air velocity ranges between 0.0 and 2.2 m/s. Due to the steady behavioural character of airflow generating from the ceiling fan, maximum air velocity values were recorded right below the fan (1.08–1.48 m/s). It gradually reduced with the increased distance from the fan. However, scenario 1 and 8 experienced maximum airspeed of 0.55 m/s and 0.67 m/s, respectively, over the active zone which is attributed to the close proximity between bed and fan, while still air

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Fig. 2 Indoor air velocity profiles due to a natural ventilation, b ceiling fan and c air-conditioning

zones were observed over the active zone for scenarios 5 (0.18 m/s), 6 (0.17 m/s) and 7 (0.20 m/s) due to an increased gap between the ceiling fan and bed location. The constant reference air-conditioner (AC) inlet velocity and inlet supply temperature were assumed v = 2 m/s and T = 297 K (23.85 °C) for all the ten cases. Figure 2c shows nearly uniform air velocity distributions for all cases. Although a negligible difference in the highest and lowest mass-weighted average speed of 0.015 m/s was observed, the CFD predicted air velocity over the active zone was found to range between 0.02 m/s (scenario 6) and 0.84 m/s (scenario 9). The high air velocity zone of 0.8 m/s was found constant in all cases due to stable air throw from AC. Hence, optimized bed location with respect to the AC position can improve experiential air velocity over the active zone.

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3.2 Most Natural Ventilation Effective Design Scenario The sensor reckoned average outdoor air speeds at air-inlet were 0.47, 0.52, 0.52, 0.31, 0.23, 0.61, 0.21 and 0.98 m/sec. Figure 3a demonstrates that design scenario 4, 5 and 6 could not provide comfortable air velocity over active zone with sole nat-vent as well as ceiling fan and air-conditioning owing to poorly positioned bed. Out of eight hours, design scenarios 3, 7 and 9 delivered comfortable indoor airspeed for five hours. ‘Scenario 1’ was observed to be the most feasible design as the active zone had thermally comfortable nat-vent-driven indoor airflow for six hours. Figure 3b elucidates that effective percentage of comfortable ventilation for design scenarios 6 and 9 were recorded minimum for natural ventilation. While scenario 1 could deliver comfortable air velocity for over 96.95% of active zone in the natvent scenario and 89.1% from AC, scenarios 10 and 8 recorded highest (79.88 and 79.52% of the active zone) in terms of ceiling fan induced ventilation. This signifies that scenario 1 is the most nat-vent effective indoor design layout. Lastly, when the scenarios were compared against indoor air velocity performance levels for average nat-vent condition, ceiling fan and AC (see Fig. 3b), scenarios 1 and 9 performed best as these solutions delivered comfort air velocity from both natvent, AC and ceiling fan contexts. Hence, considering the afore-referred conclusions from the three above indicators, ‘scenario 1’ was decided to be the most feasible design layout with the highest nat-vent efficiency.

Fig. 3 Comparison of scenarios utilizing natural ventilation, ceiling fan and air-conditioning

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Table 1 Time required by air-conditioning to reduce to an ambient comfortable temperature Months

April

May

June

July

Sept.

Oct.

Nov.

Avg. Temp (°C)

28.1

29.7

28.9

27.2

27

28

27

Set Temp (°C)

26.85

τ (min)/day

1.34

3.03

2.19

0.37

0.1613

1.23

0.161

3.3 Cooling Energy-Reducing Potential Based on the weather data retrieved from IMD Mumbai, five months were found to be thermally convenient when the average temperature was recorded below 300 K (26.85 °C). This explicated that the ‘scenario 1’ would be able to deliver thermally comfortable air velocity over active zone without the assistance of any mech-vent techniques (ceiling fan or AC). While for seven warmer months, an aggregated utilization of afore-mentioned mech-vent strategies was required to reduce the room temperature. Table 1 shows the total time taken by the 1 kW AC to reduce the indoor temperature with 54.81 cu.m volume using Eq. 1. It can be observed from Fig. 4 that for the seven warmer months, the aggregated energy consumption for ‘scenario 1’ was accounted to 2.15 kWh for AC and 378 kWh for ceiling fan, amounting to 380.17 kWh. The estimation was subsequently lower than the base-case scenario with 744.6 kWh (AC: 105 kWh and ceiling fan: 639.6 kWh). This can be primarily attributable to the five months of natural ventilation efficiency and its potential to deliver thermally comfortable temperature for ‘scenario 1’ which saved cooling energy consumption during this period. Owing to the poor furniture location, the base-case scenario was forced to utilize mech-vent modes throughout the year to exhibit acceptable thermal comfort conditions. Furthermore, the occupants, unaware of energy concerns, tend to operate the AC for excess hours (0.5–1 h/day), advertently leading to an increase in unnecessary energy consumption. The energy saving due to natural ventilation is the difference of the cooling demand without natural ventilation and that with natural ventilation. Considering the flat rate

Fig. 4 Average monthly temperature variation in Mumbai (left); energy consumption pattern of ‘design scenario 1’ and ‘base-case scenario’

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of electricity charges, an annual saving of INR 2575.86 could be achieved through effective utilization of natural ventilation strategies. Thus, this analysis represents that with mere alteration in bed location, cooling energy demand can be significantly reduced by increasing indoor ventilation levels.

4 Conclusion A mixed-mode design and numerical analyses-based sequential methodological approach were adopted here to assess the interior design layout with maximum cooling energy-saving potential due to natural ventilation utilization. The results conclude that appropriate interior design with an optimized partition wall and furniture location as depicted in ‘scenario 1’ can deliver thermally comfortable indoor ventilation levels and can harness higher cooling energy-saving potential. Factors like windows operating schedule, construction materials, built area, room orientation, and occupants which are aerodynamically effective parameters have not been considered here. Moreover, the cost savings have been estimated based on a flat rate of INR 7/kWh which would portray a different image with progressive slab consideration. Nevertheless, the results demonstrate an exact potential of inexpensive interior retrofit design to analyze the probability of utilizing natural ventilation to have both environmental and economic benefits in tropical climates. This analysis can pave a pathway to the development of regulatory design guidelines for environmentally sustainable and energy-efficient low-income habitat rejuvenation.

References 1. Sarkar, A., Bardhan, R.: Optimizing interior layout for effective experiential indoor environmental quality in low- income tenement unit : a case of Mumbai, India. In: Building Simulation and Optimization Conference, Sept 2018, pp. 11–12 2. Pachauri, S., Spreng, D.: Direct and indirect energy requirements of households in India. Energy Policy 30, 511–523 (2002) 3. Kumar, A., Kumar, K., Kaushik, N., Sharma, S., Mishra, S.: Renewable energy in India: current status and future potentials. Renew. Sustain. Energy Rev. 14(8), 2434–2442 (2010) 4. Lee, C., Chang, C.: Energy consumption and GDP revisited: a panel analysis of developed and developing countries. Energy Econ. 29, 1206–1223 (2007) 5. Crompton, P., Wu, Y.: Energy consumption in China: past trends and future directions. Energy Econ. 27, 195–208 (2005) 6. Tong, Z., Chen, Y., Malkawi, A., Liu, Z., Freeman, R.B.: Energy saving potential of natural ventilation in China: the impact of ambient air pollution. Appl. Energy 179, 660–668 (2016) 7. Oropeza-perez, I., Alberg, P.: Energy saving potential of utilizing natural ventilation under warm conditions—a case study of Mexico. Appl. Energy 130, 20–32 (2014) 8. Indraganti, M.: Adaptive use of natural ventilation for thermal comfort in Indian apartments. Build. Environ. 45(6), 1490–1507 (2010) 9. Bardhan, R., Debnath, R., Malik, J., Sarkar, A.: Low-income housing layouts under socioarchitectural complexities: a parametric study for sustainable slum rehabilitation. Sustain. Cities Soc. 41(April), 126–138 (2018)

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10. Phadke, A.: Avoiding 100 new power plants by increasing efficiency of room air conditioners in India: opportunities and challenges, June 2014 11. The Maharashtra Electricity Regulatory Commission and Electricity Supply Code, (Annexure ‘ A’) Approved Tariff Schedule Maharashtra State Electricity Distribution Co. Ltd., no. 19, pp. 1–24 (2012) 12. Lueker, J., Bardhan, R., Sarkar, A., Norford, L. K.: Indoor air quality among Mumbai’s resettled populations: Comparing Dharavi slum to nearby rehabilitation sites. Build. Environ. 167, (2020) 13. Sarkar, A., Bardhan, R.: A simulation based framework to optimize the interior design parameters for effective Indoor Environmental Quality (IEQ) experience in affordable residential units: cases from Mumbai, India. In: IOP Conference Series: Earth and Environmental Science, vol. 294, p. 012060 (2019) 14. Babich, F., Cook, M., Loveday, D., Rawal, R., Shukla, Y.: Transient three-dimensional CFD modelling of ceiling fans. Build. Environ. 123, 37–49 (2017)

Development of an Improved Cookstove: An Experimental Study Himanshu, S. K. Tyagi, and Sanjeev Jain

1 Introduction The limited availability of fossil fuels and has forced researchers to search for alternative sources of energy for domestic cooking activities. Presently, about 2.8 billion people across the globe lack access to clean cooking energy [1]. Majority of them rely on the traditional stoves using solid biomass fuel to meet their daily cooking energy requirements. The incomplete burning of biomass in traditional stoves leads to lower thermal efficiency and higher emissions of pollutants [2]. The people especially young children and women who are exposed to emissions from traditional stoves suffer from adverse health effects [3]. Indoor air pollution from burning of solid fuel used for cooking is accountable for approximately 4.3 million deaths annually in addition to various respiratory and cardiovascular diseases [4]. Emission of black carbon from cooking stoves was found to be one of the essential causes of rapidly changing climate [5]. Improved cookstoves can significantly reduce the environmental and healthrelated issues caused due to traditional stoves. The forced draft improved cookstoves are found to be the most promising in reducing black carbon emissions released due to biomass burning [6]. The emission of particulate matter (PM 2.5) was found to be lowest for advanced forced draft cookstoves in comparison with traditional cookstoves [7]. An experimental study was carried out to compare the emission of PM from improved and traditional biomass cookstove, and the results indicated that the emissions of CO and PM were drastically decreased for improved gasifier stoves as compared to three stone stoves [8]. The recent advancements such as material of construction of cookstove, mode of air supply to the combustion chamber, design methodology and testing methods have been discussed thoroughly to improve the performance of traditional cookstoves [9]. Himanshu · S. K. Tyagi (B) · S. Jain Indian Institute of Technology Delhi, New Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_10

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The effect of stove design and fuel type on efficiency and emissions of natural draft cookstoves was investigated and it was concluded that the semi-gasifier toplit up-draft stoves can significantly reduce the emission levels if they are operated with specified fuel under controlled operating conditions [10]. The performance of fifteen different cookstove models including both natural as well as forced draft configurations were analyzed experimentally [11]. The results indicated that the combustion in natural and forced draft top-lit up-draft models was cleaner due to proper mixing of air-fuel resulting in complete burning of volatiles [11]. The technical aspects such as design principles, parameters required to assess the cookstove performance, different testing protocols and methods available for performance evaluation of biomass cookstoves were reported on the basis of the existing literature [12]. The use of forced draft cookstoves in place of traditional cookstoves can result in huge saving of fuel and a significant reduction in emissions of methane, organic carbon, black carbon and other hydrocarbons can also be achieved [13]. The present work is focused on the development of an improved forced draft cookstove to reduce the emission of CO and PM resulting from solid biomass burning. An experimental investigation has been carried out to investigate the thermal performance and emission characteristics of the developed cookstove model.

2 Materials and Methods An improved cookstove following the principle of gasification has been developed on the basis of design parameters such as height, diameter of the cookstove and air flow rate requirement available in the literature [14]. The developed cookstove model is a forced draft and the combustion chamber was of cylindrical shape. Two axial fans were attached to the combustion chamber to supply both primary as well as secondary air. A variable speed arrangement was also provided to alter the amount of air being supplied according to requirement. The developed cookstove model consists of two coaxial cylinders with outer and inner radii of 90 mm and 70 mm, respectively. The primary air was supplied below the grate placed at the bottom portion of the combustion chamber as shown in Fig. 1 for gasification of fuel kept inside the chamber. The holes were provided at the top portion of combustion chamber, i.e., inner cylinder to fulfill the secondary air requirement. The preheated air was introduced into the combustion chamber via secondary air holes by passing the air through annulus provided between two concentric cylinders. The diameter of both primary and secondary air holes was kept to be 4 mm. Biomass pellets of 8 mm diameter were used as fuel in the present study. The thermal performance and emission characteristics of the developed improved cookstove have been calculated by using Bureau of Indian Standards under standard operating conditions [15]. The schematic diagram of the setup as shown in Fig. 2 was used to investigate the performance of the improved cookstove. The major components of the setup used in the present study are duct, hood, flue gas analyzer, PM

Development of an Improved Cookstove: An Experimental Study

Fig. 1 Schematic representation of developed improved cookstove

Fig. 2 Schematic representation of the experimental test facility

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sampler, desktop and exhaust blower. The hood as shown in Fig. 2 was used to collect the emissions followed by dilution of the same by mixing with ambient air. The diluted emissions were sampled through PM analyzer to know the amount of PM in the cookstove emissions. The calorific value of biomass pellets was determined by bomb calorimeter. The sampling probe of gas analyzer (Testo 350 XL) was inserted in the duct to measure the emissions of CO. The range and resolution of gas analyzer used in the present work for measurement of CO emissions were 0–10,000 ppm and 1 ppm, respectively. The flue gas was sampled through PM 2.5 cyclone using suitable pump arrangement. The particulate matter was accumulated on the filter paper kept in the filter holder of PM analyzer and the mass of the same was determined by calculating the difference between initial and final weights of the filter. The velocity of exhaust gas passing through the duct was measured to calculate total volume of exhaust gas. Microbalance having resolution of 1 µg was used to weigh filter papers. The temperature of water was noted by using PT 100 temperature sensor with least count of 0.1 °C. The digital weighing balance having resolution of 1 g was used to measure the water filled inside the vessel.

3 Results and Discussion A total of six number of experiments were performed to ensure the accuracy of the results. The heating value of biomass pellets used in this study was found to be 17.3 MJ/kg.

3.1 Thermal Efficiency The thermal efficiency for six set of experiments is shown in Fig. 3 and the average value of the thermal efficiency for the present model during all the experiments were found to 36.82% which was beyond the minimum requirement of 35% to be fulfilled by any forced draft cookstove as prescribed by MNRE. The thermal efficiency for all experiments lies in the range of 36.52–37.4%. The higher thermal efficiency was attributed to precisely managed air flow rates which resulted in complete combustion of volatiles. Also, the heat losses were minimized from outer surface of improved cookstove due to incorporation of air gap surrounding the combustion chamber. The uncertainty in measurement of thermal efficiency was found to be 0.16%.

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Thermal Efficiency (%)

40 38 36 34 32 30

1

2

3

4

5

6

Experiment Number Fig. 3 Thermal efficiency of improved cookstove model

3.2 CO Emission The emission factors for CO calculated in g/MJD and g/kg-fuel are shown in Table 1. It can be observed that the emission factor for CO for the present model was in range of 5.67–7.06 g/kg which is approximately ten times lower as compared to the emission factor of CO for traditional cookstove [16]. The CO emissions were lesser in the improved stove due to reduced heat losses as a result of air gap which acted as insulation, and hence leading to higher combustion chamber temperature. The air flow rates of primary as well as secondary air were also adjustable as less air is required during start and stop phases of cookstove. The uncertainty in calculation of CO emissions was found to be 2.02%. Table 1 Emission factors for CO emission

Experiment

CO (ppm)

CO (g/MJD )

CO (g/kg)

1

46

0.96

5.87

2

47

1.03

6.43

3

51

1.09

6.63

4

52

1.03

6.57

5

60

1.098

7.06

6

49

0.89

5.67

Average

50.83

1.02

6.37

108 Table 2 Emission factors for PM 2.5 emission

Himanshu et al. Experiment

PM 2.5 (mg/MJD )

PM 2.5 (mg/kg)

1

30.65

185.91

2

32.01

198.87

3

30.92

186.68

4

29.47

186.97

5

28.57

183.92

6

28.12

179.04

Average

29.96

186.90

3.3 PM 2.5 Emission The PM 2.5 emission factors calculated in mg/MJD and mg/kg-fuel are presented in Table 2. The range of PM 2.5 emission factor for the present model was found to be 179–198 mg/kg which was approximately twenty-five times lower than that of the traditional cookstove [16]. The PM level decreased significantly due to uniform distribution of preheated secondary air into combustion chamber at the top portion of the cookstove which completely burnt the particulates which otherwise could escaped into the environment. The uncertainty in determining PM 2.5 emission was 0.47%.

4 Conclusions The present study has been carried out to investigate the thermal performance and emissions of CO and PM 2.5 from an improved forced draft cookstove using pellets as fuel. The combustion chamber was insulated by providing an air gap to increase the thermal efficiency of the stove. The secondary air was also preheated as it came in contact with the wall of combustion chamber by allowing it to pass through an air gap before being supplied to the chamber. The thermal efficiency of the improved stove was found to be three times higher than that of the traditional stove. The emission factors for both CO and PM 2.5 were determined on the basis of mass per unit of energy delivered to the pot and mass per unit quantity of fuel. The average value of emission factors for CO and PM 2.5 calculated in terms of mass per unit quantity of fuel was 6.37 g/kg and 186.9 mg/kg, respectively. The emissions from the developed model were drastically reduced due to optimum air supply, preheating of secondary air and an air gap around the combustion chamber. The outer surface temperature of the cookstove was still very higher than the ambient which suggested that heat loss should be minimized for further improvement of the thermal efficiency.

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References 1. Energy Access Outlook 2017: International Energy Agency https://www.iea.org/public ations/freepublications/publication/WEO2017SpecialReport_EnergyAccessOutlook.pdf. Last accessed 5 Feb 2019 2. Jetter, J.J., Kariher, P.: Solid-fuel household cook stoves: Characterization of performance and emissions. Biomass Bioenergy 33(2), 294–305 (2009) 3. Berrueta, V.M., Edwards, R.D., Masera, O.R.: Energy performance of wood-burning cookstoves in Michoacan, Mexico. Renew. Energy 33(5), 859–870 (2008) 4. Subramanian, M.: Deadly dinners. Nature 509(7502), 548 (2014) 5. Venkataraman, C., Habib, G., Eiguren-Fernandez, A., Miguel, A.H., Friedlander, S.K.: Residential biofuels in South Asia: carbonaceous aerosol emissions and climate impacts. Science 307(5714), 1454–1456 (2005) 6. Kar, A., Rehman, I.H., Burney, J., Puppala, S.P., Suresh, R., Singh, L., Singh, V.K., Ahmed, T., Ramanathan, N., Ramanathan, V.: Real-time assessment of black carbon pollution in Indian households due to traditional and improved biomass cookstoves. Environ. Sci. Technol. 46(5), 2993–3000 (2012) 7. Jetter, J., Zhao, Y., Smith, K.R., Khan, B., Yelverton, T., DeCarlo, P., Hays, M.D.: Pollutant emissions and energy efficiency under controlled conditions for household biomass cookstoves and implications for metrics useful in setting international test standards. Environ. Sci. Technol. 46(19), 10827–10834 (2012) 8. Just, B., Rogak, S., Kandlikar, M.: Characterization of ultrafine particulate matter from traditional and improved biomass cookstoves. Environ. Sci. Technol. 47(7), 3506–3512 (2013) 9. Kumar, M., Kumar, S., Tyagi, S.K.: Design, development and technological advancement in the biomass cookstoves: a review. Renew. Sustain. Energy Rev. 26, 265–285 (2013) 10. Tryner, J., Willson, B.D., Marchese, A.J.: The effects of fuel type and stove design on emissions and efficiency of natural-draft semi-gasifier biomass cookstoves. Energy Sustain. Dev. 23, 99–109 (2014) 11. Still, D., Bentson, S., Li, H.: Results of laboratory testing of 15 cookstove designs in accordance with the ISO/IWA tiers of performance. EcoHealth 12(1), 12–24 (2015) 12. Sutar, K.B., Kohli, S., Ravi, M.R., Ray, A.: Biomass cookstoves: a review of technical aspects. Renew. Sustain. Energy Rev. 41, 1128–1166 (2015) 13. Sharma, M., Dasappa, S.: Emission reduction potentials of improved cookstoves and their issues in adoption: an Indian outlook. J. Environ. Manage. 204, 442–453 (2017) 14. Belonio, A.T.: Rice husk gas stove handbook. Appropriate Technology Center. Department of Agricultural Engineering and Environmental Management, College of Agriculture, Central Philippine University, Iloilo City, Philippines (2005) 15. Bureau of Indian Standards (BIS): Indian standard on Portable Solid Biomass Cookstove (Chulha First Revision). IS 13152 (Part 1) (2013) 16. Venkataraman, C., Sagar, A.D., Habib, G., Lam, N., Smith, K.R.: The Indian national initiative for advanced biomass cookstoves: the benefits of clean combustion. Energy Sustain. Dev. 14(2), 63–72 (2010)

Impact of Demand Response Implementation in India with Focus on Analysis of Consumer Baseline Load Jayesh Priolkar and E. S. Sreeraj

1 Introduction The power sector in India faces various issues like low plant load factor, network congestion, aging assets, the high value of transmission, and distribution loss. Shifting towards a decentralized generation can overcome power sector challenges. Demand response (DR) is the technique of managing load consumption patterns of the consumer in response to the needs of power utility with aim of lowering electricity costs, infrastructural deferral, and improving system reliability [1, 2]. DR programs where renewable energy sources (RES) are integrated can overcome challenges like dynamic intermittency, ramping nature, uncertainty, and volatility reliably and effectively [3]. DR if implemented on large-scale results in the reduction of capacity requirements as well as CO2 emission reduction [4]. DR is one of the important components for the successful implementation of the smart grid. A smart grid is an intelligent two-way power and information flow delivery system from source to the consumer which facilitates the integration of distributed generation sources, storage systems, demand-side management, and electric vehicles [5]. Consumer baseline load (CBL) estimation is one of the important aspects of realizing and tapping DR potential. Baseline gives reference consumption which is determined based on consumer’s consumption characteristics and past load data. Developing a standard baseline for a different class of consumers helps the utility to decide about incentives and compensation to consumers for their participation in DR programs. Our work presented in this paper is divided into two parts. The first part reviews various aspects of DR and its possible impacts on RES integration and deployment of smart grids in India. In the second part case study of CBL estimation is presented and analyzed. From the load data analysis for the last three years of one of the industrial feeder in Goa state, the baseline load curve is developed by averaging, adjustment, J. Priolkar (B) · E. S. Sreeraj National Institute of Technology Goa, Ponda, Goa 403401, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_11

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maximum value, and regression-based methods. Various performance metrics related to accuracy, bias, and variability are found and all the methods are compared for their effectiveness. The results obtained from CBL analysis help to know the load curtailment needed during event and DR potential available. Power utility of the state does not have a standard method for baseline estimation and effective data regarding the potential value of DR. CBL analysis presented in this work can be generalized and used for the other feeders of the utility for the determining potential value of DR.

2 Importance of Baseline on CBL Estimation Accurate estimation of CBL is crucial for success of the DR program. Estimation of CBL gives what will be expected consumption pattern of a consumer in the absence of a DR event. CBL is estimated based on historical data and forecasting methods. Measurement of DR typically involves comparing actual load during time of curtailment to estimated load that would otherwise have occurred without curtailment. The actual load is metered by smart meters at the consumer’s location, so the difference between CBL and actual-metered load gives actual realizable DR potential. If CBL estimation is less, consumers are less motivated to participate in the DR program because of receiving lower incentives from utility. Incase of overestimation of CBL utility is less motivated to operate the DR program, because of the overestimation of load reduction and paying off higher incentives to consumers. The literature on various aspects of CBL estimation is available in [6–9]. A statistical method is proposed for CBL estimation for non-residential buildings in California [6]. Various CBL methods are analyzed and compared based on accuracy and bias for residential consumers in [7]. For residential consumers, effect of CBL on the performance of the peak-time rebate program is investigated [8]. CBL estimation based on load pattern clustering for residential consumers is proposed; results obtained are compared with averaging and regression-based methods [9].

3 DR Impact Analysis on Renewable Energy Integration and Smart Grids 3.1 DR Impact on Renewable Energy Integration Implementing the DR program in a power system network can overcome ramping nature, uncertainty, volatility, and dynamic intermittency of RES [10–16]. To model the unit commitment problem for an isolated power system with a major share of wind energy resource, two approaches are used in [10]. In the first approach, utility control loads remotely as per system requirement, and in the second approach, consumers

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based on price signals shift the loads to minimize the costs. The stochastic unit commitment model is used to handle variations in wind power by deploying DR programs, three cases based on centralized load control, demand-side bidding, and wind power variation on deferrable loads is investigated in [11]. To determine the optimal size of wind, photovoltaic and battery-based power systems, real-time pricing and interruptible load DR programs are used. An optimal scheme for real-time pricing and interruptible load is formulated to minimize system cost and loss of energy probability [12]. From the perspective of the system operator, profit maximization problem is formulated so that he can decide to procure energy from RES or spot market. An optimal DR-based price scheme to sell power to end-users for a different time frame is also proposed in [13]. To effectively capture time-varying uncertainty of wind on the generation and consumer behavior, the effect of real-time pricing is analyzed through a nodal based DR model in [14]. Mixed-integer linear programming simulation software is used for developing scheduling model which incorporates DR and intermittent RES with random outages of generation units in [15]. DR management strategy for non-deferrable load with RES and storage using continuous-time optimization is proposed in [16]. DR implementation will provide necessary capacity addition when renewable sources like wind and solar are ramping up or down. DR deployment will also influence optimal sizing and siting of RES. It will help to improve reliability and minimize cost in terms of sizing and location of RES for state utility.

3.2 DR Impact on Smart Grid Deployment The use of DR provides operational flexibility for smart grids. The established infrastructure of smart grids and active participation of the consumers creates opportunities for DR deployment. DR provides advantages of peak load shifting resulting in energy savings, cost savings for power purchase, and improving reliability which is also the objective of smart grid implementation. A transition toward the use of smart appliances among consumers also creates scope for tapping DR potential in India. As the penetration level of electric vehicles in the smart grid increases, DR will play a significant role in load curtailment/enhancement by unidirectional charging and bidirectional charging vehicle to grid and grid to vehicle. DR along with electric vehicles can address peak shaving, valley filling in power system network thus improving efficiency as well as it can help to provide balancing service in a smart grid environment [17].

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4 Discussion on DR Status, Opportunities, and Challenges for India Most of the states have constituted DSM cell to look after energy efficiency and DR programs. On a pilot basis, various DR programs were carried out in India to understand the effectiveness of its implementation. Tata Power company undertook and implemented an integrated DR model for Mumbai city. Another pilot project was carried out by Tata Power company and Maharashtra electricity regulatory commission to understand effectiveness of DR. Tata Power company along with Delhi distribution board carried out Auto-DR program to characterize load profile of the consumers, and it was reported that technical DR potential available for industrial consumer category was approximately 25 MW [18]. Smart grid projects are being carried out under the national smart grid mission in most of the states, and DR implementation is included in all these projects. Projects related to the integration of DR with RES are also included in these projects. Technological, economic, social, and regulatory issues need to be addressed for realizing DR potential in India. Optimal implementation of DR programs by state utility will provide reliability, economic, and societal benefits to all stakeholders. Reduction of the peak to average ratio can help to defer generation capacity requirement, transmission, and distribution enhancement. Price spikes in wholesale electricity markets and congestion in transmission networks during peak hours can be avoided by using DR. To overcome load shedding problems which is most common in various states of India, DR can be an effective tool. The ability of DR to provide a fast and prompt response by either ramping up or reducing load as per need will facilitate the grid connection of intermittent RES. DR program implementation will help to decide the optimal site and size of the RES and energy storage systems. It is recommended that price-based DR for retail consumers in the country need to be prioritized with smart metering infrastructure development. Introduction and implementation of real-time pricing for large consumers, real-time pricing or critical peak pricing for commercial consumers, and time of use for residential consumers will help to enhance DR deployment. Implementation of incentive-based programs needs to be improved based on the eligibility criteria of consumers, curtailment terms, cost recovery, and incentive payments.

5 Case Study on CBL Estimation Electricity utility in Goa state is owned by the government, and it still functions like vertically integrated utility with no significant technological, economic, and organizational reforms. There are no significant developments in the area of demand response, energy efficiency, and smart grids in the state. From our analysis of load data, it is observed that utility faces power and energy deficit mostly during peak hours which results in load restrictions for industrial consumers. Implementation of

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DR programs will enable state utility to improve reliability as well as get economic and social benefits. DR can be very effective to overcome load shedding problem. For CBL estimation, hourly load consumption and temperature data of industrial feeder for three years 2016, 2017, and 2018 are collected from the 33/11 kV substation of Goa. CBL is estimated to analyze the load curve by different methods and assess DR potential available for industrial consumers.

5.1 Averaging Method This method calculates for each hour of the daily average of load at that hour across all the included days chosen based on the data selection criterion [6, 7]. For baseline estimation in March 2016, 2017, and 2018, ten weekdays load consumption data is selected. The days when demand does not follow historical patterns are excluded to improve the accuracy of baseline estimation. For 10 in 10 method out of 10 selected days, the highest 10 days data for each hour is taken and a baseline is calculated by an averaging algorithm. For 7 in 10 and 5 in 10 baselines averaging method out of 10 selected days, highest 7 days data and highest 5 days data are taken and baseline by an averaging algorithm is calculated. For three years of data of March, CBL is computed and results obtained for the year 2018 are presented in this paper. For a 10 in 10 method comparison between the CBL and actual consumption values are shown in Fig. 1. For a 5 in 10 method the comparison between the CBL and actual consumption values are shown in Fig. 2. These figures indicate that predicted baseline consumption values by 10 in 10 method closely follows actual day load consumption as compared to 7 in 10 and 5 in 10 methods. 3

CBL 10 in 10

Load (MWh)

Actual Day 2

1

0 0

4

8

12

16

Time (Hours) Fig. 1 Comparison of 10/10 CBL with actual day for March 2018

20

24

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CBL 5 in 10

Load (MWh)

Actual Day 2

1

0

0

4

8

12

16

20

24

Time (Hours) Fig. 2 Comparison of 5/10 CBL with actual day for March 2018

5.2 Maximum Value Method In this method, the maximum value of load consumption for each interval for 10 days is selected and CBL estimation is done by averaging of maximum values. The comparison between the CBL and actual data for the maximum value method is shown in Fig. 3. This method is less accurate, and the baseline load curve does not closely follow actual day load consumption since the maximum values are taken for evaluation of baseline. Adjustment Method To consider the influence of weather, actual operating conditions, and for consistent comparison between actual load and baseline values during an event, adjustment method is used. Adjustment of the baseline load is done by scaling that is multiplication with an adjustment factor. Estimated values by 10 in 10 baseline are multiplied 3 CBL (Max)

Load (MWh)

Actual Day 2

1

0 0

4

8

12

16

20

Time (Hours)

Fig. 3 Comparison of CBL with actual day for March 2018 by maximum value method

24

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3

Load (MWh)

CBL (Adj) Actual Day

2

1

0 15

16

17

18

19

20

21

Time (Hours) Fig. 4 Comparison of CBL with actual day for March 2018 by adjustment method

with an adjustment factor to get adjusted baseline values. Adjustment moment for baseline is considered for the last two hours before the event. The adjustment factor is given by the following equation.     C(d, h = n) = Al(d,h=n−2) + Al(d,h=n−1) / Pl(d,h=n−2) + Pl(d,h=n−1)

(1)

where C(d, h = n) is adjustment factor, Pl(d, h) is predicted load, Al(d, h) is actual load, d is the day number, and h is the hour [19]. For comparison between CBL and actual load data (see Fig. 4), estimated consumption values closely follow the actual day load consumption. This method is most accurate compared to other methods because baseline adjustment is as per actual situations and due to consideration of a small timeline window which is from 15:00 to 21:00 h. Regression Method The baseline is calculated from a multi-regression model based on the daily energy equation, which considers consumer’s daily energy consumption as a dependent variable, temperature and time as an independent variable [9]. In this model, past load consumption data is correlated with temperature and time. The coefficient of the model for the data analyzed is estimated by linear ordinary least square regression and is given by the following equation. CBLt = −0.2053 + 0.0106 ∗ t + 0.0859 ∗ Tt

(2)

where CBLt = Consumer base line load for particular hour, t = hour number, T t = average air temperature for particular hour t, 0.2053, 0.0106, and 0.0859 are multiregression coefficients. For the regression method comparison between the CBL and actual values are shown in Fig. 5.

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CBL (Reg)

Load (MWh)

Actual Day

2

1

0 0

4

8

12

16

20

24

Time (Hours) Fig. 5 Comparison of CBL with actual day for March 2018 by regression method

6 Performance Metrics Evaluation for CBL Methods Averaging based and maximum value CBL methods are analyzed based on accuracy, bias, and variability for understanding how closely it predicts the actual consumer load. Accuracy of baseline indicates how closely it predicts the actual load. The tendency of baseline to over or under predict actual load is known as bias. Root mean square deviation (RMSD) and normalized root mean square error (NRMSE) are used as a statistical measure of accuracy, average relative error (ARE), and normalized average relative error (NARE) is estimated to know bias. Relative error ratio (RER) and normalized relative error ratio (NRER) are used as a statistical measure of variability [9]. ⎛  N ⎞  X 1,t − X 2,t 2 ⎠ RMSD = ⎝ N t=1

(3)

where X 1,t = Actual consumption, X2,t = Predicted consumption (10 in 10, 5 in 10) and N = Number of data points that is 24 NRMSE = RMSD/(Ymax − Ymin ))

(4)

where Y max = Maximum consumption on an actual day and Y min = Minimum consumption on an actual day ARE = X 1 / X 2

(5)

where X 1 = Average of predicted consumption − Average of actual consumption and X 2 = Actual consumption

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Table 1 Comparison of evaluated metrics for averaging and maximum value CBL methods CBL method

RMSD

NRMSE

ARE

NRER

10 in 10

0.070609

0.070014

0.009715

0.029578

7 in 10

0.077821

0.077069

0.01053

0.032611

5 in 10

0.08717

0.086332

0.01481

0.035368

Maximum value

0.20794

0.205933

0.078532

0.044553

Table 2 Comparison of evaluated metrics for adjustment and regression-based CBL methods CBL method

RMSD

NRMSE

ARE

NRER

Adjustment

0.034622

0.063786

0.00367

0.010815

Regression

0.085677

0.084849

0.010279

0.036321

R E R = Z 1 /Z 2

(6)

where Z 1 = Predicted consumption − Actual consumption and Z 2 = Average of actual consumption NRER = Standard deviation(RER)

(7)

Table 1 lists the computed values of performance metrics for all three averaging and maximum value methods. The least values are obtained for 10 in 10 baseline which shows better performance from the context of accuracy, bias, and variability over 7 in 10, 5 in 10 and maximum value methods. An averaging method is simple to understand for consumers and easy to evaluate for utility and can encourage more DR participation. Since additional data is used for analysis in adjustment and regression methods, performance metrics for these methods are listed separately in Table 2. From the computed values of performance metrics, it is seen that the adjustment factor substantially improves the performance of adjustment baseline in terms of higher accuracy and improved bias. The predicted baseline load curve closely follows the actual day load consumption in adjustment method as compared to all other methods used for analysis. From the load and solar insolation data, it was found that load peaks up from 10:00 to 16:00 h, solar insolation also peaks up during the same period. The integration of solar photovoltaic power system in the grid along with DR implementation can help to manage load effectively.

7 Conclusion Impact of DR on RES integration and smart grid in the country are highlighted and possible measures for effective implementation are suggested in this paper. From the survey, it is concluded that a wide scope for DR exists in the country and steps need

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to be initiated to tap this potential. DR implementation for the Indian power grid will enhance grid capacity, improve the quality, and reliability of power as well as promote renewable power generation. Various methods of CBL estimation for industrial feeder are analyzed. Among averaging and maximum value methods, 10 in 10 CBL method closely predicts the actual day load consumption. From performance metrics, it is found that the adjustment method of estimation is most accurate as compared to all other methods used for the analysis. Baseline methods suggested for estimation can be adopted for other feeders of the state utility to devise suitable DR programs for consumers. From the analysis of load curve data of feeder, estimated CBL values and solar insolation data of the last three years, it is suggested that integration of solar photovoltaic system along with DR can manage industrial load more effectively.

References 1. Medina, J.: Demand response and distribution grid operations: opportunities and challenges. IEEE Trans. Smart Grids 1(2), 193–198 (2010) 2. Albadi, M.: A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008) 3. Brahman, F.: Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system. Energy Build. 90, 65–75 (2015) 4. Fan, Z.: Smart grid communications: overview of research challenges, solutions, standardization activities. IEEE Commun. Surv. Tutorials 15(1), 21–38 (2013) 5. Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461– 478 (2014) 6. Sharifi, R.: Customer baseline models for the residential sector in the smart grid environment. Energy Rep. 2, 74–81 (2016) 7. Wijaya, T.: When bias matters: an economic assessment of demand response baselines for residential customers. IEEE Trans. Smart Grids 5(4), 1755–1763 (2014) 8. Mohajeryami, S.: The impact of customer baseline calculation methods on peak time rebate program offered to residential customers. Electr. Power Syst. Res. 137, 59–65 (2016) 9. Li, K., Wang, B., Wang, Z., Wang, F., Mi, Z., Zhen, Z.: A baseline load estimation approach for the residential customer based on load pattern clustering. In: Proceedings International Conference on Applied Energy, ICAE2017, Cardiff, UK (2017) 10. Dietrich, K.: Demand response in an isolated system with high wind integration. IEEE Trans. Power Syst. 24(1), 20–29 (2012) 11. Papvasiliou, A.: Large scale integration of deferrable demand and renewable energy sources. IEEE Trans. Power Syst. 28(2), 1385–1394 (2013) 12. Tobary, S.: Optimal sizing of PV wind battery power system considering DR programs. In: Proceedings of IEEE International Conference on Power Electronics and Drives Systems, Honolulu, USA (2017) 13. Cao, X., Zhang, J., Vincent Poor, H.: Optimal renewable penetration in energy procurement and demand response. In: Proceedings of IEEE International Conference on Communication, Kanas City, USA (2018) 14. Zeng, B.: Integrated planning for a transition to low carbon distribution system with renewable energy generation and DR. IEEE Trans. Power Syst. 29(3), 1153–1165 (2014) 15. Shahidehpour, M.: Stochastic operation security with demand response and renewable energy sources. In: Proceedings of IEEE Power and Energy Society General Meeting, San Diego, USA (2012)

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16. Leithon, J.: Demand response and renewable energy management using continuous time optimization. IEEE Trans. Sustain. Energy 9(2), 991–1000 (2018) 17. Miao Tan, K.: Integration of electric vehicles in smart grid: a review on a vehicle to grid technologies and optimization techniques. Renew. Sustain. Energy Rev. 53, 720–732 (2016) 18. Deshmukh, R., Yin, R., Ghatikar, G.: Estimation of potential and value of DR for industrial consumers in Delhi. In: Proceedings of India Smart Grid Week, Bangalore, India (2015) 19. Coughlin, K.: Statistical analysis of baseline load models for non-residential buildings. Energy Build. 41, 374–381 (2009)

Double Dielectric Barrier Discharge-Assisted Conversion of Biogas to Synthesis Gas Bharathi Raja , R. Sarathi , and Ravikrishnan Vinu

1 Introduction In recent years, due to increased energy demand and limited fossil fuel resources, there is a need to consider alternate renewable energy sources [1]. Biogas is contemplated as a promising feedstock for energy production. Currently, India has the second-largest number of biogas plants [2]. Biogas production requires lesser capital investment than other thermochemical processes to convert renewable feedstocks. The main constituents of biogas produced in anaerobic digestion of organic wastes from animal, agricultural and municipal wastes include CH4 (50–70%) and CO2 (30–50%) [3]. However, due to its lower calorific value, its use for power generation requires higher-energy consumption. Dry reforming of methane is considered as an effective way to produce syngas (H2 and CO), which is also used as a promising feedstock for various fuel and chemical syntheses via Fischer–Tropsch process, and for the production of power. CH4 + CO2 → 2H2 + 2CO H = 247 kJ mol−1

(1)

Dry reforming of methane is an endothermic process as shown in reaction (1), and requires high temperatures (above 800 °C) to attain reasonable, energy-efficient and cost-effective conversion of the highly stable CH4 and CO2 mixture [4]. Nonthermal plasma technology delivers a promising alternate means to convert biogas to syngas. Non-equilibrium conditions favor the formation of highly energetic electrons to initiate the thermodynamically unfavorable reactions at low temperatures and atmospheric pressure [5]. Non-thermal plasma can be generated by dielectric barrier Bharathi Raja (B) · R. Vinu Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India e-mail: [email protected] R. Sarathi Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_12

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discharge, corona discharge, gliding arc discharge and microwave discharges. One of the major advantages of using non-thermal plasma is that reactive processes requiring high activation energy can be easily achieved due to high electron temperature of 104 –105 K, corresponding to mean electron energy of 1–10 eV. Moreover, as a result of non-equilibrium condition among electrons, ions, radicals and neutral gas molecules, gas is at a low temperature ( commercial HZSM-5 (95.7%) > HZSM-5(20) (89.7%) > HZSM-5(60) (76.9%) ≈ HZSM-5(40) (74.2%).

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Table 2 Selectivity (%) of pyrolysates using various hydrodeoxygenation catalysts Compounds

Non-catalytic HZSM-5 HZSM-5 HZSM Comm. W2 C/γ-Al2 O3 (20) (40) 5 (60) HZSM-5

Phenolics Phenol

5.7

1.0

1.3

1.5

1.0

Cresol

8.4

0.7

1.7

2.7

0.8

0.3 0.0

Guaiacol

3.1

0.0

0.0

0.0

0.0

0.0

Methyl guaiacol

3.3

0.0

0.0

0.0

0.0

0.0

Ethyl guaiacol

0.9

0.0

0.0

0.0

0.0

0.0

Vinyl guaiacol

0.9

0.0

0.0

0.0

0.0

0.0

Other phenolics

5.6

0.0

0.0

0.5

0.2

0.0

Oxygenates Acetic acid

4.6

0.0

0.0

0.0

0.0

0.0

2-Pentanone

2.9

0.0

0.0

0.0

0.0

0.0

2-Cyclopenten-1-one

7.7

0.0

1.7

12.3

0.0

0.0

2-Cyclopenten-1-one, 2-methyl-

5.8

0.0

0.0

0.7

0.0

0.0

2-Butanone

13.3

2.0

5.4

3.1

0.0

0.0

2-Butenal

2.5

0.0

0.0

0.0

0.0

0.0

2-Cyclopentene-1,4-dione

0.1

0.0

0.0

0.0

0.0

0.0

Cyclopentanone

2.8

0.0

0.0

0.0

0.0

0.0

Other oxygenates

10.0

0.0

3.8

0.4

0.0

0.8

Furan, 2-methyl-

1.8

1.9

6.2

6.3

0.0

1.2

Furfural

3.0

0.0

0.0

0.0

0.0

0.0

2(3H)-Furanone, 5-methyl-

2.5

0.0

0.0

0.0

0.0

0.0

Furan derivatives

Benzofuran

0.0

1.3

1.6

1.6

0.0

0.0

2(5H)- Furanone

1.4

0.0

0.0

0.0

0.0

0.0

Other furan derivatives

6.6

1.0

1.9

2.7

0.0

0.2

Aromatic hydrocarbons Benzene

0.9

9.5

13.5

5.8

7.3

4.7

Toluene

1.7

22.5

13.7

15.3

24.2

10.2

Xylene

0.2

20.7

13.2

15.3

25.5

9.8

Styrene

0.0

1.1

2.3

1.5

2.8

0.1

Benzene derivatives

0.1

13.7

13.1

15.5

13.2

31.6

Indene derivatives

0.3

10.9

10.1

10.5

12.2

9.6

Naphthalene derivatives

0.1

9.2

4.8

8.8

10.1

15.4 (continued)

Hydrodeoxygenation of Bio-Oil from Fast Pyrolysis …

147

Table 2 (continued) Compounds

Non-catalytic HZSM-5 HZSM-5 HZSM Comm. W2 C/γ-Al2 O3 (20) (40) 5 (60) HZSM-5

Aliphatics hydrocarbons 1,3-Cyclopentadiene, 1-methyl-

0.0

1.9

1.3

1.8

0.2

2.7

4-Methylenecyclopentene

0.0

0.0

0.0

0.0

0.0

4.2

2-Pentene,3-methyl-, (Z)-

0.0

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References 1. Oasmaa, A., Elliott, D.C., Korhonen, J.: Acidity of biomass fast pyrolysis bio-oils. Energy Fuels 24(12), 6548–6554 (2010) 2. Adjaye, J.D., Bakhshi, N.N.: Upgrading of a wood-derived oil over various catalysts. Biomass Bioenergy 7(1–6), 201–211 (1994)

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3. Nguyen, T.S., Zabeti, M., Lefferts, L., Brem, G., Seshan, K.: Catalytic upgrading of biomass pyrolysis vapours using faujasite zeolite catalysts. Biomass Bioenergy 48, 100–110 (2013) 4. Nguyen, T.S., He, S., Raman, G., Seshan, K.: Catalytic hydro-pyrolysis of lignocellulosic biomass over dual Na2 CO3 /Al2 O3 and Pt/Al2 O3 catalysts using n-butane at ambient pressure. Chem. Eng. J. 299, 415–419 (2016) 5. Parsapur, R.K., Selvam, P.: A remarkable catalytic activity of hierarchial zeolite (ZH-5) for tertiary butylation of phenol with enhanced 2,4-di-t-butylphenol selectivity. ChemCatChem 10(18), 3978–3984 (2018) 6. Venkatesan, K., He, S., Seshan, K., Selvam, P., Vinu, R.: Selective production of aromatic hydrocarbons from lignocellulosic biomass via catalytic fast-hydropyrolsis using W2 C/γ-Al2 O3 . Catal. Commun. 110, 68–74 (2018)

Simulation of Horizontal Axis Wind Turbine Using NREL FAST Solver Asmelash Haftu Amaha, Prabhu Ramachandran, and Shivasubramanian Gopalakrishnan

1 Introduction The wind is an inexpensive form of clean energy which can be harvested from small scale to large scales without running out. Two of the most commonly employed wind energy devices are horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). In the case of HAWT, the use of a tall tower allows access to higher wind speeds because wind speed increases with altitude. This can increase the power output dramatically since the power is proportional to the cube of the wind speed [12, 13]. The rotor blades rotate at a right angle to the wind direction allowing them to collect power through the full rotation. Yaw control is incorporated so that the turbine arranges itself perpendicular to the flow and gather maximum wind power when the wind direction changes. This allows the turbine to produce very high power with maximum efficiency close to the Betz limit. One of the drawbacks with HAWTs is that the efficiency of modern HAWTs increases with the increase in tower height and blade length, currently reaching 160 meters long and more [15]. However, it is difficult to make the peak efficiency a reality in many cases. Cost of transportation is more; heavy and costly cranes are needed for installation. A yawing mechanism is also required [15]. This makes it difficult to setup such technology in third-world countries. The higher the length of the blade the higher the tip speed and the noisier the turbines. In addition, there is a larger thrust force on the tower. Furthermore, the peak efficiency can be obtained at high wind speeds and only some countries have areas with high wind speeds. The HAWTs in countries with lower wind speed are not as efficient as desired. When compared with VAWT blades, HAWTs can capture more wind energy for the reason that the whole area swept by HAWT blades is perpendicular to the wind A. H. Amaha (B) · P. Ramachandran · S. Gopalakrishnan Indian Institute of Technology Bombay, Powai, Mumbai 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_15

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direction. According to the diameter of their blades, HAWTs are grouped in to four categories, according to Jeng and Wang, (2016) [3]. 1. 2. 3. 4.

microscale (μSW T, diameter ≤ 0.1 m), smallscale (SSW T, 0.1 m < diameter ≤ 1 m), midscale (M SW T, 1 m < diameter ≤ 5 m), and largescale (L SW T, diameter > 5 m).

In most cases, large-scale category HAWTs are installed in grid systems or wind farms, whereas small-scale HAWTs find applications near residential areas. The wind turbine considered in this study is employed for mid-scale power production. Over the past, various ways have been applied to find the most suitable and convenient options to design and analyze HAWTs. Most of the research works explored so far, focused on different blade configurations (like 2 blades and 3 blades), and make use of research tools and theories like blade element momentum (BEM), improved BEM, commercial and open-source (CFD), and experimental studies. In this context, the experimental NREL Phase VI model with S809 airfoil is one of the most commonly studied HAWT in the past. HAWTs are primary devices in harvesting wind energy. The performances of HAWTs are improved through the evaluation of the different systems in the turbine blade design. Bai and Wang [3] performed a review on several analysis techniques (numerical and experimental) employed in the design of HAWTs. The analysis methods fall into experimental and numerical (computational) categories based on previous literature. The numerical procedures reviewed include classical BEM, modified BEM, CFD, and BEM-CFD whereas the experimental ones include wind tunnel experiment and field tests. The weaknesses and strengths of these methods have been discussed comparatively. Comparative investigation of computational and experimental procedures can aid to improve performance prediction of wind turbines and yield optimal blade design option and flow visualization. Moreover, current computational methods and future research directions have been discussed in [3]. Sun and Zhang [16] demonstrated the ability to accomplish numerical simulation of unsteady airflow around HAWT blades with the help of the ANSYS Fluent package and its sliding mesh capability. The turbine considered was the phase VI S809 blade of 10 m diameter designed for 10 kW standard power output at a rotational speed of 72 rpm. The total number of cells was 1.8 million and the sliding mesh was handled through user-defined function (UDF). Their results compared well with experiments from NREL UAE wind tunnel tests, hence the demonstrated approach can be used to predict the aerodynamic performance of wind turbine rotor. Bai and Chen [2] have designed a HAWT of 10 kW power output using BEM theory and modified stall model, and numerically simulated the blades to analyze the aerodynamic characteristics and flow structure. They have developed improved BEM theory which used various loss models (stall model, tip-loss factor and stall delay model) for predicting the performance of the turbine. The design conditions of the turbine were based on specified flow conditions of wind speed (10 m/s), tip-speed-ratio, and angle of attack using the S822 set of airfoils whose AR, blade radius, and chord length (c) are main design parameters [2, 14]. They have also

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conducted 3D steady-state RANS simulation with the Spalart–Allmaras turbulence model using commercial CFD code on 2.4 million cells. For rated wind speed of 10 m/s, comparison of the results show CFD as a better approach over improved BEM for the investigation of HAWT blades. Mohamad [14] performed aerodynamic simulations of steady and unsteady flow over NREL reference wind turbine called controls advanced research turbine (CART) which is designed for a rated power of 600 kW. The machine is designed for a specific airfoil family, S809. The method is based on BEM which used an open-source code, AeroDyn, developed by NREL. Results were verified against a finite element analysis. Power spectral density model has been applied to capture turbulence and unsteady effects on the blade. Components of normal and tangential forces, as well as lift coefficient, were determined. Anjuri [1] presented computational results for a 3D CFD model of NREL Phase VI turbine rotor with tip plate. The rotor was stall regulated, producing 20 kW power with full span pitch control. A single rotor was modeled with a periodicity using ANSYS CFX commercial CFD RANS solver. Transition model was applied along with steady wind condition without shear. The basic blade constitutes similar configurations with 0◦ yaw angle at the root and 3◦ pitch angle at the tip and with no tower and nacelle. The thrust, mechanical power, as well as spanwise force distributions were compared well with findings from experimental measurements showing the capability of the approach in extracting 3D aerodynamic effects. Bergman and Iollo [5] presented a methodology which reveals how to estimate output power that a HAWT can extract from the wind as a function of upstream wind. They used the standard two-bladed S809 airfoil as a test case due to the availability of NREL Ames test data and solved the incompressible NS equations on a fixed Cartesian grid. The rotating blades and mast were modeled by a penalization term in the governing equations. Second-order accurate scheme was chosen in space and time for solving the NS equations, and the collocated fractional algorithm was used for time integration. Even though the thrust evolution curve agreed with experiments, the results of the approach did not show a higher degree of accuracy for the curve of mechanical power versus wind velocity except for showing the same tendency. The discrepancies could be accounted for the fact that boundary layers may not have been accurately computed, turbulence may not have been modeled properly, and there may be issues with domain confinement. However, the drawbacks could be improved by creating a refined zone near the blades and using interpolation of results computed on the bigger domain with a coarser grid. In this paper, we demonstrate a numerical simulation of unsteady airflow over a horizontal axis wind turbine. The type of wind turbine studied is the NREL phase VI turbine. It is an experimental two-bladed HAWT. Simulation was run using the FAST solver [10, 11] with emphasis on power output. The objective of the present work is to obtain an aerodynamic prediction of HAWT blades using the freely available NREL FAST solver for varied parameters and flow conditions. The FAST solver produces outputs very quickly and requires a much lower computational effort as compared with a complete CFD simulation. We aim to assess the accuracy of the code for applications in wind turbines and generate

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aerodynamic data for verification with minimum computational cost and time. The work is part of a project on CFD analysis of wind energy devices. The Phase VI rotor is used to validate the FAST with available benchmarks. Results of the investigation for uniform but time-varying flow conditions are presented in the results and discussion section.

2 Methodology 2.1 NREL FAST Solver FAST is a fatigue, aerodynamic, structural, and turbulence analysis multi-physics engineering software specialized for wind energy applications. The tool has an aero-hydro-servo-elastic capability for full-scale simulation of onshore and offshore HAWTs operating in several conditions. FAST allows one to perform load and stability analysis, and obtain performance data. It helps the development and operation of wind farms involving HAWTs. The computer-aided engineering (CAE) tool is actively developed and maintained by The National Renewable Energy Laboratory of the USA (NREL). FAST is open-source available for free. FAST has been validated by measurements and verified and hence widely employed in the industry, Jonkman [10]. FAST 8, the latest version, consists of several modules for modeling physics efficiently. It utilizes low-order models that reduce computational cost. The modules available in the FAST solver are: 1. 2. 3. 4. 5.

AeroDyn for rotor aerodynamics HydroDyn for hydro-dynamics ServoDyn for control and electrical system dynamics ElastoDyn for structural dynamics, as well as TurbSim for generating turbulent inflow wind.

These modules can be coupled into a tool that enable us to perform aero-hydroservo-elastic analysis of wind turbines. The inputs and outputs connect several of the modules for specialized applications. For example, TurbSim can generate inflow wind field for use by AeroDyn module and then AeroDyn solves for the aerodynamic loads applying its BEM solver, and then ElastoDyn to obtain blade deformations etc. NREL has a software that couples OpenFOAM to FAST called SOWFA which can help in modeling and simulation of entire wind farm. In such a case, OpenFOAM models the wind farm aerodynamics with multiple turbines, involving the aeroelastic as well as wake interactions, and FAST models the turbine dynamics through actuator line model (ALM) (see Max Becker [4] and Jonkman [10]).

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2.2 Blade Element Momentum (BEM) Blade element theory (BET) is a method used to calculate forces on rotating bladed (propellers). The whole blade is first broken down into several small elements, and then calculating the forces on each element of the blade. Then these forces can be integrated along the entire blade and over one rotor revolution. The forces and moments produced by the whole propeller or rotor are then determined without considering the velocity induced on the rotor disk. Momentum theory or actuator disk theory describes the modeling of ideal actuator disk of rotor blades [4]. These techniques provide the designer a quick estimate of size and aerodynamic performance with low cost. They use the principle that the summation of aerodynamic forces in streamwise direction must be equal to the rate of change of momentum of the air which must be equal to the mass flow rate times the overall change in velocity. By equating this force to the force obtained in terms of pressure difference across the rotor, one obtains the flow velocity across the disk to be equal to half the sum of inlet and outlet velocities. Inlet velocity in this case is the freestream velocity and the pressure is freestream pressure in both ends of the streamtube. These theories are directly applied to wind turbines. Other modifications of these theories like single, double, and multiple streamtube models have been employed so far [6, 11]. The disk is a discontinuous surface through which Bernoulli’s equation is not applicable. For high rotor solidities and large tip speed ratios, the simplified 1D momentum equation and other assumptions are not valid. Therefore, the models fail to capture and predict the real phenomenon [7, 17]. Both the above methods are combined into more efficient method called blade element momentum (BEM) method to assuage some of the problems encountered when used individually (example calculating the induced velocities). The FAST solver explained in Sect. 2 uses the BEM [9, 10].

2.3 Performance Parameters In order to describe performance and operation of HAWTs non-dimensional parameters like coefficient of Power (C p ), tip speed ratio (TSR or λ), and solidity (σ ) are commonly used. Power coefficient is the ratio of extracted power to available power. C p estimates the aerodynamic efficiency of lift-based wind turbines. C p = Cq × λ. where, Cq is torque coefficient obtained from simulation. In this case, λ, is the ratio of turbine tip speed to freestream velocity (U∞ ). Cp =

P 1 3 A ρU∞ 2

.

(1)

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Ct , the torque coefficient is given as, Ct =

CL =

CD =

T 1 2 A ρU∞ 2

L 1 ρUr2 A 2

D 1 ρUr2 A 2

(2)

(3)

(4)

where, C L and C D are coefficients of lift and drag forces and Ur is relative velocity vector, ρ is density of the fluid, and A is the area swept by the wind turbine.

3 Results and Discussion NREL phase VI is a two-bladed experimental horizontal axis wind turbine for performing unsteady aerodynamics experiment. This turbine has been extensively used for testing purposes. It is a 10 kW, 10 m diameter standard turbine rotating at 72 rpm. The blades are twisted and tapered and the airfoil section is made of S809 except for the root. Tilt angle and cone angle are both 0◦ . The turbine was simulated for wind speeds of (5–25) m/s in a simple upwind configuration perpendicular to the rotor plane. We compute unsteady flow simulation over the NREL’s two-bladed HAWT turbine. The output of FAST solver via VTK files was used here to visualize the full turbine geometry given in Fig. 1. To run one simulation. The amount of time that FAST takes for converged results of one simulation is 368.00 s on intel core i7 computer with 8 GB of RAM. The model surface geometry of the blades is shown in Fig. 2. It was generated from coordinates information given by NREL software and main the features are shown in Table 1. Figure 3 (part a) plots the rotor torque versus wind speed. It starts from the lowest value of torque at 5 m/s, shows a steady increase of torque upto the maximum value at 11 m/s wind speeds. Figure 3 (parts b, c, d) describes other performance coefficients and parameters of the turbine. The evolution in the pattern of the plots can be observed relative to the torque. FAST uses an input file about the wind information which is in the form of a module called InflowWind. This module contains options for several different types of wind conditions. These types include steady wind, user-defined, binary TurbSim, and uniform wind. We first run this module for uniform wind type of ramped winds (5–25 m/s) with no shear effect, for which the results are indicated in Figs. 3 and 4. Uniform wind file is used to create (by the user) uniformly time-varying wind conditions at hub height as opposed to the steady wind which is internally calculated using steady conditions of the wind.

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Fig. 1 NREL phase VI wind turbine in paraview from FAST output

Fig. 2 NREL phase VI blade 3D model

In Fig. 4, we compare the numerical power curve obtained employing NREL FAST code with experiment and with three other standard CFD packages used earlier by other authors. The validation of results with experiment shows very good accuracy upto the location of maximum power and have some variation afterward. The CFD results of Fluent [16], Star CCM+ [18], and Ellipsys3D [17] match well with FAST. However, the verification with Fluent code show some discrepancies at higher as well as lower wind speeds, with FAST predicting the higher value of output power at high wind speeds and lower power at lower wind speeds. For wind speeds above 12

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Table 1 Parameters for NREL HAWT CFD model Parameters Value Parameters Airfoil Chord length, C Diameter of turbine, Dt Number of blade, N Reynolds number, Re

S809 0.737 m 10.058 m 2 ≈ 0.5 mil

Freestream TI Freestream velocity Rotational speed,  Tip speed ratio Pressure at outlet

Value 0.5 5–25 m/s 72 RPM Calculated 0 pa

Fig. 3 Rotor power versus wind speed, Method: NREL FAST flow solver

m/s, this particular turbine showed separation and stalled condition on the surface of its airfoil blade as shown by Yelmule [18]. The maximum power is in the order of 10 KW and occurs in the maximum wind speed range which is between 10 and 13 m/s [4, 16].

4 Conclusions The NREL FAST solver uses BEM theory, an iterative analysis and design technique, applicable only for HAWT. BEM is a mix of two theories, blade element theory and momentum theory, each formulated based on certain assumptions, combined to produce a set of equations [8] and solved iteratively. This method is simple to understand, computationally cheap, and efficient. It depends on the availability of

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Fig. 4 Comparison of rotor power of FAST with experiment and CFD. The results have been taken from references [2, 16, 18]

airfoil data for lift and drag coefficients as a function of angle of attack for certain Reynolds number. BEM is dependent on airfoil data and empirical relations which is considered to be the main limitation. In this paper, we employ the standard NREL FAST computational technique to calculate the power output of the NREL phase VI horizontal axis wind turbine (HAWT) rotor. The rotor is a two-bladed, 10 m diameter, and 10 kW HAWT with S809 airfoil blade profile. We simulate uniformly time-varying wind flow and the results of the given wind condition (read externally from a file) show a very good agreement with experiment as well as with many of the CFD solvers for unsteady aerodynamics. The work attempted in this section is part of a project on CFD analysis of wind energy devices, and the results obtained using FAST would help toward a better understanding of the field and efficient use of computational tools to solve similar problems. FAST has a scope to be improved further by coupling with other solvers such as OpenFOAM for which OpenFAST and SOWFA codes are two examples. The unsteady flow simulation was computed using the solver, and the results are in good agreement with those published in the literature. Acknowledgements The authors are thankful to Indian Institute of Technology Bombay for supporting this work.

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References 1. Anjuri, E.V.: Comparison of experimental results with cfd for nrel phase vi rotor with tip plate. Int. J. Renew. Energy Res. (IJRER) 2(4), 556–563 (2012) 2. Bai, C., Hsiao, F., Li, M., Huang, G., Chen, Y.: Design of 10 kw horizontal-axis wind turbine (hawt) blade and aerodynamic investigation using numerical simulation. Procedia Eng. 67, 279–287 (2013) 3. Bai, C.J., Wang, W.C.: Review of computational and experimental approaches to analysis of aerodynamic performance in horizontal-axis wind turbines (hawts). RSE Rev. 63, 506–519 (2016) 4. Becker, M.: fastfoam-an aero-servo-elastic wind turbine simulation method based on CFD (2017) 5. Bergmann, M., Iollo, A., Ouest, I.B.S., Team, M.: Numerical simulation of horizontal-axis wind turbine (HAWT). In: International Conference on Computational Fluid Dynamics (ICCFD7), vol. 1 (2012) 6. Chen, G., Gu, C., Hajaiej, H., Morris, P.J., Paterson, E.G., Sergeev, A.: Openfoam computation of interacting wind turbine flows and control (i): free rotating case (2014) 7. Digraskar, D.A.: Simulations of Flow Over Wind Turbines (2010) 8. Ingram, G.: Wind Turbine Blade Analysis Using the Blade Element Momentum Method. Version 1.1. Durham University, Durham (2011) 9. Jeromin, A., Bentamy, A., Schaffarczyk, A.: Actuator disk modeling of the mexico rotor with openfoam. In: ITM Web of Conferences, vol. 2, p. 06001. EDP Sciences (2014) 10. Jonkman, B., Jonkman, J.: Fast v8. 16.00 a-bjj. NREL (2016) 11. Jonkman, J., Hayman, G., Jonkman, B., Damiani, R., Murray, R.: Aerodyn v15 User’s Guide and Theory Manual. NREL, Golden, CO, USA (2015) 12. Liu, J., Lin, H., Zhang, J.: Review on the technical perspectives and commercial viability of vertical axis wind turbines. Ocean Eng. 182, 608–626 (2019) 13. Micallef, D., Van Bussel, G.: A review of urban wind energy research: aerodynamics and other challenges. Energies 11(9), 2204 (2018) 14. Mo, J.O., Lee, Y.H.: CFD investigation on the aerodynamic characteristics of a small-sized wind turbine of NREL Phase VI operating with a stall-regulated method. J. Mech. Sci. Technol. 26(1), 81–92 (2012) 15. O’Brien, J., Young, T., O’Mahoney, D., Griffin, P.: Horizontal axis wind turbine research: a review of commercial CFD, FE codes and experimental practices. Progr. Aerosp. Sci. 92, 1–24 (2017) 16. Sun, Y., Zhang, L.: Numerical simulation of the unsteady flow and power of horizontal axis wind turbine using sliding mesh. In: 2010 Asia-Pacific Power and Energy Engineering Conference, pp. 1–3. IEEE (2010) 17. Vermeer, L., Sørensen, J.N., Crespo, A.: Wind turbine wake aerodynamics. Progr. Aerosp. Sci. 39(6–7), 467–510 (2003) 18. Yelmule, M.M., Vsj, E.A.: CFD predictions of NREL Phase VI rotor experiments in NASA/AMES wind tunnel. Int. J. Renew. Energy Res. (IJRER) 3(2), 261–269 (2013)

Do Energy Policies with Disclosure Requirement Improve Firms’ Energy Management? Evidence from Indian Metal Sector Mousami Prasad

1 Introduction India’s target to reduce its emission intensity by 33–35% by the year 2030 (on the year 2005 levels) has been placed within the broad objective of keeping the global average temperature below 2 °C. Industrial activities are one of the significant sources of environmental degradation. Of the many environmental pressures, CO2 emissions are one of the most significant contributors to climate change. India alone emitted 6.6% of global CO2 in the year 2014, of which a significant source is due to the increase in consumption of fossil fuel sources of energy. As per sectoral emission inventory using energy consumption, industries in India, emit around 30% of CO2 . In this regard, energy policies play a significant role. These policy instruments could be economic, regulatory or information-based. In India, many studies have examined the role of regulations (command and control) on reducing firms’ impact on the environment [1–4]. Institutional pressure like regulations is supply-side instruments that reduce firm-level emissions by increasing the marginal penalty cost. There is also an increase in the number of studies arguing for improvement in firms’ disclosures to bring transparency and improve the accountability of the firms towards the environment. Following this, there have been energy policies that are informationbased. The policies require disclosures to be made by firms that range from general to specific and can take the form of Business Responsibility Report (BRR), Corporate Social Responsibility report (CSR) and energy consumption details as part of Perform Achieve Trade (PAT) mechanism. However, the empirical evidence on the role of energy policy with disclosure component on low carbon growth provides mixed evidence in developed nations and is very few in developing nations. In India, most of the studies have examined the environmental impact in the form of water pollution [2, 3] and emissions [5, 6]. M. Prasad (B) IIT Bombay, Mumbai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_16

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Within emissions, studies have mostly examined the regulated emissions like SPM, SO2 and NOx [6–8] and only a few have examined CO2 [1, 9]. This study, therefore, extends previous studies and examines the role of disclosure-based energy policy on low carbon growth using data from metal firms. For this, the study first builds a firm-level CO2 emission inventory using fossil fuel consumption (direct emission) and electricity use (indirect emission). The calculated emissions are used to measure emission intensity: an indicator to examine low carbon growth. The impact of energy policy with varying disclosure component on emission intensity is modelled using the economics of emission framework. The results find that energy policy with disclosure requirements have a positive impact on the reduction of emission intensity of firms in the Indian metal sector. Further, the policy with specific quantitative disclosure is significant under various model conditions. This suggests that energy policy with disclosure components may be used as supplementary policy instruments for low carbon growth. The rest of the paper is organized as follows. Section 2 presents the literature review and conceptual framework followed by Methodology in Sect. 3. Results and Discussion are presented in Sect. 4 and conclusions along with limitations and scope for future work are discussed in Sect. 5.

2 Literature Review 2.1 The Institutional Context of Energy Policy with a Disclosure Component in India India uses a command and control approach to regulate the pollutants emitted by industries through Air Act (1981), Water Act (1974). For instance, Central Board of Pollution Control (CPCB) has prescribed emission range of SO2 , NOx and PM in iron and steel sector for each unit, like coke oven, a sintering plant, blast furnace, basic oxygen furnace, rolling mills, arc furnaces, induction furnace [10]. Similarly, for plants in the aluminium sector, emission of particulate matter is prescribed for aluminium plant and smelter plant. Emission of SOx , NOx though is not explicitly prescribed for the aluminium sector [11]. However, the status of regulatory compliance by Indian firms has attracted a lot of criticism on account of poor execution and monitoring. Further, the information provided by the regulatory body like central/state pollution control boards (CPCB/SPCB) is found to be not publically available and there is a lot of difficulty in procuring the data through Right To Information (RTI) Act [6]. As a result, in recent times there has been a focus on information-based policy instruments. The disclosures may be made either as part of the annual report or as a standalone report. The policies with the disclosure component aimed at improving the accountability and transparency of firms. There are presently three mechanisms that cover energy-related information disclosure for Indian firms.

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First, Business Responsibility Report (BRR): Capital market regulator, Securities and Exchange Board of India (SEBI) in the year 2012 mandated the top 100 firms based on market capitalization to publish information on their responsible business practices. The report is called Business Responsibility Report (BRR) and is based on the principles of National Voluntary Guidelines (NVG) on Social, Environmental and Economic Responsibilities of Business. The disclosure requirement includes information on energy use per unit of product and what has been the reduction achieved by firms in their value chain; initiatives in clean technology, energy efficiency, renewable energy; whether advocated in the advancement of energy security. Some of this information is specific but optional while some require an answer in yes/no format or general discussion. The information may be published as part of their annual report, effective from March 2012 onwards [12]. The coverage of firms was later increased to include 500 firms in the year 2015. Second, Perform Achieve and Trade (PAT): PAT is a market-based instrument under National Mission on Enhanced Energy Efficiency (NMEEE) as part of National Action Plan on Climate Change (NAPCC) [13]. PAT requires firms to reduce their specific energy consumption and covered 478 facilities from eight energy-intensive sectors (aluminium, cement, chor-alkali, fertilizer, iron and steel, pulp and paper, textiles and thermal power plants) over time-period 2012–2015. As a result of the PAT mechanism, firms have to disclose their specific energy consumption over the three year period. The information provides the details of the plant/firm, their location, specific energy consumption (TOE/Ton of Product) and Product Output (Ton) both for baseline year and target year. Finally, Companies Act 2013: The Act mandates firms to incur expenditure on social responsibility and disclose the same as the annual report of corporate social responsibility, effective form year 2014. The areas that a firm can engage as part of social responsibility are also based on NVG, similar to BRR requirement. However, as the social responsibility expenditure is directed towards society, the activities cannot be the normal course of business. As a result, there may not be a direct impact on the firm’s CO2 emissions. Some studies, on the other hand, note a positive impact of engaging in social responsibility and reduction of their impact on the environment. A brief summary of these policies is provided in Table 1. Table 1 Comparison across energy policies with disclosure component Characteristics

DISSEBI

DISCSR

DISPAT

Information required

Quantitative and qualitative

Quantitative and qualitative

Quantitative

Format specified

Yes

Yes

Yes

Periodicity

Annual

Annual

Every three years

Nature

General

General

Specific

Target

Firm

Firm

Plant

Launched

2012

2013

2012–2015 (first cycle)

Source Author’s Summarization

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As seen in Table 1, there is a similarity between the structure of disclosures to be made business responsibility requirement and CSR requirement. Further, for both these policies, the areas that qualify for disclosures are based on National Voluntary Guidelines. This suggests strong complementarities amongst the two.

2.2 Economics of Emission Firms’ production emits CO2 emissions, which is a serious threat to climate change. Energy-intensive industries are particularly responsible for the emissions as they meet their energy needs through fossil fuels. The environment has the capacity to absorb emissions. The firms may use this environmental capacity to discharge their entire emissions or a part of their emissions. However, in case of policies that regulate emissions, the firm will not discharge all the emissions in the environment, for the fear of attracting a penalty. Policies whether in the form of regulations; economic instruments and information; aim at reducing the supply of emissions in the environment. For the firm, the supply curve for emissions is given by the marginal penalty curve. The marginal penalty increases with a higher level of emissions, requiring the firm to pay extra to make an additional unit or emission [14]. The demand curve of emissions is given by the marginal abatement cost, which reduces at a higher level of emissions, making it a downward sloping curve. Marginal abatement cost is influenced by factors like the skilled workforce, environment management system employed by firms. Most of the past studies in India have examined the role of supplyside variables in reducing the firms’ impact on the environment. Within this group, a large number of studies has examined the role of regulations on water pollution [2] and some on air pollution. However, the role of information policy remains less investigated even though the disclosures are being argued as supplementary policy tools to limit emissions. Further studies examining the impact on policies on climate change is severely limited. Therefore, using the economics of emission framework, I hypothesize that there is a significant positive relationship between energy policy with disclosure component and reduction in emissions.

3 Research Methodology 3.1 Data Sample I use metal firms as sample owing to the significant contribution to the economic growth of India at around 2% of GDP and energy profile of the industry. Within the industrial emissions in India, the metal sector is one of the significant emitters of CO2 contributing more than half of the emissions over the last several decades. Also, the firms in the metal sector are classified under red category by pollution control

Do Energy Policies with Disclosure Requirement Improve …

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boards on account of high pollution intensity. The sector meets most of the energy requirement from fossil fuel sources of energy that adds to industrial emissions. The firms from the metal sector are selected based on the National Industry Classification (NIC). NIC classifies manufacture of basic metals as (a) Manufacture of basic iron and steel (Group 241), (b) Manufacture of basic precious and other non-ferrous metals (Group 242) and (c) Casting of metals (Group 243). The metal firms were shortlisted on the basis of the following criteria: (a) The firms should have published annual reports during the financial year 2006– 07 to 2014–15. (b) The firms should have disclosed information on fuel-wise energy consumption. The time period of study has been selected as years 2006–07 to 2014–15 to include an exhaustive list of metal firms. The nine-year period in the present study, saw many environmental policies being introduced; the effect of which is likely to be captured in the analysis. The process resulted in an unbalanced panel for 121 metal firms over 2006–07 to 2014–15. The sample firms are representative of the Indian metal sector. For instance, in the iron and steel sector, the sample of 76 firms in 2006-07 produce more than 70% of the nation’s crude steel. Further, within these 76 firms, six firms produce 53% of nation’s finished steel [15]. Most of the firms in group category 242 are manufacturers of aluminium followed by copper and other non-ferrous metals like zinc and lead. The sample firms produce 55% of the nation’s aluminium, 60% of the nation’s copper cathode and 90% of the nation’s zinc. The main producers in aluminium metal include public sector firm National Aluminium Company Limited (NALCO) and Hindalco Industries. Further, Hindalco is also the largest producer of copper cathode and Hindustan Zinc is the largest producer of zinc and lead [16].

3.2 Variables and Their Measurement I examine the role of disclosure-based energy policy on low carbon growth using the economics of emission framework. Low carbon growth is indicated by CO2 emission intensity. In absence of firm-level emission inventory, I calculate the emissions from energy consumption details following the steps of Prasad and Mishra [9], which includes classification of all energy consumption sources into solids, liquids and gases. After this, the emissions factors for respective fuels are arrived using their net calorific values, carbon content and oxidation factor. The resulting carbon emission coefficient used in this study are 2.242 (a ton of CO2 per ton of solid fuel), 3.089 for (a ton of CO2 per ton of liquid fuel) and 0.002 for (a ton of CO2 per ton of gaseous fuel). These emission coefficients are then multiplied by the energy consumption of fuels to arrive at CO2 emissions.

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CO2 Emissions =

n 

Cit (eCO2i )

i=1

where i is the type of energy used (solid, liquid and gas); t is the year of consumption; eCO2i is the CO2 emission coefficient for i type of energy consumed; The resulting CO2 emissionsit of the firms i in year t is divided by the total assetsit of the firm i in year t to arrive at CO2 emission intensity of an individual firm in a particular year. Disclosure-based energy policy: measured as a binary variable taking a value 1 if the firm is covered under the three energy policy discussed in the previous section; (a) business responsibility report requirement (DISSEBI) (b) corporate social responsibility disclosure requirement (DISCSR) and (c) perform achieve trade disclosure requirement (DISPAT). Of 121 sample firms, 71 firms are covered under either of the energy policy. 17 firms are covered under PAT, 63 under CSR rules and six firms under business responsibility report. Further, 13 firms are covered under both CSR and PAT both, and two firms under all three policies. As discussed in the theoretical framework of the economics of emission, the relationship between disclosure-based energy policy and emission intensity may also be influenced by firm characteristics. I include the firm characteristics like size, age of assets, research and development expenses and labour productivity as control variables. These variables have been found to be significant in influencing the firms’ emission profile in previous studies [1, 17–19]. Size of the firm is measured as natural log of sales. Age of assets is measured as gross fixed assets divided by total gross assets. Research and development expenditure is a binary variable taking a value of 1 if the firm discloses their research expenses, 0 otherwise. Labour productivity is measured as wages and salaries divided by sales. In addition, some studies have found that voluntary compliance like environment standard is a demand-side instrument that helps firms lower their marginal abatement costs [9, 17, 18], and hence the study includes ISO 14001 as an additional control variable. ISO 14001 is a binary variable that takes the value of 1 if any plant/firm is compliant with ISO 14001 and 0 otherwise. The data for company financials are obtained from CMIE Prowess and those pertaining to CSR regulation are from Ministry of Corporate Affairs website, and PAT disclosure from Bureau of Energy Efficiency website.

3.3 Model Specification The impact of disclosure-based energy policy regulation on emission intensity is modelled using fixed-effect model. Fixed-effect model controls for the endogeneity concerns arising from firm characteristics that vary across firms but remains constant over time [17]. CO2 emission intensityit = a0 + a1 EDRit + a2 qit + a3 vi + n it

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where i = firm, t—time period, EDR—energy policy with disclosure requirement; qit —firm-specific variables, vi —firm fixed-effect variables; nit —error term with zero mean and constant variance.

4 Results and Discussion Around 60% (71 out of 121) metal firms are covered by at least one of the three energy policies based on disclosure requirements. The results of fixed-effect regression of various energy policies on emission intensity are presented in Table 2. I have estimated the impact of energy disclosure regulation under three different assumptions. In Model 1, the basic relationship between various disclosure regulations and emission intensity is examined. In Model 2, I introduce the firm characteristics as control variable, and finally in Model 3, I include the year dummies as well. As seen in Table 2, the coefficient of DISCSR is significant and negatively related to emission intensity. This suggests that energy disclosure regulation of CSR alters the behaviour of firms and results in lower emissions, helping the firms achieve low carbon growth. The other two disclosure variables namely DISSEBI and DISPAT, however, are not significant. A plausible reason could be that only a small proportion of firms are covered under SEBI regulation and PAT mechanism. In the case of SEBI regulation, for the years 2012–2014, only the top 100 firms were required to submit a business responsibility report. When compared to the study sample, only six firms are covered. PAT, on the other hand, is targeted at the plant level, which, when aggregated to the firm level, reduces the number of firms. As the CSR and BRR requirements are both based on similar principles, even if one of them is significant, it suggests that the energy disclosure requirement has a positive impact on the firm’s energy management. Apart from disclosure regulations, the other firm characteristics like age of assets and ISO 14001 is negatively associated with emissions suggesting that firms with younger assets and environment management system have lower emissions intensity. R&D expenses, however, are not significantly associated with emissions. Size, on the other hand, has a non-linear relationship with emissions. Firms with smaller sizes have higher emissions and as the firm becomes larger, the economies of scale and scope help firms lower their emissions.

5 Conclusion Managing economic growth along with concerns of climate change is a challenge particularly for developing nations like India. In order to achieve low carbon growth for industries, it is required that climate responsibilities are fixed at the firm level. In this regard, there have been several policies with disclosure requirements for firms like Perform Achieve Trade, Business Responsibility Report and Corporate Social

166 Table 2 Fixed effect regression

M. Prasad Variables

Model 1

Model 2

Model 3

DISSEBI

0.086

0.197

0.144

(0.129)

(0.153)

(0.144)

−0.482***

−0.453***

−0.294**

(0.102)

(0.110)

(0.122)

−0.019

0.012

0.058

DISCSR DISPAT

(0.139)

(0.136)

(0.134)

−1.393***

−2.348***

(0.428)

(0.490)

Labour productivity (log)

0.017

0.026**

(0.010)

(0.010)

Size (log)

0.532***

0.426***

(0.143)

(0.134)

ISO 14001

−0.345***

−0.114

(0.090)

(0.101)

0.064

0.077

Age of assets (log)

R&D Size (log)2

(0.108)

(0.115)

−0.031***

−0.013

(0.009)

(0.008) −0.124***

D1

(0.035) D2

−0.279***

D3

−0.260***

D4

−0.359***

(0.049) (0.060) (0.068) D5

−0.599***

D6

−0.558***

D7

−0.547***

(0.081) (0.106) (0.101) −0.753***

D8

(0.107) Constant

−4.416***

−6.720***

−7.159***

(0.012)

(0.741)

(0.749) (continued)

Do Energy Policies with Disclosure Requirement Improve … Table 2 (continued)

167

Variables

Model 1

Model 2

Model 3

Observations

1001

1001

1001

R-squared

0.100

0.153

0.267

Number of com

121

121

121

Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1

Responsibility Report. These disclosure policies cover energy, in addition to other environmental and social areas. Based on the study sample for metal firms, the present study examined and finds a positive significant impact of disclosure-based energy policy in helping firms lower their emissions. However, as the policies are based on the size of firms and their energy consumption, around 40% of sample firms are not covered by any of the alternative disclosure policy. A case is therefore made for expanding the coverage of the policies to smaller firms as well. Also, there is a need for effective execution and monitoring of disclosure regulation, such that it does not lead to becoming a tool for impression management by firms. In this regard, specific quantitative information should be encouraged by firms. The study has some limitations. The study used metal sector as a sample. Future studies may examine the disclosure regulation applicable to other energy-intensive sectors. In addition, the firms used in the study are listed firms. However, emissions are also made by firms that are not listed. This may be examined in future studies.

References 1. Mandal, S.K., Madheswaran, S.: Environmental efficiency of the Indian cement industry: an interstate analysis. Energy Policy 38(2), 1108–1118 (2010) 2. Murty, M.N., Kumar, S.: Win–win opportunities and environmental regulation: testing of porter hypothesis for Indian manufacturing industries. J. Environ. Manage. 67(2), 139–144 (2003) 3. Dutta, N., Narayanan, K.: Impact of environmental regulation on technical efficiency a study of chemical industry in and around Mumbai. Sci. Technol. Soc. 16(3), 333–350 (2011) 4. Kathuria, V.: Public disclosures: Using information to reduce pollution in developing countries. Environ. Dev. Sustain. 11(5), 955–970 (2009) 5. Murty, M.N., Kumar, S.: Measuring productivity of natural capital. In: Tendulkar, S.D., Mitra, A., Narayanan, K., Das, D.K. (eds.) India: Industrialisation in a Reforming Economy, Essays for K. L. Krishna. Academic Foundation, New Delhi (2006) 6. Shetty, S., Kumar, S.: Are voluntary environment programs effective in improving the environmental performance: evidence from polluting Indian Industries. Environ. Econ. Policy Stud. 19(4), 659–676 (2017) 7. Murty, M.N., Kumar, S., Dhavala, K.: Measuring environmental efficiency of industry: a case study of thermal power generation in India. Environ. Resour. Econ. 38(1), 31–50 (2007) 8. Murty, M.N., Gulati, S.C.: Accounting for Cost of Environmentally Sustainable Industrial Development in Measuring Green GDP: A Case Study of Thermal Power Generation State of Andhra Pradesh in India. New Delhi, E/253/2005 (2004)

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9. Prasad, M., Mishra, T.: Low-carbon growth for Indian iron and steel sector: exploring the role of voluntary environmental compliance. Energy Policy 100, 41–50 (2017) 10. Ministry of Environment and Forests: Ministry of Environment and Forests, Notification. Regd. NO. D.L-33004/99 (2012) 11. Ministry of Environment and Forests: The Environment (Protection) Act, 1986. New Delhi (1986) 12. Securities and Exchange Board of India: SEBI (2012) Business Responsibility Reports (2012) 13. Bureau of Energy Efficiency: Perform, Achieve & Trade (PAT) (2011) 14. Dasgupta, S., Hettige, H., Wheeler, D.: What improves environmental compliance? Evidence from Mexican industry. J. Environ. Econ. Manage. 39(1), 39–66 (2000) 15. Joint Plant Committee: Secretary’s DO report (Flash Report)—March 2017 (FY2016–17), Kolkata (2017) 16. Ministry of Mines: Monthly Summary on Non-ferrous Minerals and Metals March 2015 (2015) 17. Yang, X., Yao, Y.: Environmental compliance and firm performance: evidence from China. Oxf. Bull. Econ. Stat. 74(3), 397–424 (2012) 18. Nishitani, K., Kaneko, S., Fujii, H., Komatsu, S.: Are firms’ voluntary environmental management activities beneficial for the environment and business? An empirical study focusing on Japanese manufacturing firms. J. Environ. Manage. 105, 121–130 (2012) 19. Goldar, B.: Energy intensity of Indian manufacturing firms: effect of energy prices, technology and firm characteristics. Sci. Technol. Soc. 16(3), 351–372 (2011)

Power Management of Non-conventional Energy Resources-Based DC Microgrid Supported by Hybrid Energy Storage Jaynendra Kumar, Anshul Agarwal, and Nitin Singh

1 Introduction Human society requires an increasing amount of energy for domestic, commercial, agricultural, industrial and transport uses. Energy can be got from renewable and non-renewable energy sources. Non-renewable sources such as natural gas, coal and petroleum are very effective as far as the power production quality is concerned. Due to this reason, they have been conventional sources for power generation. But these sources are available in finite amount, and they are decreasing day by day [1]. Therefore, an alternative solution for power production is required. Continuous research and development in solar energy conversion technologies have made solar energy an efficient and economical source for electricity generation [2, 3]. To observe the solar energy potential in India, Indian government has set the target to generate electricity of 175 GW from renewable energy sources out of which 100 GW is from solar only. Applications of solar energy are water pumping, commercial building, and residential homes and space telecom mainly [4]. Another way to utilize non-conventional resources is to develop efficient energy conversion devices like fuel cells. Fuel cells are compatible with other conventional and non-conventional primary power sources. The electrical response time of an FC is generally fast, being mainly associated with the speed at which the chemical reaction is capable of restoring the charge that has been drained by the load. Because an FC system is composed of many mechanical devices, the whole FC J. Kumar (B) · N. Singh MNNIT Allahabad, Prayagraj 2110004, India e-mail: [email protected] N. Singh e-mail: [email protected] A. Agarwal NIT Delhi, New Delhi, Delhi 110040, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_17

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system has a slow transient response as well as slow output power ramping [5]. An energy storage system is an essential part of DCMG for stable and reliable operation with the intermittent energy sources like solar and variable loads when it is operating in islanded mode. DCMG net power can be categorized into two fractions: high-frequency components (fast power fluctuations) and low-frequency components (slow power fluctuations). Due to unavailability of a storage device which can handle both types of power fluctuations, HESS has been coming up with the effective and economical solution [6, 7]. DC system is superior over the AC system, especially for non-conventional sources like solar PV and fuel cell [8, 9]. So here, a DC microgrid (DCMG) system is proposed. However, some authors have discussed the performance of solar and fuel cells or solar with the battery storage or solar with the hybrid energy storage system separately. In this paper, the proposed system includes a hybrid energy source (one intermittent and one reliable) with the hybrid energy storage (battery: low power density and high energy capacity and SC: high power density and low energy capacity).

2 DC Microgrid System An islanded DCMG is a complete set of sources, storage system, interconnecting devices and loads. The systematic diagram of the proposed system has been shown in Fig. 1.

V fc

Charge Controller FC controller

Ifc

GFC

GS C1

GS C2

Boost converter

BDC

Fuel Cells Super-Capa citors Module

Boost converter

Charge Controller GBAT1

PV Array

Vpv Ipv

GPV MPPT Contr oller

GBAT2 BDC

Batter y-Banks Module

DC load 3kW + 2kW – 2kW

Fig. 1 Systematic diagram of proposed DCMG system

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Non-conventional energy resources include renewable sources, e.g. solar, wind, geothermal and ocean energy, etc., and energy conversion devices, e.g. fuel cells, etc. The proposed system includes both types of sources, i.e. renewable source, solar, and energy conversion device, fuel cells. To extract maximum power from the PV system, an efficient MPPT algorithm is essential. Two most developed hill-climbing MPPT algorithms are perturb and observe (P&O) and incremental conductance (INC). There is no general conclusion in the literature on which one of the two algorithms is the best one. Some literature suggests that the INC is a little more efficient compared to P&O [10, 11]. Therefore, here INC algorithm has been chosen for the PV system. The principle of the algorithm can be understood by the flow chart and the mathematical expression which is given in Fig. 2 and Eq. (1), respectively. δINC = (I /V ) + (I /V )

(1)

The sign of δINC decides the direction of next perturbation. At MPPT, δINC is zero. Fuel cells are a conversion device which converts chemical energy directly into electrical energy. Fuel cells work on electrochemical reactions (oxidation and reduction) of hydrogen with the oxygen. Various types (based on operating temperature Start Sense Vpv(k),Ipv(k) ∆V = Vpv(k)-Vpv(k-1) ∆I = Ipv(k)-Ipv(k-1)

∆V = 0

Yes

No Yes

Ipv + (∆I/∆V)Vpv = 0 No

Yes

Ipv + (∆I/∆V)Vpv > 0 No

Increase Vpvref

Decrease Vpvref

∆I = 0

Yes

No Yes

∆I > 0 No Increase Vpvref

Decrease Vpvref

Update history Vpv(k-1) = Vpv(k) and Ipv(k-1) -Ipv(k)

Fig. 2 INC algorithm for the PV system

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and fuel type) of fuel cells are used according to the application [5]. Due to the high power density of proton exchange membrane fuel cells (PEMFCs), it has been preferred choice for power generation of the developers and chosen for the proposed system. Chemical reactions for PEMFC take place at anode and cathode, and cell reaction is given in Eqs. (2, 3, 4), respectively [12]. H2 = 2H+ + 2e−

(2)

1 O2 + 2H+ + 2e− = H2 O 2

(3)

2H2(g) + O2(g) → 2 H2 O(l) + energy

(4)

The two primary attributes of storage devices are energy density and power density. Combination of battery and SC has been chosen for the proposed DCMG as HESS because of various reasons such as their availability, relatively low cost, the similarity in working principle and most notably their component attributes over each other’s limitations [7]. The converter that interfaces SC and DC bus is operating in a voltage control (VC) mode to regulate DC bus voltage which relaxes the battery from charging and discharging cycles; as a result, it improves the battery life [6]. The DCMG interfacing devices are DC/DC or DC/AC converters. Here, the unidirectional boost converter is used for the solar PV and FC interface as it works for MPPT PV system also. Bidirectional buck–boost DC/DC converter topology is chosen for integrating the battery and SC due to its various advantages such as its efficient operation, lightweight due to the absence of transformer, simple, easy to control, economical and most importantly compatibility with the system over other bidirectional DC/DC converter topologies [13]. Various parameters of the unidirectional boost converter are calculated by the following equations [14]. VO = VDC =

Vin 1− D

IO = (1 − D)Iin

(5) (6)

L=

Vin × D f × I

(7)

C=

IO × D f × V

(8)

where VO , VDC , Vin , D, IO , Iin , L, C, f , I and V are output voltage of the converter, DC bus voltage, input voltage of the converter, duty ratio of the power devices, output current from the converter, input current of the converter, inductance

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Fig. 3 Bidirectional buck–boost DC/DC converter

S1

D1

L

C DC

V DC

CE

VE

S2

D2

of the converter, capacitance of the converter, switching frequency of the power devices, ripple current of the inductor and ripple in output voltage, respectively. BDC works in two modes; buck and boost [13]. A systematic diagram of the bidirectional buck–boost converter is shown in Fig. 3. In charging mode, DC bus is connected to high voltage side or input (i.e. V DC = V i ). Battery and ultra-capacitors are connected to the low voltage side or output (i.e. V E = V O ). The power flows from the high voltage side to the low voltage side, and converter operates in buck mode. (1 − D)R 2 fS

(9)

(1 − D)   8L f S V Vo

(10)

L criCharging = CDCcri =

In discharging mode, voltage of energy storage systems (V ESS ) is the input voltage (i.e. V E = V i ) and the DC bus voltage (V DC ) is the output voltage (i.e. V DC = V O ). In discharging mode, power flows from the low voltage side to the high voltage side and converter operates in boost mode. L criDischarging =

(1 − D)2 R D 2 fS D 

C Ecri = R fS

V Vo



(11) (12)

where D, R, f S and V are duty ratio of the power devices, internal resistance of the storage device, switching frequency of the power devices and ripple in output voltage, respectively.

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3 Proposed Converter Control Since the solar irradiance and temperature keep changing throughout the day, so control strategy must fulfil the load demand in all conditions. Simultaneously, power should be balanced and microgrid should be stable. The MPPT controller adjusts the voltage by a small amount from the solar PV and calculates the power. If the power increases/decreases, further adjustment is tried until the power no longer increases/decreases. In Fig. 4, solar PV voltages and currents (V PV and I PV ) are continuously sensed and operating point of the DC/DC converter is decided by the slope (I/V-I/V ). When the slope is positive, the duty cycle (D) is increased by D and vice versa. To control the power flow from the fuel cells, a PI controller is developed as shown in Fig. 5. The fuel cell power is compared with the reference power of the fuel cells, and the power difference is divided by the reference voltage which passes through the PI controller to generate an error signal for the PWM generator. It generates the pulses for the unidirectional interfacing converter according to the power variation in DC bus. Charging and discharging of the battery and the SC are decided by variation in instantaneous DC bus voltage from the reference voltage. Variation in the DC bus voltage will govern the quantity of deficit/surplus power. Conversion of the voltage error signal (e(s)) into the power demand/supply reference signal for the battery and SC is presented by the block diagram shown in Fig. 6a. Here, three blocks have been used, namely PI controller, mean and rate limiter. PI block received the error signal as input and gives the manipulated output signal (m(s)). The output signal is ringing in its waveform, and their average value is continuously changing. So, to obtain the plot of moving average value, mean block is utilized. The power deficit/surplus are

Z -1 I PV

-

∆ I

+ +

V PV

Z

-1

∆V -

∆ I/ ∆ V + + I/V

∆D

-∆ D

T >0 F

D

+ -

Z -1

PWM Generator

Limiter

G PV

Fig. 4 MPPT controller for solar PV

P FC ref +

PWM Generator

PI Controller

-

Saturation P FC

V dc

Fig. 5 FC power controller

G FC

Power Management of Non-conventional Energy … V DC +

PI Controller

Mean

175 P BAT_REF

P REF

Rate Limiter

V DC _REF

-

+

P SC _REF

(a) +

P BAT_REF or P SC_REF V BAT or V SC

PI Controller

Z-1

Switch +

-

+

V BAT or V SC

IBAT or ISC -

+

Satutation V DC

T >0 F

+ PI Controller

Z

-1

0

T >0 F

+

PWM Generator

PWM Generator

G BAT2

G SC2

G BAT1 G SC1

Switch

(b) Fig. 6 BDC controller for battery and SC. a Reference generation. b Switching pulse generation

fulfilled by the battery and SC both. The battery response time is not much faster, so in battery power signal passing through a rate limiter blocks to limit the power flow rise into/from the battery, whereas SC response time is fast so it does not require rate limiter. The governing equations are discussed as below: e(s) = |VDC_ REF − VDC |

(13)

ki ) e(s) s

(14)

m(s) = (k p +

where k p and k i are the proportional and integral gains of the PI controller. To control the buck–boost converter operation, it is required to vary the pulse width according to power need/supply from the storage devices. To produce the pulses for the buck–boost converter from the power need/supply (power reference), a block diagram has been given in Fig. 6b, in which power signal is first converted into a current signal by dividing it with the terminal voltage of the storage device and then produces the error signal by comparing it with the battery current or SC. The error signal is passed with the different blocks (i.e. PI controller block then to delay block to summation block to division block to saturation block to switch to PWM generator block) to convert it into pulses to operate the buck–boost converter switch. The PI block manipulates the error signal. It is used because the simplicity in implementation does not require a high computation process and gives a suitable performance. Delay block is inserted here to provide the transport delay. Summation block will add the signal with the battery terminal voltage, and divide block divides the output with the DC link voltage. Saturation block is provided here to limit the

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magnitude of the upper and lower boundary of the signal. Here, the upper limit has been taken 1 and the lower limit zero. Switch block is used to decide the buck–boost converter operation either in buck mode or boost mode. It operates using if-else logic. PWM generator produces the pulses to operate the buck–boost converter. According to the power supply/demand, it adjusts the duty ratio.

4 Results and Discussion In an islanded operation of DCMG, storage devices are the only responsible unit which limits the DC bus voltage and maintains power quality. Equations (15) and (16) are the power balanced equations during charging and discharging conditions. Ppv + Pfc = Pdc + Pbat + PSC

(15)

Ppv + Pfc + Pbat + PSC = Pdc

(16)

The load varies in three steps 3 kW, 5 kW and 3 kW and changes at t = 1 s and t = 2 s, respectively. Transient and steady-state fluctuations are handled by SC. Power variations in load change are balanced by the battery. According to the net power (Available power or power from PV and FC—load demand). After a few transients, PV system output power gets constant. It delivered constant 2 kW and operates on MPPT. However, little glitches are observed during the load change. FC continuously delivers 2.5 kW power, and its response time is fast (Fig. 7a). Figure 7(b) presents PV power, voltage and current variations. Battery and supercapacitor SoC, and voltage and current variations are shown in Fig. 7c, d, respectively. The voltages are almost constant, and fluctuations appear in currents. Voltage and current waveforms of fuel cells and DC link are given in Fig. 7e, f. There are small glitches which are present in the DC link voltage during load change; otherwise, voltage is almost constant. DC link current varies according to the load demand. Voltage and current ripples for the DC link, battery, supercapacitor and FC are calculated from the obtained results as shown in Table 1 at steady state. Voltage ripples are within the permissible limit (1%), which represents the stable operation of DCMG. Current ripple in SC is ≈824%, which represents that fluctuations occur during steady state and are handled by SC.

5 Conclusion In this paper, an islanded DCMG is realized, which has been developed by the solar PV, FC, HESS and three-step variable DC load. Performances of various subsystems such as solar PV and HESS are analysed and discussed, and results are presented.

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(a). Power management of the proposed DCMG system.

(b). Power, voltage and current of the PV system

(c). Battery voltage, current and SOC Fig. 7 a Power management of the proposed DCMG system. b Power, voltage and current of the PV system. c Battery voltage, current and SOC. d SC voltage, current and SOC. e FC voltage, current and SOC. f Stable DC bus voltage and current

Ripples in the output voltage waveforms are within permissible limit. It is observed that HESS is very much compatible with the PV-FC system. The too high current ripples in SC conclude that the steady-state fluctuations are handled by the SC which relaxed battery and improves its life.

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(d ). SC voltage, current and SOC

(e). FC voltage, current and SOC

(f). Stable DC bus voltage and current Fig. 7 (continued)

301.45

48.324

48.002

45.084

DC link

Battery

SC

FC

V max

44.948

48.001

48.314

299.05

V min

45

48

48.318

300

V avg

Table 1 Ripples in voltage and current waveforms

0.80

0.302

0.002

0.02

Vavg

V Ripple (%) Vmax − V min × 100

55.72

0.97

55.32

−0.05

9.97 −31.2

10.04

I min

−30.4

I max

55.54

0.1238

−30.8

10

I avg

0.72

823.91

0.026

0.70

Iavg

I Ripple (%) Imax −I min

× 100

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References 1. Shahzad, U.: The need for renewable energy sources. Int. J. Inf. Technol. Electr. Eng. pp. 16–18 (2015) 2. Spagnuolo, G., Petrone, G., Araujo, S.V. et al.: Renewable energy operation and conversion schemes: a summary of discussions during the seminar on renewable energy systems. IEEE Ind. Electron. Mag. 4(1) (2010) 3. Rajput, S.K.: Solar Energy: Fundamental, Economics and energy Analysis (2017) 4. Annual report 2017–18: Ministry of New and Renewable Energy. Government of india 5. Farooque, M., Maru, H.C.: Fuel cells—The clean and efficient power generators. Proc. IEEE 89(12), 1819–1829 (2001) 6. Kumar, J., Agarwal, A., Agarwal, V.: A review on overall control of DC microgrids. J. Energy Storage 21, 113–138 (2019) 7. Hajiaghasi, S., Salemnia, A., Hamzeh, M.: Hybrid energy storage system for microgrids applications: a review. J. Energy Storage 21, 543–570 (2019) 8. Kumar, J., Srivastava, S., Agarwal, V.: Power management of solar based DC microgrid system enabled by solid state transformer. In: 14th IEEE India Council International Conference (INDICON), Roorkee India (2017) 9. Sanjeev, P., Padhy, N.P., Agarwal, P.: Autonomous power control and management between standalone DC microgrids. IEEE Trans. Ind. Inf. 14(7), 2941–2950 (2018) 10. Aureliano, M., Brito, G.D., Galotto, L., Sampaio, L.P., Melo, G.D.A., Canesin, C.A.: Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans. Ind. Electron. 60(3), 1156–1167 (2013) 11. Subudhi, B., Pradhan, R.: A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Trans. Sustain. Energy 4(1), 89–98 (2013) 12. Daud, W.R.W., Rosli, R.E., Majlan, E.H., Hamid, S.A.A., Mohamed, R., Husaini, T.: PEM fuel cell system control: a review. Renew. Energy 113, 620–638 (2017) 13. Ravi, D., Reddy, B.M., Shimi, S.L., Samuel, P.: Bidirectional DC to DC converters: an overview of various topologies, switching schemes and control techniques. Int. J. Eng. Technol. 7(4.5), 360–365 (2018) 14. Patil, R., Anantwar, H.: Comparative analysis of fuzzy based MPPT for buck and boost converter topologies for PV application. In: International Conference on Smart Technologies for Smart Nation (SmartTechCon), Bangalore, India (2017)

Sizing of a Solar-Powered Adsorption Cooling System for Comfort Cooling Sai Yagnamurthy, Dibakar Rakshit, and Sanjeev Jain

Nomenclature Ao A1 COP I T η

Optical efficiency of collector Negative first-order efficiency coefficient (W/m2 K) Coefficient of performance Incident radiation on collector (W/m2 ) Temperature (K) Efficiency

1 Introduction Among the various available solar cooling technologies, adsorption cooling technologies have been recommended for small-scale and mobile systems and observed to yield better results for part load conditions than the absorption counterpart [7]. Many researchers have demonstrated the feasibility of solar-powered adsorption cooling systems and studied the influence of various parameters like solar insolation, ambient temperatures, etc., and arrangements like auxiliary heating, mass recovery, etc., on their performances [4, 5, 11]. In order to realize a complete autonomous system, the primary challenge has been the sizing of solar collector and storage system for appropriate demand matching, which requires the use of dynamic simulations [10]. S. Yagnamurthy · D. Rakshit (B) Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India e-mail: [email protected] S. Jain Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_18

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TRNSYS is a commercial software developed by the University of Wisconsin, which has been widely used by researchers for performance assessment and experimental validation. Ali et al. [2] evaluated the performance of a solar adsorption cum desalination system for a solar ETC-powered two-bed silica gel–water system in Assiut, Egypt, using a model developed in TRNSYS with an inbuilt MATLAB interlinking component. Al-Rbaihat et al. [1] conducted experimental and simulation studies on a solar flat plate collector-powered adsorption cooling system for a room cooling application under Jordanian climate. The adsorption chiller performance characteristics were closely matching between TRNSYS and the real system with a maximum percentage deviation of 19.3% in chilled power. Palomba et al. [9] developed a solar heating and adsorption-powered cooling model in TRNSYS and validated the real-time results of the solar air-conditioning and heating system installed in Shanghai Research Institute of Building Science, observing an error of less than 10%. In the current study, an approach has been devised for sizing the solar collector and storage volume systems for a solar-powered cooling system, making use of minimal data such as monthly averages of the daily average cooling load and peak load along with the monthly averages of the daily average solar irradiation and ambient temperature data. This was followed by an economic analysis to arrive at the optimal values of collector area and storage volumes. A dynamic simulation has been done in TRNSYS for the same solar-powered adsorption cooling system for meeting the room cooling load requirements of school building near Roorkee, India. The accuracy of the results obtained using the proposed approach has been crosschecked with the detailed dynamic simulation results obtained from TRNSYS, in terms of solar cooling fractions yielded as well as total costs incurred per unit mass of CO2 mitigation.

2 System Description 2.1 Components of Solar Adsorption Room Cooling System Model in TRNSYS The components considered in this simulation have been taken from the TRNSYS libraries. The major components of the solar room cooling layout shown in Fig. 1 are listed below: Solar thermosiphon collector with integrated storage A thermosiphon evacuated tube collector was used for providing thermal energy input to the adsorption cooling system. The efficiency equation for the collector is given by the following equation:

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Fig. 1 TRNSYS layout of solar adsorption cooling system

η = a0 − a1

(Tin − Tamb ) I

(1)

The collector parameters a0 and a1 are considered with the values of 0.63 and 3.12 W/m2 K, respectively, corresponding to an experimentally tested thermosiphon ETC, with a tilt angle of 30°. Adsorption chiller unit The Type 909 component with the data of a commercial adsorption chiller of 3-ton nominal capacity and 0.6 nominal COP has been used in this simulation. The cooling tower’s air-to-water volumetric flow rate ratio has been fixed at 400, in accordance with the design data of the cooling tower, while the sump volume and airflow fan capacities are 0.5 m3 and 186.4 W, respectively. Cooling Room Macro Type 19 detailed single-zone model has been used for modelling the cooling room geometry and to simulate the cooling load conditions. The dimensions of the room are 8.5 m × 6 m × 3.5 m. The room is oriented at 26 degrees from north towards west direction. The ventilation rate of the room is 0.09 m3 /s as per ASHRAE Standard 62.1 [3]. The room has a total occupancy of 30 people. The occupancy hours of the room are from 8 AM to 4 PM for 6 days a week. The occupancy reaches the peak value of 30 at the beginning, end and during the lunch hour and has an average value of 2 during the rest of the day. Fan Coil Unit Type 996 fan coil component of TRNSYS was used for coupling the adsorption chiller to the cooling room.

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2.2 Components of Room Cooling Load Estimation The cooling load of the room has been estimated in TRNSYS, using the Type 19 detailed single-zone model with energy rate controller, for the considered room geometry, construction and occupancy patterns. The energy rate controller estimates the cooling load required to maintain the room below a temperature of 27 °C and RH below 60% as per the upper comfort limit recommendation for less than 10% PPD by IMAC [6].

2.3 Control Strategy There are two chiller controller units (Type 2-Aquastat C) in the system, as shown in Fig. 1. The purpose of these controllers is as follows: Cooling mode Aquastat (Type 2-Aquastat C and Type 2-Aquastat C-2) Hot water inlet control: This controller turns on the adsorption chiller including all of its pumps, only when the temperature of water in the thermal storage is in the operable range of the chiller (55–85 °C). Room temperature control: This controller ensures that the chiller delivers cooling power to the room only as long as the room temperature is above the minimum comfort temperature of 22 °C as per the recommendations of IMAC for less than 10% PPD [7]. The controller would not turn on the chiller until the temperature of the room rises beyond the mid-value of the thermal comfort range (i.e. beyond 24.5 °C). Besides these, a programmable calculator turns off the chiller when the occupancy of the room falls to zero.

3 Simulation Results and Discussion 3.1 Preliminary Estimation of Solar Thermal System Sizing An initial estimate of the collector aperture area and storage volumes was obtained making use of the minimal data available for the given application at a certain location. The collector area was estimated monthly based on the daily average cooling load, irradiation and ambient temperatures as follows: A=

Daily Cooling load Average solar radiation × Operating COP × Operating efficiency × Daylight hours

(2)

Sizing of a Solar-Powered Adsorption Cooling … Table 1 Cost and power consumption details of solar cooling system

185

Capital investment (CI)

Unit cost

Power consumption (W)

Solar collector

Rs. 6000/m2

Storage volume

Rs. 6000/100 L

Adsorption chiller

Rs. 617,548.6

395

Cooling tower

Rs. 30,000

186

Fan coil unit

Rs. 19,750

186

Additional costs

% of capital cost

Piping and installation 10% (PI) Annual operation and maintenance (AM)

2%

Salvage value (S)

25% for solar collector and storage unit and 5% for the chiller-related components

where the operating efficiency is estimated from Eq. 1 with T in = 75 °C and morning average ambient temperatures. The operating COP of the chiller was taken to be around 0.6, as it fluctuated very little with varying operating conditions. To determine the suitable collector area, an economic analysis was carried out to compare the costs incurred per unit mass of CO2 avoided in running the chiller with selected collector area of solar cooling unit, instead of a solo operation using a conventional air-conditioning unit. Table 1 shows the costs and respective electrical power consumptions of the various units involved in the solar cooling system taken from MNRE guidelines of solar water heating systems [8], and market surveys of various manufacturers. It can be seen that the cost of adsorption chiller of 3-ton capacity is higher than a conventional chiller of the same capacity. One of the key reasons for this is the usage of customized designs in adsorption cooling systems. PV = CI + PI +

S AM((1 + d)n − 1) − d(1 + d)n (1 + d)n

(3)

where PV is the present value of total costs incurred, ‘d’ is the discount rate taken to be 10.74% as per the norms of Central Electricity Regulatory Commission, India, and ‘n’ is the useful life of the plant which is taken to be 15 years. Considering an EER value of 4.0, the number of units of electricity saved (SU) for a selected collector area is given by  SU = SCF ×

Annual cooling demand EER

−Auxiliary power consumption × annual working hours) × n

(4)

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50.00

120 100 80 60 40 20 0

40.00 30.00 20.00 10.00 0.00

Aperture area (sq.m)

Thermal power (kWh)

where SCF is the fraction of cooling energy met by each collector area. Considering 0.9 kg of CO2 emissions per unit electricity produced and ignoring the costs of storage unit, the solar cooling fraction and costs were computed corresponding to each monthly collector area required (shown in Fig. 2) and the results are shown in Fig. 3. It was observed that the collector aperture area of 40 m2 yielded the least cost incurred per unit mass of CO2 mitigation. The maximum cooling load over the year obtained from the simulation was 8.04 kW. A suitable adsorption chiller has been chosen based on the minimum capacity available in the market (10 kW). Since the cooling load has been observed to be below the nominal capacity of the adsorption chiller throughout the year, a chilled water storage was considered unnecessary. Equation 6 shows the correlation proposed for storage tank volume estimation to accommodate for the difference in the solar thermal power availability and the thermal power requirements of the adsorption chiller (Fig. 4). Figure 5 shows the storage volume requirements computed monthly corresponding to the collector area of 40 m2 .

Daily chiller thermal demand Daily solar thermal availability (with 40sq.m collector area) Collector aperture area required

Months of the year

1.00

1200.00

0.80

1000.00 800.00

0.60

600.00

0.40

400.00

0.20

200.00

0.00 10.00 20.00 30.00 40.00

Cost in Rs

Solar cooling fraction

Fig. 2 Monthly estimates of daily average thermal demand and availability and the corresponding aperture areas required

Solar cooling fraction Total cost per kg of CO2

0.00

Collector area (sq.m) Fig. 3 Variation of solar cooling fraction and total costs incurred with varying collector area

187

1000

5.00

800

4.00

600

3.00

400

2.00

200

1.00

0

0 2 4 6 8 10 12 14 16 18 20 22

Cooling load (kW)

Solar radiation (W/sq.m)

Sizing of a Solar-Powered Adsorption Cooling …

Solar radiation Cooling load

0.00

Time of the day Fig. 4 Cooling load and solar radiation fluctuation over a typical day in July

V =

Peak load duration ×





Peak cooling load Operating COP − Average solar thermal power × Efficiency

C p T

(5)

Thermal power (kW)

10

350 300

8

250

6

200

4

150 100

2

50 0

0

Thermal storage capacity (L)

The graph in Fig. 5 shows that the average thermal power from the collector of 40 m2 aperture area exceeds the peak thermal power for half of the months considered and hence no thermal storage volume is necessary. However, the chiller requires an overhead tank for hot water supply for which the minimum thermal storage capacity was found to be 100 L. The optimal storage volume was computed to be 300 L based on the economic analysis, though the deviation in cost computed was less than 5% with that obtained using the storage volume of 100 L.

Thermal storage volume required (L) Daily average peak chiller thermal demand

Months of the year Fig. 5 Monthly estimates of daily average peak thermal demand and availability and the corresponding storage volumes required

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3.2 TRNSYS Simulation Results A detailed simulation analysis was conducted in TRNSYS with the collector areas varying from 10 to 60 m2 and the storage volumes varying from 100 to 800 L. The performance was evaluated in terms of solar cooling fraction which is defined as follows: SCF =

No. of hours in comfort condition ( 1200 °C) to get the desired phase and at that temperature, La2 Zr2 O7 phase is seen as an impurity phase which restricts the ionic movement. Further Li7 La3 Zr2 O12 is not stable at humid air. Similarly, Li2 S-P2 S5 glass-based ionic conductors are also not chemically stable and react with the moisture of the air and create harmful H2 S gas [4]. Comparative to other SSEs, NASICON has advantages with respect to its high chemical stability in contact with Li metal and has easy synthesis process. Al+3 substituted LAGP (Li1+x Alx Ge2-x P3 O12 ) with NASICON structure has been studied in detail and is found compatible with Li metal anode and A. Das · M. Goswami (B) · M. Krishnan Glass and Advanced Material Division, Bhabha Atomic Research Centre, Mumbai 400085, India e-mail: [email protected] S. K. Deshpande UGC-DAE CSR, IUDCT, Mumbai 400085, India P. Preetham · S. Mitra Electrochemical Energy Laboratory, Department of Energy Science and Engineering, Indian Institute of Technology-Bombay, Powai 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_23

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shows high ionic conductivity (~10−4 Scm−1 ) at room temperature. In this study, LAGP has been substituted by Si4+ in place of P5+ (Li1+x Alx Ge2-x P3-y Siy O12 , LAGPS) to increase the stability against moisture and see the effect on the structure and ionic conductivity [5]. XRD has been used to see the nature of the phase formed in the material and also Rietveld analysis to find out the exact cell structure, amount of Al+3 substitution and the cell parameters. EIS at low temperature helped to separate the conductivity due to grain and grain boundary. The cell has been fabricated with developed electrolyte and commercially available anode and cathode and has been tested for its performance.

2 Experimental 2.1 Synthesis of LAGPS Solid Electrolyte Glasses with nominal composition Li1.5 Al0.5 Ge1.5 P2.9 Si0.1 O12 (LAGPS) were prepared by the conventional melt-quenching technique. Every 100 g batch was prepared by mixing the initial constituents, in the form of carbonate and diammonium hydrogen phosphate of proportionate amount. Calcination was carried out for sufficient time to convert the initial constituents into their corresponding oxide form. This process was repeated to ensure complete decomposition after through mixing and grinding. After calcination, the charge was mixed and grounded properly and melted in a Pt-Rh crucible. The melt was held at the melting temperature for 1–2 h for homogenization and cast onto a metal plate. The glass was annealed at around 450–550 °C for 4–5 h and cool down to room temperature slowly. The glass was crystallized using predetermined heat schedule.

2.2 Characterization of the Electrolyte The phase formation in the sample was confirmed using XRD (Model Bruker 8 tools X-Ray Diffractometer). The electrical conductivity was measured on 1–2 mm thick and 10–12 mm diameter crystallized LAGPS samples. Gold coating was done on both the surfaces for good electrical contact. Novacontrol make frequency analyzer was used for conductivity measurement. The measurements were carried out in the frequency range of 1–106 Hz and the temperature range of 223–303 K.

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2.3 Assemble of LiFePO4 /LAGPS/Li Cell For cell fabrication, LiFePO4 (LFP) coated aluminum foil was used as cathode. The loading of the active material was 9.47 mg cm−2 . Li metal was used as anode. LFP/LAGPS/Li cell was assembled with 2032-coin cell inside argon-filled glove box. To reduce the LFP/solid electrolyte interfacial resistance, a small amount of liquid electrolyte [1 M lithium hexafluorophosphate (LiPF6 ) solution dissolved in ethylene carbonate (EC) and dimethyl carbonate (DMC) (EC:DMC = 1:1)] was added between them. The charge/discharge performance of the cell was evaluated using Arbin automatic battery analyzer within the voltage range of 2.6–4.0 V at the constant current of 0.05C (1C = 170 mAg−1 ).

3 Theoretical Consideration In ionic conduction, an ion (or charge carrier) hops from one site to another and hence charge flows. Charge carrier formation process is similar to defect pair generation, but, in NASICON conductors, charge carriers are already present in the sample. Trapping of the ions is not possible in this kind of conductors because the dopants are similar in size and electronegativity with the constituents is to be replaced. So, the thermal generation of charge carriers is not possible here. The temperature dependency of ionic conductivity comes only from the ion hopping rate  ν = ν0 exp

Sm k



  Hm exp − kT

(1)

 Hm where ν0 = 2Md 2 is the fundamental attempt frequency of ion hopping, ν is the hopping rate of mobile ion or “relaxation frequency” [6], Hm is the enthalpy for charge carrier migration, M is the mass of lithium ion, and Sm is the entropy [7]. In general, for all solid-state ionic conductors, the temperature dependency of DC ionic conductivity is given by Arrhenius-type equation   Ea σ0 exp − σ (T ) = T kT

(2)

where σ0 is the pre-exponential factor, k is the Boltzmann constant, E a is the energy barrier which has to be overcome for long-range ionic hopping or better known as activation energy for ionic conduction. E a can be evaluated from the slope of ln(σ T ) versus T1 plot. If l and A are the thickness and area of the sample, respectively, the total DC conductivity can be evaluated by

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σt =

1 l Rt A

(3)

where Rt is the total (Grain + Grain boundary) resistance calculated from the complex impedance plot.

4 Results and Discussion 4.1 Structural Analysis X-ray diffraction result confirms the formation of NASICON-based LiGe2 (PO4 )3 (LGP) phase [8]. Pattern showed shift in peak position, and it is due to the substitution of Al and Si in place of Ge and P, respectively [9]. The crystal structure consists of GeO6 octahedra and PO4 tetrahedra connected each other by their corners. There are two positions of Li+ ion: M1(0,0,0) and M2(0.07,0.34,0.08) in this structure [5]. Figure 1 shows the Rietveld refinement of the XRD pattern with R-3c space group. Absence of any broad hump in Fig. 1 suggests maximum conversion of glass to crystalline phase. The refined structural parameters of the major phase (LAGPS) are given in Table 1. The refinement shows that 6.69% AlPO4 (LAGPS: 93.31%) of the total weight exists in the sample. All the estimated standard deviations (e. s. d.) are obtained from Berar’s method [10, 11].

*

20000

Intensity/a.u.

15000

*

10000

5000

*

*

* #

Phase quantification:

93.31% *# LAGPS: AlPO4: 6.69%

#

0

* * *

** * * * * * * * ** * **

-5000 20

30

40

50

60

70

2θ/ Degree

Fig. 1 Experimental (•) and calculated (-) XRD pattern of LAGPS. The vertical lines show the Bragg positions. The difference profile is shown at the bottom

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Table 1 Typical crystallographic and structure parameters after refinement a

b

c

Rwp , Rexp

Volume Density Goodness of (unit cell) (Calculated) fit (χ2 ) and Z

8.2498(5) Å 8.2498(5) Å 20.644(2) Å 1405.01 A3 , 6

3.62 gcm-3

4.03

7.10%, 3.54%

4.2 Electrical Analysis Cole-Cole [12] plot for the glass ceramics sample measured at 223 K is shown in Fig. 2. In LAGPS systems, the ionic conductivity contribution, from Li+ ions, present in bulk/grain is shown at high-frequency region and at low frequency, it is from grain boundary. So the resistivity is related to grain and grain boundary at high and low frequencies, respectively [13]. In Fig. 2, two depressed semicircle segments are observed, corresponding to grain and grain boundary. These semicircles were fitted using nonlinear least square fit method, and the grain and grain boundary resistances are found from the intercepts of the fitted semicircles with real axis [9, 14]. Resistance is found to decrease with the increase in temperature suggesting lower-energy barrier for charge carrier hopping. Total DC ionic conductivities at different temperatures are calculated using Eq. (3). The calculated activation energy of ionic conduction (E a ) is 0.45 eV. To resolve the contribution to the impedance from bulk and grain boundary, measurements were carried out at low temperatures ranging from 223 to 273 K apart -2.0x105

Fitted curve for grain boundary Fitted curve for grain 223K

Z"/ohm

-1.5x105

-1.0x105

-5.0x104

0.0

0.0

5.0x104

1.0x105

Z'/ohm Fig. 2 Z” versus Z’ plot at 223 K

1.5x105

2.0x105

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Fig. 3 σac versus frequency plot at different temperatures

σac/Scm-1

10-3

303K 273K

10-4

253K 10-5

10-6 102

223K

103

104

105

106

Frequency/Hz

from the measurement at 373 K. Figure 3 shows the real part of AC conductivity at different temperatures for heat-treated sample. Due to increase in ion hopping rate with temperature (Eq. 1), σac increases significantly up to a value 2.03 × 10−4 Scm−1 at room temperature (303 K). Figure shows a sharp drop in σac at lower-frequency region (103–104 Hz). With the increase in temperature, the curves merge with each other. This indicates the accumulation of space charge at the electrode interface, and it is significantly high at higher temperature due to more ionic hopping. There are two almost flat regions in the mid-frequency range at lower temperatures (223–273 K), and they are attributed to the DC conductivity due to grain and grain boundary, respectively. But, at 303 K, these two regions are merged and give a combined contribution to DC conductivity. Further, it is seen that the ionic conductivity increases at high-frequency region because of the reduction in energy barrier with the increase in temperature [15]. Figure 4 shows the first three cycles of galvanostatic charge–discharge profiles of LFP/LAGPS/Li cell in the voltage range of 2.6–4.0 V at a constant current rate of 0.05C. The voltage plateau is nearly at 3.63 V for charging and at 3.21 V for discharging of the first cycle at 0.05C. These values are different from the reaction potential (3.45 V) of lithium intercalation/deintercalation of LFP, and this is due to high interfacial resistance [4]. At the first charging cycle, the specific capacity calculated is 154 mAhg−1 which is approximately 90% of the theoretical capacity (170 mAhg−1 ) and after that the charge and discharge capacities are almost fixed at nearly 130 mAhg−1 (approximately 76% of theoretical capacity). The low values of charge and discharge capacities of the cell are attributed to the high interfacial resistance at the electrolyte/electrode interface. Figure 5 depicts the values of discharge capacity and coulombic efficiency at different cycles. The capacity retention up to 20th cycle is almost 100% which reveals that LAGPS can be used in solid-state Li ion batteries. Figure 6 shows the impedance spectra of LFP/LAGPS/Li cell at room temperature. The high-frequency and mid-frequency fitted semicircles reveal the resistance of bulk electrolyte and the

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237

4.0

Voltage/V vs Li/Li+

3.8 3.6

1st cycle 2nd cycle 3rd cycle

3.4 3.2 3.0 2.8 2.6

0

20

40

60

80

100

120

140

160

-1

Specific capacity/mAhg

Fig. 4 Galvanostatic charge and discharge curves of LFP/LAGPS/Li cell at 0.05C at room temperature

100

150 80

125

60

100

Discharge Capacity Coulombic efficiency

75

40

50 20

25 0

0.05C

Room temperature

0

5

10

15

20

Coulombic efficenecy/%

Discharge Capacity/mAhg-1

175

0

Cycle Number Fig. 5 Cyclic performance of specific discharge capacity and coulombic efficiency of the cell at 0.05C at room temperature

electrolyte/electrode interface, respectively. The values of these two resistances are found to be 500 and 1507 , respectively. In future work, we are focusing to improve the interfacial contact to achieve maximum capacity.

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Fig. 6 Cole-Cole plot of LFP/LAGPS/Li cell at room temperature

Fitted curve for interfacial resistance Fitted curve for bulk electrolyte resistance

-1400

Experimental data

Z"/Ohm

-1200 -1000 -800 -600 -400 -200 0 0

200

400

600

800

1000 1200

1400

1600

Z'/ohm

5 Conclusions LGP glass ceramics doping with Al and Si was successfully prepared using the conventional melt-quenching technique. Material showed correct phase formation and microstructure, and a good value of ionic conductivity (~2.03 × 10−4 Scm−1 ) was achieved at room temperature. Rietveld refinement confirmed the formation of desired phase (Li1.5 Al0.5 Ge1.5 P2.9 Si0.1 O12 , LAGPS) and used to quantify the amount of major and impurity phases. Low-temperature electrical measurement could distinguish the contribution of grain and grain boundary to the overall bulk resistance. Performance of the solid-state cell exhibited a good capacity retention and coulombic efficiency. Improvement in electrochemical performance by reducing the overall resistance of the cell can succeed for use of the material as commercial solid-state electrolytes.

References 1. Huang, M., Liu, T., Deng, Y.-F., Geng, H.-X., Shen, Y., Lin, Y.-H., Nan, C.-W.: Effect of sintering temperature on structure and ionic conductivity of Li7−x La3 Zr2 O12−0.5x (x = 0.5 ~ 0.7) ceramics. Solid State Ionics 204–205, 41–45 (2011) 2. Ito, S., Nakakita, M., Aihara, Y., Uehara, T., Machida, N.: A synthesis of crystalline Li7 P3 S11 solid electrolyte from 1,2-dimethoxyethane solvent. J. Power Sources 271, 342–345 (2014) 3. Kim, K.-M., Shin, D.-O., Lee, Y.-G.: Effects of preparation conditions on the ionic conductivity of hydrothermally synthesized Li1+ x Alx Ti2-x (PO4 )3 solid electrolytes. Electrochim. Acta 176, 1364–1373 (2015) 4. Zhao, Erqing, Ma, Furui, Guo, Yudi, Jin, Yongcheng: Stable LATP/LAGP double-layer solid electrolyte prepared via a simple dry-pressing method for solid state lithium ion batteries. RSC Adv. 6, 92579–92585 (2016)

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5. Das, A., Krishna, P.S.R., Goswami, M., Krishnan, M.: Structural analysis of Al and Si substituted lithium germanium phosphate glass-ceramics using neutron and X-ray diffraction. J. Solid State Chem. 271, 74–80 (2019) 6. Wert, C., Zener, C.: Interstitial atomic diffusion coefficients. Phys. Rev. 76(8), 1169–1175 (1949) 7. Francisco, B.E., Stoldt, C.R.: Energetics of ion transport in NASICON-type electrolytes. J. Phy. Chem. C 119(29), 16432–16442 (2015) 8. Alami, M., Brochu, R., Soubeyroux, J.L., Gravereau, P., Le Flem, G., Hagenmuller, P.: Structure and thermal expansion of LiGe2 (PO4 )3 . J. Solid State Chem. 90(2), 185–193 (1991) 9. Das, A., Goswami, M., Krishnan, M.: Study on electrical and structural properties in SiO2 substituted Li2 O-Al2 O3 -GeO2 -P2 O5 glass-ceramic systems. Ceram. Int. 44(11), 13373–13380 (2018) 10. Berar, J.F., Lelann, P.: E.s.d.’s and estimated probable error obtained in Rietveld refinements with local correlations. J. Appl. Cryst. 24, 1–5 (1991) 11. Berar, J.F.: Data optimization and propagation of errors in powder diffraction. Acc. Pow. Diff. II, NIST Sp. Pub. 63, 846 (1992) 12. Cole, K.S., Cole, R.H.: Dispersion and absorption in dielectrics I. Alternating current characteristics. J. Chem. Phys. 9(4), 341–351 (1941) 13. Chung, H., Kang, B.: Increase in grain boundary ionic conductivity of Li1.5 Al0.5 Ge1.5 (PO4 )3 by adding excess lithium. Solid State Ion. 263, 125–130 (2014) 14. Crawford, J.F.: A non-iterative method for fitting circular arcs to measured points. Nucl. Instrum. Methods Phys. Res. 211, 223–225 (1983) 15. Brahma, S., Choudhary, R.N.P., Thakur, A.K.: AC impedance analysis of LaLiMo2 O8 electroceramics. Phys. B 355(1–4), 188–201 (2005)

Adaptive Relaying Scheme for a Distribution Network with Highly Penetrated Inverter Based Distributed Generations Kirti Gupta and Saumendra Sarangi

1 Introduction With the increase in deployment of inverter-based distribution generations (IBDGs) in the modern grid, the analysis to develop a reliable protection scheme also needs modification. Earlier, the protection system was designed according to the synchronous generators used conventionally which have a contribution of 6–10 p.u. in the fault current. On the contrary, the inverter operation in the IBDGs limits this contribution to a maximum of 3 p.u. in order to protect its electronic devices connected [1]. This behaviour imposes to modify the setting of overcurrent relays (OCRs) used for the protection of the distribution system.

2 Literature Review The literature survey suggests a solution to this problem can be broadly classified into two groups. The first group consists of variation in IBDG parameters like the disintegration of DGs [2, 3], integration of distributed generations (DG)s with its optimum placement, rating, type, etc. [4, 5]. It is obtained through various optimization techniques. The second group consists of modifications in overcurrent relay (OCR) parameters by either offline or online method. The offline method consists of applying optimization techniques to find the optimum setting of the relays corresponding to a particular operation scenario keeping in mind the various constraints K. Gupta (B) National Institute of Technology Uttarakhand, Srinagar, India e-mail: [email protected] S. Sarangi Motilal Nehru National Institute of Technology Allahabad, Allahabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_24

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and the objective function [6, 7]. On the other hand, the online setting consists of adaptive setting obtained through Artificial Neural Network, multi-agents, hybrid multi-agents, etc. [8, 9]. In this paper, a novel method has been presented considering the application of low voltage ride through (LVRT) [10], problem of staircase current waveform in highly penetrated distribution system [11, 12]. A novel way is presented for the coordinated operation of OCRs using superconducting fault current limiters (SFCLs). In order to maintain the coordinated operation of OCRS appropriate value of SFCL is selected to limit the extra fault current injected in the network. The paper has been organized in the following manner namely, Sect. 3 formulates the problem, Sect. 4 proposes the novel scheme, Sect. 5 shows the result and discussions, Sect. 6 finally concludes the work.

3 Problem Formulation Considering a case of highly penetrated distribution network shown in Fig. 1. The protection scheme recommended by IEEE standard 929 is tabulated in Table 1.

Fig. 1 Variation in fault current from substation

Adaptive Relaying Scheme for a Distribution Network … Table 1 Protection scheme for PV system

243

Terminal voltage (p.u.)/grid frequency (Hz)

Disintegration time (cycles)

V < 0.5

6 cycles

0.5 ≤ V < 0.88

120 cycles

0.88 ≤ V < 1.1

Normal operation

1.1 ≤ V < 1.37

120 cycles

1.37 ≤ V

2 cycles

f < 59.3

6 cycles

59.3 ≤ f ≤ 60.5

Normal operation

60.5 < f

6 cycles

A fault occurs at the location shown making the bus voltages of IBDGs nearer to fault location to dip below 0.5 p.u. making it to disconnect in six cycles. The other IBDGs also shows the disintegration behaviour according to the table shown. The IBDGs showing normal operation will ride through during fault and will provide reactive power support. However, after disconnection of IBDGS within six cycles would increase the substation fault current making the bus voltages to drop further. The IBDGs now would disconnect from the system according to the updated values of bus voltages. The time of operation (t op ) of the overcurrent relay is shown by (1): top =

0.14(TMS) PSM0.02 − 1

(1)

where t op : Time of operation TMS: Time Multiplier Setting PSM: Plug Setting Multiplier The PSM defines the severity of the fault occurred. It is denoted as the ratio of fault current to the threshold value of current. The threshold value is the minimum value of the current necessary to activate the operation of relay. The TMS is used to provide a delay between the operation of primary (nearer to fault) and backup (neighbouring to the primary) relays for coordinated operation. As the operation of overcurrent relays depend on the value fault current and considering the above example it is clear that fault current will have a varying nature. Consequently, demands either the modifications in relay setting or limit this fault current variations in order to maintain the reliable and coordinated operation of relays.

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4 Proposed Method The flowchart of the proposed method is presented in Fig. 2. A particular fault contingency is taken into account. The load flow and short circuit analysis are carried out to obtain the bus voltages. According to the protection scheme of the PV systems tabulated in Table 1 corresponding PV is disconnected in the allowed time. If the extra current after IBDG disintegration in the lines doesn’t affect the coordinated operation of the protective devices then no SFCL addition is needed. On the contrary, if the increment of substation current effects the coordination of relays then suitable value of SFCL should be computed and need to be connected in the system. Fig. 2 The flowchart of proposed method

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Fig. 3 Equivalent model for PV system

4.1 Fault Analysis Many authors have presented different ways for analysis of fault in case of IBDG integrated system [12]. Here BIBC and BCBV matrix approach is taken into account with IBDGs represented as shown in Fig. 3. Considering a four bus balanced three phase distribution system with IBDGs connected on buses 2 and 3 in Fig. 4. The bus numbers, bus voltages, branch currents, node currents, relays and loads are mentioned in it. Applying Kirchoff’s current law (KCL) the relation between the branch and node currents is obtained as follows: I34 = I 4 I23 = (I3 − Iinv3 ) + I34 = (I3 − Iinv3 ) + I4  − →  I12 = I2 − I inv2 + I23 = (I2 − Iinv2 ) + (I3 − Iinv3 ) + I4 Consequently, on applying Kirchoff’s voltage law (KVL) the relation between bus voltages and line drops are obtained. V2 = V1 − Z 12 I12 V3 = V2 − Z 23 I23 = V1 − Z 12 I12 − Z 23 I23 V4 = V3 − Z 34 I34 = V1 − Z 12 I12 − Z 23 I23 − Z 34 I34

Fig. 4 Sample distribution network with IBDG penetration

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In matrix form, the KCL equations can be transformed as: ⎤ ⎡ ⎤ ⎤⎡ 111 I12 (I2 − Iinv2 ) ⎣ I23 ⎦ = ⎣ 0 1 1 ⎦⎣ (I3 − Iinv3 ) ⎦ I4 I34 001 ⎡

In compact form: Ibranch = [BIBC]Inode

(2)

where BIBC = Bus Injection to Branch Current Similarly, the KVL equations can be written in matrix form ⎤ ⎡ ⎤ ⎡ ⎤⎡ ⎤ V2 Z 12 0 0 I12 V1 ⎣ V1 ⎦ − ⎣ V3 ⎦ = ⎣ Z 12 Z 23 0 ⎦⎣ I23 ⎦ V1 V4 I34 Z 12 Z 23 Z 34 ⎡

V = [BCBV]Ibranch

(3)

V = [BCBV][BIBC]Inode

(4)

Combining (2) and (3): or,

where, BCBV = Branch Current to Bus Voltage Note: all are vector quantities The analysis for different symmetrical and unsymmetrical faults to obtain post fault voltages can be obtained by (4). The analysis has not been presented here due to shortage of pages.

4.2 Reactive Current Injection by IBDGs The factors which govern the fault response of IBDG through the control action has been presented and verified both analytically and through simulation. The dynamic voltage support during fault can be expressed as shown in Fig. 5.   du = Vld f  − |Vfault |

(5)

Case: 1 if voltage drop, du > 0.1 p.u. then:



Im i1PQ = Im i1PQldf + K factor ∗ (du − 0.1)

(6)

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Fig. 5 Current contribution by IBDG

if Im(i1PQ ) < imax then:





Re i1PQ = min i max − Im i1PQldf , Re i1PQldf

(7)

Im i1PQ = i max

(8)



Re i1PQ = 0

(9)

otherwise,

where du: Voltage drop at PV bus in (p.u.) V ldf , i1PQ ldf : Load flow voltage and current at PV bus (p.u.) V fault: Voltage at PV bus during fault (p.u.) Im(i1PQ ): Imaginary part of current supplied by PV (p.u.) Re(i1PQ ): Real part of current supplied by PV (p.u.) Case: 2 if voltage drop du ≤ 0.1



Im i1PQ = Im i1PQldf

(10)





Re i1PQ = Re i1PQldf

(11)



where Iinv = Re i1PQ + jIm i1PQ .

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4.3 Superconducting Fault Current Limiters SFCLs have special property that when the current is below its critical value then it remains in the superconducting state (i.e. Z = 0 ) but during fault conditions the current exceeds this critical value and S-N transition occurs, i.e. it goes to normal state from the superconducting state [13]. Equation (12) represents the impedance of SFCL as ⎧ ⎪ 0, if (t < t0 ) ⎪ ⎪ t−t0  21 ⎨  , if (t0 ≤ t < t1 ) (12) Z (t) = Z n 1 − exp − T F ⎪ a , if (t1 ≤ t < t1 ) − t + b (t ) ⎪ 1 1 1 ⎪ ⎩ a1 (t − t1 ) + b2 , if (t ≥ t1 ) where Z n : Impedance saturated at normal temperature T F : Time constant t o : quench starting time t 1 : first recovery starting time t 2 : secondary recovery starting time a1 , b1 , b2 : Coefficients of first-order linear function.

4.4 Coordination Criteria Figure 6 shows the coordination boundary between primary and backup relays. The limits of current has been shown for which the coordination (hatched area) between the relays will be maintained. t 1 and t 2 are the minimum and maximum coordination time interval (CTI) limits. Generally, this limit is in between 0.2 and 0.8 s. Summarizing all these points we can apply this technique proposed in order to obtain the reliable and coordinated operation of the protection devices.

5 Result and Discussions In this paper, the IEEE 12 bus, 11 kV system has been selected which is shown in Fig. 7 with its line and load data in [14]. The proposed method is simulated and tested on DIgSILENT PowerFactory software. The switches are operated according to the system scenarios. Here generator, relays, buses, switches, loads are represented by G, R, B, S, L, respectively, with the corresponding subscripts. A set of SFCLs are placed in series from the external source to the system. The analysis can be carried out for both symmetrical and asymmetrical faults at different locations in the system.

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Fig. 6 Coordination between relays

Fig. 7 Highly IBDG penetrated IEEE 12 bus distribution system

Fig. 8 a Current contribution by IBDG, b Bus voltage profile for symmetrical fault at bus 12

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Fig. 9 Staircase waveform of the network

The fault current contribution by PV is shown in Fig. 8a. It is clearly visible that during fault there is an increment in reactive current injection in order to support bus voltages. Correspondingly, there is a decrement in active current injection maintaining the overall current to be limited within imax . The bus voltages are shown in Fig. 8b for a symmetrical fault at bus 12. According to the protection scheme the corresponding PVs will disintegrate at prescribed time. The staircase waveform of the branch currents are shown in Fig. 9. Here four cases are considered. In first case, all IBDGs are connected in the system. Further, according to the protection scheme different stages are mentioned. The stage 1 denotes the first event of disintegration. Similarly, stage 2 and 3 depicts the second and third event of disintegration, respectively.

6 Conclusion The proposed method is validated on the IEEE 12 bus distribution system and is found to be effective in maintaining the coordinated operation of the protective devices in the network. This approach can further be extended to other inverter-based DGs with different types of faults considering different values of fault impedances.

References 1. Haj-ahmed, M.A.: The influence of inverter-based DGs and their controllers on distribution network protection, vol. 9994 (2013) 2. Brahma, S.M., Girgis, A.A.: Development of adaptive protection scheme for distribution systems with high penetration of distributed generation. In: 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No. 03CH37491), vol. 4, no. 1, pp. 56–63 (2003) 3. Tailor, J.K., Osman, A.H.: Restoration of fuse-recloser coordination in distribution system with high DG penetration. In: IEEE IEEE Power Engineering Society General Meeting, pp. 1–8 (2008)

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4. Chaitusaney, S., Yokoyama, A.: Reliability analysis of distribution system with distributed generation considering loss of protection coordination. In: International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–8 (2006) 5. Naiem, A.F., Hegazy, Y., Abdelaziz, A.Y., Elsharkawy, M.A.: A classification technique for recloser-fuse coordination in distribution systems with distributed generation. IEEE Trans. Power Deliv. 27(1), 176–185 (2012) 6. Yen, M., Conde, A., Hsiao, T., Martín, L., Trevi, T.: Enhanced differential evolution algorithm for coordination of directional overcurrent relays. Electr. Power Syst. Res. 143, 365–375 (2017) 7. Bayati, N., Dadkhah, A., Sadeghi, S.H.H.: Considering variations of network topology in optimal relay coordination using Time-Current-Voltage characteristic (2017) 8. Lai, K., Illindala, M.S., Haj-ahmed, M.A.: Comprehensive protection strategy for an Islanded Microgrid using intelligent relays, vol. 53, no. 1, pp. 47–55 (2017) 9. Jamali, S., Borhani-bahabadi, H.: Self-adaptive relaying scheme of reclosers for fuse saving in distribution networks with DG, vol. 1, no. 1, pp. 8–19 (2017) 10. Heong, K., Tan, C., Bakar, A.H.A., Seng, H., Mokhlis, H., Illias, H.A.: Establishment of fault current characteristics for solar photovoltaic generator considering low voltage ride through and reactive current injection requirement. Renew. Sustain. Energy Rev. 92(May), 478–488 (2018) 11. Fani, B., Dadkhah, M., Karami-horestani, A.: Adaptive protection coordination scheme against the staircase fault current waveforms in PV-dominated distribution systems, pp. 2065–2071 (2018) 12. Hooshyar, H., Baran, M.E.: Fault analysis on distribution feeders with high penetration of PV systems. IEEE Trans. Power Syst. 28(3), 2890–2896 (2013) 13. Rebizant, W., Solak, K., Brusilowicz, B., Benysek, G., Kempski, A., Rusin, J.: Electrical power and energy systems coordination of overcurrent protection relays in networks with superconducting fault current limiters, vol. 95, pp. 307–314 (2018) 14. Gupta, K., Sarangi, S.: Adaptive overcurrent relay setting for distribution system using superconducting fault current limiters. In: 2018 IEEE 8th Power India International Conference (PIICON), Kurukshetra, India, pp. 1–6 (2018)

Optimization in the Operation of Cabinet-Type Solar Dryer for Industrial Applications Vishal D. Chaudhari, Govind N. Kulkarni, and C. M. Sewatkar

1 Introduction Drying is one of the most common and essential processes in agriculture, food, paper and pulp industries. It is a process of moisture removal from a product in order to attain the desired moisture content. Drying of food products helps to increase shelf life and reduce post-harvest losses. Open sun drying is a popular method of drying but has limitations like dust contamination, insect infestation, spoilage due to rain, etc. The solution to these difficulties forms the basis for design and use of solar dryer. Solar dryers can be classified as direct and indirect solar dryers. In direct mode, the product to be dried is exposed directly to solar radiation [1, 2]. Exposure to direct sunlight leads to discoloration and vitamin loss of the product. Local temperature rise is observed to be of unacceptable level in some products [3]. The indirect solar dryers are equipped with solar collectors where air is heated and transported to the stacks in which products are kept. A better control over temperature and air flow is possible in such dryers [4, 5]. Solar dryers are further classified as active and passive. In active dryers, circulation of air is by forced convection, while in passive types the same is through natural convection. The duration of drying reduces with better quality of product in passive dryers compared to open sun drying [4, 6]. Continuous drying of the product is not guaranteed due to the non-uniformity of the temperature in natural convection dryers V. D. Chaudhari · C. M. Sewatkar (B) Department of Mechanical Engineering, Govt. College of Engineering and Research, S.P. Pune University, Avasari, Pune, India e-mail: [email protected] V. D. Chaudhari Department of Mechanical Engineering, Cusrow Wadia Institute of Technology, Pune, India G. N. Kulkarni Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_25

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[7, 8]. In active dryers, temperature of the air and flow rate of air could be controlled in a better way as against passive dryers [9–11]. The temperature of the drying medium is a major factor that determines quality of the dried product. High drying temperature damages the nutritional quality of the product, while low drying temperature results in longer drying periods [12– 15]. Moreover, different permissible drying temperatures are maintained to prevent damage to the product [16]. Investigations on performance of solar dryers for industrial applications are not common in the literature. Studies on integration of solar dryers with auxiliaries are also not readily traceable in the literature. On the back ground of escalating energy prices solar heat is emerging as a prospective complementary option in various low temperature industrial drying processes up to 60 °C. Drying temperature governs the drying efficiency and drying time [17]. A correct drying temperature must be maintained to conserve the industrial resources. The challenge lies in the integration of solar input with the auxiliaries to maintain the desired dryer temperature. This paper proposes a mathematical model of an improvised cabinet-type industrial solar dryer that will operate in batches with auxiliaries at set temperature and solar radiation intensity for the location as input and estimates the quantum of auxiliary energy needed to maintain this temperature in the dryer. The objective of the proposed analysis is to ensure effective utilization of solar heat by minimizing the energy consumption of auxiliaries while maintaining a constant temperature.

2 The Improvised Cabinet-Type Dryer Figure 1 shows schematic diagram of the proposed model of the industrial solar dryer. The dryer comprises of a metallic cabinet with pentagonal cross section. Inclined dimension of the pentagon forms width of the dryer (W ). Length of the dryer (L) is normal to the plane of the paper and is not visible. Solar radiation is incident

Fig. 1 Schematic of the improvised cabinet-type industrial solar dryer

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on the aperture W × L. Figure 1 also shows the control volume using dashed line. The dryer space comprises of air, internal surfaces of the walls, tray frames, trays, product to be dried, auxiliary electric heaters, and exhaust fans. Electric heaters are sized suitably to attain uniform heating of the dryer space. Openings at the front and top ensure air circulation in the dryer. Aperture has a double glass glazing. A control panel is used for modulation of electric supply to the heaters and fans. Dryer walls are insulated with glass wool. The insulation is clad with aluminum composite sheets for imparting strength and prevents heat loss to the surrounding. Dryer can be installed at a fixed location in the sunlight, suitably oriented to receive solar heat through the double glass glazing. With solar heat gain, dryer space temperature increases, increasing the product temperature. The drying temperature can be set as per the requirement of the product. Moisture evaporated from the product is driven out in the atmosphere. Fresh ambient air enters the dryer through front openings, mixes with the hot air in the dryer, and becomes hot and humid. Hot and humid air is drawn out of the dryer with the help of the exhaust fans through the top openings. If solar radiation intensity is insufficient to attain the desired temperature, auxiliary heater will be switched on to heat the dryer space and balance demand will be met. In the event of receipt of excessive solar heat, dryer space temperature may rise beyond the desired value. The exhaust fans will then accelerate enabling enhanced mixing of ambient air with the hot air in the dryer space. The operation will continue till dryer space attains the desired temperature. Auxiliary heater and exhaust fan consumption constitute auxiliaries. If auxiliary consumption becomes minimum, utilization of solar heat will be maximum. The aim of this work is to minimize the consumption of auxiliaries over a day.

3 The Mathematical Model Figure 2 shows energy balance across the dryer. Energy transfers across the control volume will have an effect on its internal energy. Energy balance of the control volume can be expressed as follows: Fig. 2 Energy balance of the dryer

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m i cpi

∂T ∂t

= Q S − Q LT − Q LS − Q air

(1)

Left-hand side of Eq. (1) describes change in the internal energy of the dryer space, while right-hand side describes energy interactions across the control volume. In the above equation, mi is the mass of a single internal constituent of the dryer. Internal constituents of the dryer comprise inner wall, tray frames, trays, product to be dried, and air occupying the dryer space. C pi indicates specific heat of the corresponding internal constituent. Cumulative heat capacity of the dryer space can be evaluated by knowing masses and specific heats of the internal constituents. Heat capacities of electric heaters and fans are omitted for simplicity. In the above expression, T is the dryer space temperature. All the internal constituents are assumed to be in thermal equilibrium with each other and at temperature T. Time step under consideration is t. Dryer space receives heat from sun (Qsun ) as well as the auxiliary (QAux ). Total heat supplied to the dryer is denoted as Qs . Heat loss from glazing surface is (Q LT ), while that from the sides is (Q Ls ). The heat losses can be evaluated as follows. Q s = Q Sun + Q Aux

(2)

Q LT = Ut Aa (T − Tamb )

(3)

Q LS = U S A S (T − Tamb )

(4)

The extent of heat loss depends on the temperature of the air in the dryer (T ). Heat carried away by humid air drawn out of the dryer (Q air ) can be estimated as Q air = m air × C p,air × (T − Tambient )

(5)

Merging Eq. (3) and (4) in Eq. (1), 

m i cpi

∂T ∂t

= Q S − Ut Aa (T − Tamb ) − U S A S (T − Tamb ) − m air C p,air (T − Tamb )

(6)

Solution of the differential Eq. (6) may be obtained analytically over a time step t.       Q S − Ut Aa T f − Tambient − U S A S T f − Tamb − m air C p,air T f − Tamb Q S − Ut A g (Ti − Tamb ) − U S A S (Ti − Tamb ) − m air C p,air (Ti − Tamb ) =e

  U A +U A +m C t a air p,air S S  ×t − m c i pi

(7)

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With known values of top loss coefficients (U t ), side heat loss coefficients (U s ), and air flow rate (mair ), Eq. (7) determines the total heat to be supplied (Qs ) to maintain the desired dryer space temperature T. In this equation, subscript i indicates parametric values at the initial instance, while subscript f indicates values at the final instance. Final temperature of the dryer space at the end of a certain time step t can be determined by simplifying Eq. (7). 

T f = − C1 Q S − Ut A g (Ti − Tamb ) − U S A S (Ti − Tamb ) − m air C p,air (Ti − Tamb ) 

   −Q S − Ut A g + U S A S + m air C p,air Tamb / Ut Aa + U S A S + m air C p,air (8)  U A +U A +m C  t a air p,air S S  − ×t m c

i pi where C1 = e . Mass flow rate of the exhaust fans can be obtained by again writing energy balance across the dryer. The heat carried out by the air is equal to the difference between heats supplied and heat utilized and lost from the surfaces. The equation obtained for mass flow rate of the blower is

m˙ blower =

(Q S − Q Sun − Q LT − Q LS ) + Minimum air circulation C p,air (Ti − T )

(9)

If a pressure difference of hw m of water column is maintained across the fan, then the energy consumption of the fans can be calculated. Q fan = PB. t = m˙ blower . g . h w . t kWh

(10)

The auxiliaries supplied to the dryer are auxiliary heat QAux and fan consumption QFan . The aim of the study is to minimize the consumption of auxiliaries. Minimize : Auxiliaries = Q Aux + Q Fan

(11)

4 Illustration The mathematical model is illustrated with a typical solar dryer of 4 × 6 m aperture. The parameters chosen for illustration are given in Table 1. Hourly mean values of the solar radiation on 15th March at Pune are included. The time horizon of 24 h is used with a time step of 10 min. In the industrial drying applications, temperature specifications are stringent. A strict maintenance of temperature ensures optimization of time, economy, and product quality. Drying processes of different products are specified at prescribed temperatures in various investigations [17]. Thus, any value of dryer space temperature up to 85 °C can be set in this dryer.

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Table 1 Data for the illustrative example Dryer aperture W × L

4m × 6 m

Slope of the aperture

21°

Mass of dryer internal constituents

933 kg

Specific heat of the dryer internal constituents

477 kJ/kg-K

Mass of single batch of the product to be loaded 50 kg in the dryer Specific heat of the product

2180 kJ/kg-K

Inner wall

Stainless steel, 1 mm thickness

Wall insulation

Glass wool 75 mm thick, density 48 kg/m3 thermal conductivity—0.04 W/mk

Outer wall

Aluminum composite sheet 3 mm thick. Thermal conductivity—0.3 W/mk

Location

Pune (18.530 North, 73.850 East), India

Day

15th March

Minimum air circulation rate

0.16 kg/s

Pressure difference across the exhaust fans, m of water column

0.35

The mathematical model is solved to obtain dryer space temperature. The dryer space temperature varies with the solar radiation intensity and ambient temperature. The mathematical model also estimates energy consumed by auxiliaries to maintain a set dryer temperature. Auxiliaries can be modulated to obtain the set value of dryer space temperature. For the chosen dryer configuration at Pune, India, on 15th March, variation of dryer space temperature over a single day is shown in Fig. 3. Energies entering the dryer space include solar flux only, and no auxiliary energies are set on. Figure 4 shows the variation of dryer space temperature with a constant temperature line of 55 °C. The linear characteristic indicates dryer space maintained at 55 °C temperature. Three regions A, B, and C can be identified in Fig. 4. Regions A and B appear below the set temperature line. During this period (1 AM to 9 AM and 5 PM to 1 AM), auxiliary heating is needed to maintain the set temperature. Region C above the set temperature line signifies the need of heat removal by enhanced air circulation from 9 AM to 5 PM. The proposed configuration of dryer can be operated at any set temperature. For example, at a set temperature of 55 °C, the dryer space will be maintained at 55 °C for 24 h. The auxiliary heat requirement will be 97.7 kWh while the fan energy consumption of 15.4 kWh during a day. The total auxiliaries are estimated as 113.1 kWh per day. With a variation in set value of temperature total auxiliary energy varies. The objective is to minimize the auxiliaries. Further, with a reduction in set value, the total auxiliary energy required reduces and reaches a minimum at 45 °C. This is shown in Figs. 5 and 6. Figure 6 shows three characteristics with set value of dryer space temperature as abscissa and energy

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Fig. 3 Variation of dryer space temperature over a day with solar heat only

Fig. 4 Region of heat addition and heat removal at a set temperature of 55 °C

consumption in kWh per day as ordinate. First characteristic relates variation of auxiliary heat. The consumption of auxiliary heat increases with set value in a linear way, while exhaust fan consumption decreases nonlinearly. Third characteristic in Fig. 8 shows the effect of increase in set temperature on total auxiliary energy consumption. This characteristic indicates that at a set temperature of 45 °C total auxiliary energy

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Fig. 5 Region of heat addition and heat removal at a set temperature of 45 °C

Fig. 6 Energy consumption at various set temperatures. Note that total auxiliary energy is minimum at set temperature of 45 °C

will be at 97.3 kWh per day. If the dryer is operated at 45 °C, solar energy utilization will be maximum and auxiliary energy consumption will be minimum. The phenomenon can be explained with the help of Fig. 6. Total auxiliary energy consumption initially decreases. This may be attributed to a reduced auxiliary heat

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Fig. 7 Variation of dryer space temperature if dryer is operated in day time with optimum set temperature of 50 °C

requirement at lower values of set temperatures. Blower energy predominates in the total energy consumption at lower values of set temperatures. Blower energy consumption is not as acute as electric heater consumption. Thus, the cumulative effect is reduction in total power with increase in set temperature up to 45 °C. With increase in set temperature beyond 45 °C, contribution of electric heater increases. Total energy consumption rises with set temperature beyond 45 °C. Optimum set temperature is influenced by solar radiation intensity. The higher the solar radiation intensity, the higher will be the value of optimum set temperature. In many industries, drying process is not in demand for 24 h. Especially, when the process is combined with the availability solar heat, it is beneficial to operate a solar appliance in day time. Figure 7 shows the result of operation of the proposed dryer during day time. The dryer operates from 6 AM to 7 PM for 13 h. The optimum value of set temperature increases to 50 °C with a total auxiliary consumption of 39.4 kWh per day. This underlines more effective utilization of solar heat as compared to 24-hour operation. It is beneficial to operate this dryer in daytime. The proposed mathematical model uses basic energy balance and is easy to apply. The model thus assures to be a simple tool in the resource estimation of batch-type industrial dryers suitable for integration with solar heat.

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5 Conclusion Mathematical model of an improvised design of cabinet-type solar dryer suitable for industrial applications is proposed. The model becomes suitable for industrial applications due to inclusion of auxiliaries needed to maintain a constant temperature in the dryer space over a day. The model ensures effective utilization of solar heat by minimizing the auxiliary energy. Results indicate that at a set temperature of 45 °C total auxiliary energy will be minimum at 97.3 kWh per day. If the dryer is operated at 45 °C, solar energy utilization will be maximum and auxiliary energy consumption will be minimum. The optimum value of set temperature increases to 50 °C with a total auxiliary consumption of 39.4 kWh per day if the dryer is operated in daytime. This underlines more effective utilization of solar heat as compared to 24-hour operation. It is beneficial to operate this dryer in daytime. The model thus assures to be a simple tool in the resource estimation of batch-type industrial dryers suitable for integration with solar heat.

References 1. Lawand, T.A.: A solar cabinet dryer. Sol. Energy 10, 158–164 (1966) 2. Gbaha, P., Andoha, H.Y., Sarakaa, J.K., Kouab, B.K., Toure, S.: Experimental investigation of a solar dryer with natural convective heat flow. Renew. Energy 32, 1817–1829 (2007) 3. Ekechukwu, O.V., Norton, B.: Review of solar-energy drying systems II: an overview of solar drying technology. Energy Convers. Manag. 40(6), 615–655 (1999) 4. Sharma, A., Chaen, C.R., Lan, N.V.: Solar-energy drying systems: a review. Renew. Sustain. Energy Rev. 13, 1185–1210 (2009) 5. El-Sebaii A.A., Shalab, S.M.: Experimental investigation of an indirect-mode forced convection solar dryer for drying thymus and mint. Energy Conversion Manag. 74, 109–116 (2013) 6. Sharma, V.K., Sharma, S., Garg, H.P.: Mathematical modelling and experimental evaluation of a natural convection type solar cabinet dryer. Energy Convers. Manag. 31, 65–73 (1991) 7. El-Sebaii, A.A., Aboul-Enein, S., Ramadan, M.R.I., El-Gohary, H.G.: Experimental investigation of an indirect type natural convection solar dryer. Energy Convers. Manag. 43, 2251–2266 (2002) 8. Prasad, J., Vijay, V.K., Tiwari, G.N., Sorayan, V.P.S.: Study on performance evaluation of hybrid drier for turmeric (curcuma longa L.) drying at village scale. J. Food Eng. 75, 497–502 (2006) 9. Bennamoun, L., Belhamri, A.: Design and simulation of a solar dryer for agriculture products. J. Food Eng. 59, 259–266 (2003) 10. Sreekumar, A., Manikantan, P.E., Vijayakumar, K.P.: Performance of indirect solar cabinet dryer. Energy Convers. Manag. 49, 1388–1395 (2008) 11. Benhamoua, A., Fazouane, F., Benyoucef, B.: Simulation of solar dryer performances with forced convection experimentally proved. Phys. Procedia 55, 96–105 (2014) 12. Goyal, R.K., Tiwari, G.N.: Performance of a reverse flat plate absorber cabinet dryer: a new concept. Energy Convers. Manag. 40(4), 385–392 (1999) 13. Leon, M.A., Kumar, S., Bhattacharya, S.C.: A comprehensive procedure for performance evaluation of solar food dryers. Renew. Sustain. Energy Rev. 6, 367–393 (2002) 14. Saleh, A., Badran, I.: Modelling and experimental studies on a domestic solar dryer. Renew. Energy 34, 2239–2245 (2009)

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15. Pin, K.Y., Chuah, T.G., Abdull Rashih, A., Law, C.L., Rasadah, M.A., Choong, T.S.Y.: Drying of betel leaves (Piper betle L.): Qualityand drying kinetics. Drying Technol. 27(1), 149–155 (2009) 16. Chen, H.H., Hernandez, C.E., Huang, T.C.: A study of the drying effect on lemon slices using a closed-type solar dryer. Sol. Energy 78, 97–103 (2005) 17. Hii L.C., Jangam S.K., Ong S.P., Solar drying: fundamentals, applications and innovations, Singapore (2012). ISBN: 978-981-07-3336-0

Modeling of Solar Photovoltaic-Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell to Charge Nissan Leaf Battery of Lithium Ion Type of Electric Vehicle Kamaljyoti Talukdar

1 Introduction At present, vehicles use non-renewable sources as fuel such as petrol, diesel, kerosene, etc., which are depleting in nature. These sources of fuel will become exhausted in coming future. Hence, alternate sources of fuel should be implemented. In ref. [1] authors investigated possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Also, the feasibility of integrating a local storage to the EV–PV (electric vehicle-photovoltaic) charger to make it grid independent was evaluated. In ref. [2], authors focused on the evaluation of theoretical and experimental aspects related to the different operation modes of a laboratory power architecture, which realized a micro-grid for the charging of road electric and plug-in hybrid vehicles. A first phase of simulations was aimed to evaluate the main energy fluxes within the studied architecture and to identify the energy management strategies, which optimize simultaneously the power requirements from the main grid and charging times of different electric vehicles. A second phase was based on the experimental characterization of the analyzed power architecture, implementing the control strategies evaluated in the simulation environment, through a laboratory acquisition and control system. A brief overview of working of solar vehicle is being discussed in [3]. In [4], authors presented a solar/hydrogen hybrid power system, which reduced the required hydrogen fuel cell power output by combining batteries and supercapacitors in an electric vehicle. In ref. [5], authors designed and simulated a hybrid photovoltaic (PV)-fuel cell generation system employing an electrolyzer for hydrogen generation. In [6], authors described a demonstrative plant, located near Rome (Italy), built to investigate and test some commercial solar-hydrogen technologies. An early proof-of-concept for K. Talukdar (B) Department of Mechanical Engineering, Bineswar Brahma Engineering College, Kokrajhar 783370, Assam, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_26

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a distributed hydrogen fueling option in which renewably generated, high-pressure hydrogen was dispensed at an fuel cell electric vehicle (FCEV) owner’s home is being described in [7]. In the present paper, solar photovoltaic combined with electrolyzer-PEM fuel cell for operating Nissan Leaf electric vehicle of lithium ion type is being presented for Kolkata City, West Bengal, India.

2 System Layout Figure 1 shows the schematic view of combined solar photovoltaic(PV) and electrolyzer-polymer electrolyte membrane(PEM) fuel cell for running the Nissan Leaf’s battery(ion type) which is similar to Fig. 1 in ref. [13] except hospital in ref. [13] is replaced by electric vehicle/car in which during sunshine hours solar radiation falling on PV modules produces current (IPV ) and after meeting the electric vehicle/car requirement (IEV ), extra current (IPV -IEV ) goes to PEM electrolyzer for hydrogen production and stored in tank after compressing to be used by PEM fuel

Solar RadiaƟon (G)

Solar RadiaƟon (G)

Photovoltaic modules IG

Photovoltaic modules

Inverter

IPV

IG

Charge Controller

PEM Electrolyzer IPV-IEV

Gas storage

PEM fuel cell stack

IEV IEV-IPV Electric car

Fig. 1 Schematic view of combined solar photovoltaic modules and electrolyzer-fuel cell stack

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cell during non sunshine hours. Figure 2 shows brief layout along with car during sunshine hours. During non-sunshine hours, current required for electric vehicle’s battery(IEV IPV ) is obtained from PEM fuel stack. Hydrogen stored in gas cylinder is used by PEM fuel cell and supplied to electric battery after passing through charge controller. Figure 3 shows a brief layout along with car during non-sunshine hours. PV modules and inverter for gas compressor

Solar radiaƟon Nissan Nissanleaf leafbaƩery baƩery Charge controller IPV

IPV-IEV

IEV

ConnecƟng rod

PV modules

ConnecƟng rorod

Fuel cell and electrolyzer assembly

Car movement

Fig. 2 Schematic view of car and PV modules and fuel cell assembly during sunshine hours

Nissan Nissanleaf leafbaƩery baƩery

Charge controller IEV-IPV

PV modules Solar radiaƟon and inverter for gas compressor ConnecƟng rod

IEV

ConnecƟng rod

PV modules

Fuel cell and electrolyzer assembly

Car movement

Fig. 3 Schematic view of car and PV modules and fuel cell assembly during non-sunshine hours

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3 Modeling 3.1 Modeling of Lithium Ion Type Rechargeable Battery of Nissan Leaf Electric Vehicle For operating the Nissan Leaf electric vehicle, 40 kWh lithium ion type rechargeable battery which gives 400 km distance coverage [8] at single charge is taken. The system voltage of battery is taken to be 360 V [9]. The vehicle is assumed to be moving at a speed of 35 km/h. In order to convert kWh into Ah, 40 kWh is divided by 360 V. The Ah value obtained is the capacity of the battery. Now, in order to calculate total current(A) required from solar PV modules equation no. 1 is used [10]. i spv,EV =

battery_capacity(Ah) × DoD × EBC autonomy_days × ηchargecontroller

(1)

where depth of discharge (DoD) is considered to be 80% [11], and expected battery capacity (EBC) is considered 130%. The battery charging efficiency is considered to be 90% [12], η charge controller (efficiency of charge controller) to be 85% [10].

3.2 Modeling of Solar PV System The equations used for calculating current from PV modules are obtained from [13]. Also solar radiation, ambient temperature, and wind speed data are obtained from references given in ref. [13]. Wind speed is assumed to be summation of vehicle speed(35 km/h) and wind speed in Kolkata City (for December and May). The number of PV modules in series (Ns ) is given by equation no.8 from [13]: Ns =

Vsystem,EV Vmodule

(2)

where Vsystem, EV is the system voltage of the PV modules (considered 360 V in the present study) and Vmodule is the maximum voltage of a PV module [14]. The number of PV modules in parallel (Np ) is given by equation no. 12 from [13]: where imp is the maximum current of a PV module [14]. Np =

i spv,EV i mp

(3)

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3.3 Modeling of PEM Fuel Cell The different voltage calculations are obtained from ref. [13]. Number of PEM fuel cell stacks in parallel (Nfcparallel ) can be obtained from Eq. 25 from [13] as: Nfcparallel =

i fuelcell,EV i cell

(4)

where ifuelcell,EV is the maximum current required during non-sunshine hours(17:00 h to 5:00 h) and icell is obtained from ref. [13]. Number of fuel cell connected in series (Nfcseries ) is given by equation no.26 from [13], where Vsystem is system voltage (360 V) and Vfc is net voltage obtained from single fuel cell. The hourly hydrogen consumption of fuel cell stack is obtained from Eq. 27 from [13]: m fc,EV =

i fuelcell,EV × Nfcseries × 3600 × 2 2 × F × ηfuel

(5)

where F-Faraday constant (96,500 C/mol), ηfuel -fuel utilization factor for fuel cell(considered 0.9).

3.4 Modeling of PEM Electrolyzer Excess current (IPV -IEV ) after meeting the requirement of Nissan Leaf battery of electric vehicle is sent to PEM electrolyzer for dissociating water present in electrolyzer into oxygen and hydrogen. The number of cells in stack in series is taken as 300, and effective cell area is considered to be 86.4 cm2 [15]. Amount of hydrogen produced (in gram mol)in electrolyzer with 300 cells in series in hourly basis is obtained from Eq. 30 given from [13]: Melec,EV =

(IPV − IEV ) × 300 × 3600 2×F

(6)

3.5 Modeling of Gas Compressor Power required to run gas compressor is obtained from [13]. Also assumed values are obtained from [13].

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Number of solar photovoltaic modules in parallel needed for running gas compressor is obtained from [13]. i spv,compr essor i mp

(7)

i compressortotal × DF peaksunshinehours

(8)

N p,compr essor = where i spv,compressor =

DF—derating factor of photovoltaic modules(1.25) [10] and peak sunshine hours7 h per day [16], icompressortotal —summation of current required for pressurization of hydrogen to be stored in cylinder(during sunshine hours, 6:00 h to 18:00 h). Number of solar photovoltaic modules in series needed for running gas compressor is given by: Ns,compressor =

Vsystem,compressor Vmodule

(9)

where Vsystem,compressor, i.e., system voltage of compressor is considered to be 48 V and Vmodule —34 V [14].

4 Results and Discussions From Sect. 3.1, rated capacity of battery is found to be 111.111 Ah after dividing 40 kWh by 360 V. The current required from PV modules ispv,EV is found to be 45.315 A from equation number 1. The number of PV modules needed in series and parallel for electric vehicle are 11 and 10 obtained from equation no. 2 and 3, respectively. The number of PEM fuel cell needed in parallel is obtained from equation no. 4 which is 1, where ifuelcell,EV is 1.888 A. The PEM fuel cells needed in series is found to be 350. The hourly hydrogen consumption (in non sunshine hours) by PEM fuel cell and hydrogen production in PEM electrolyzer (in sunshine hours) are obtained from Eqs. 5 and 6, respectively, and shown in Fig. 4 and 5 for the month of December and May, respectively. It is seen that summation of hydrogen consumption during non-sunshine hours from 1:00 h to 5:00 h and from 19:00 h to 24:00 h is 301.191 gm mol for both December and May, and summation of hydrogen generated during sunshine hours from 6:00 h to 18:00 h is 710.544 gm mol and 1245.128 gm mol for December and May, respectively. It is seen that hydrogen generation increases from 6:00 h to 12:00 h and again decreases to 18:00 h due to the fact that solar radiation increases from 6:00 h to 12:00 h and again decreases to 18:00 h. Hence, greater radiation means that greater current generation (IPV ) by PV modules and greater

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120

Hydrogen consumed(gm mol/h)

Gram mole(gm mol)

100

Hydrogen produced (gm mol/h)

80 60 40 20 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (in hours) Fig. 4 Hydrogen consumption and production for the month of December

180

Hydrogen consumed(gm mol/h) Hydrogen produced(gm mol/h)

160

Gram mole(gm mol)

140 120 100 80 60 40 20 0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time (in hours) Fig. 5 Hydrogen consumption and production for the month of May

current (IPV -IEV ) availability by electrolyzer thereby greater production of hydrogen according to Eq. 6. For gas compressor, number of PV modules needed in parallel and series are 34 and 2 from Eqs. 7 and 9, respectively. Table 1 below shows rating of different power system components.

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Table 1 Rating of different power system components Components of power system

Rating

No. of photovoltaic modules in parallel(Np ) for electric vehicle

10

No. of photovoltaic modules in series(Ns ) for electric vehicle

11

Electrolyzer input at 360 V

15.661 kW

No. of fuel cell in a stack(Nfcseries )

350

No. of fuel cells stacks(Nfcparallel )

1

Maximum output of each fuel cell stack

59.32A,21.24 kW

Gas compressor rating at 48 V

7.213 kW

No. of photovoltaic modules in parallel for gas compressor(Np,compressor )

34

No. of photovoltaic modules in series for gas compressor

2

5 Conclusions Based on the observations, it is found that electric vehicle having Nissan Leaf battery of 400kWh capacity can be operated throughout the day and year with the help of 10 and 11 photovoltaic modules in parallel and series, respectively, of Central Electronics Limited Make PM 150 with 15.661 k W electrolyzer, 350 fuel cell stacks collection in series with 21.24 k W power. Also compressor rating of 7.213 k W and 34 and 2 photovoltaic modules in parallel and series, respectively, of Central Electronics Limited Make PM 150 for powering compressor are sufficient. The advantage of this work presented in this paper is that electric vehicle can be recharged at any location, thereby needing no re-fueling station.

References 1. Mouli, G.R.C., Bauer, P., Zemen, M.: System design for a solar powered electric vehicle charging station for workplaces. Appl. Energy 168, 434–443 (2016) 2. Capassoa, C., Iannuzzib, D., Veneria, O.: DC Charging Station for Electric and Plug-In Vehicles. Energy Procedia 61, 1126–1129 (2014) 3. Wamborikar, Y.G., Sinha, A.: Solar Powered Vehicle. In Proceedings of the World Congress on Engineering and Computer Science, ISBN: 978-988-18210-0-3, San Francisco, USA(2010) 4. A power system combining batteries and supercapacitors in a solar/hydrogen hybrid electric vehicle, http:// ieeexplore.ieee.org/document/1554636/), last accessed 2018/12/20 5. El Shatter, ThF, Eskandar, M.N., El Hagry, M.T.: Hybrid PV/Fuel cell system design and simulation. Renewable Energy 27(3), 479–485 (2002) 6. Galli, S., Stefanoni, M.: Development of a solar hydrogen cycle in Italy. Int. J. Hydrogen Energy 22(5), 453–458 (1997) 7. Kelly, N.A., Gibson, T.L., Ouwerkerk, D.B.: Generation of high-pressure hydrogen for fuel cell electric vehicles using photovoltaic-powered water electrolysis. Int. J. Hydrogen Energy 36, 15803–15825 (2011) 8. Electric vehicle lithium ion battery|NISSAN|TECHNOLOGICAL…, https://www.nissan-glo bal.com/EN/TECHNOLOGY/OVERVIEW/li_ion_ev.html, last accessed 2018/12/2

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9. Nissan LEAF-roperld, http://www.roperld.com/science/NissanLEAFII.htm, last accessed 2018/12/5 10. Telecommunication Engineering Centre (TEC),New Delhi, Planning and maintenance guidelines for SPV power, http://www.tec.gov.in/guidelines.html, last accessed on 2011/8/15 11. A Comparison of Lead Acid to Lithium-ion in Stationary Storage Applications, https://www.bat terypoweronline.com/wp-content/uploads/2012/07/Lead-acid-white-paper.pdf, last accessed on 2018/11/3 12. Car Battery Efficiencies - Stanford University, http://large.stanford.edu/courses/2010/ph240/ sun1/, last accessed on 2018/12/7 13. Talukdar, K.: Modeling and Analysis of Solar Photovoltaic Assisted Electrolyzer-Polymer Electrolyte Membrane Fuel Cell For Running a Hospital in Remote Area in Kolkata, India. International Journal of Renewable Energy Development 6(2), 181–191 (2017) 14. Solar photovoltaic modules PM 150,http:-celindia-coin.preview1.cp247.net/cal/PM150.pdf, last accessed on 2012/4/16 15. Dale, N.V., Mann, M.D., Salehfar, H.: Semi-empirical model based on thermodynamic principles for determining 6 kW PEM electrolyzer stack characteristics. J. Power Sources 185(2), 1348–1353 (2008) 16. Some insights into solar photovoltaics-solar home lighting system, NABARD Technical Digest 7,http://www.nabard.org, last accessed on 2009/6/26

Performance Study of an Anode Flow Field Design Used in PEMFC Application S. A. Yogesha , Prakash C. Ghosh, and Raja Munusamy

1 Introduction The impact on the environment due to non-renewable energy sources and the shortage of fossil fuel reserves have given the opportunity to the present civilization for considering alternative energy options. Renewable energy sources are the energy sources which are produced continuously in nature. Some of the renewable energy sources are solar energy, hydroelectric energy, wind energy, biomass energy, and geothermal energy [1]. Renewable energy resources can provide a sustainable solution to fulfill the world’s future energy demand as they are environmentally friendly. Many types of renewable energy technologies are presently used around the world for small-scale electricity generation. These include solar, wind, tidal, hydroelectric, and geothermal technologies, and these technologies can be used for various applications such as transportation, cooling or heating of water, fulfilling rural energy demand problem, and so on [2]. All of these alternative energy resources have their own potential to fulfill the future energy demand, and at the same time, they all have their respective limitations. Intensive research has already been carried out throughout the world for last few decades on all of these renewable energy sources. Still, every one of them suffers from some disadvantages. So, it requires some improvement and evolution in these renewable technologies with time. The most concerned limitation with the solar (both photovoltaic and thermal), wind, and tidal is the lack of certainty as well as the availability of sunshine, wind speed, and tides depending on location and different time in a year limit for the application of this kind of renewable technologies. In the case of hydroelectric technology, the reservoir required for the dam in the upstream S. A. Yogesha · P. C. Ghosh (B) Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India e-mail: [email protected] R. Munusamy Engineering Research Centre, Tata Motor Limited, Pimpri, Pune 400014, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_27

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causes habitat problem on both water and in surrounding land. It also suffers from flow shortage and often leads to ecosystem damage. A non-renewable resource (also called a finite resource) is a resource that cannot be renewed or regenerated quickly enough to maintain with their use. Examples for non-renewable energy sources are fossil fuels (such as coal, wood, nuclear fuels petroleum), oil, and natural gas. Carbon is the main element of fossil fuels. Other nonrenewable resources such as nuclear fission are also existing and promising energy technologies on a global basis because of its large radioactive resources. India and Australia’s large thorium and uranium reserves can be given a few examples of for nuclear resources. But the safety and environmental concerns are two major issues associated with nuclear technology [3, 4]. Nuclear power is a relatively clean form of energy and is suitable for large-scale electrical power generation; however, the highlevel radioactive waste is a major concern and the issues related to the nuclear waste management must be considered before planning any large-scale energy production [5]. Among different energy conversion options, fuel cell systems offer an efficient and sustainable energy conversion solution. The pollution and contamination issues caused by the burning of conventional fuel sources can be fairly reduced by using hydrogen fuel in the fuel cell. Due to the production of hazardous gases like carbon dioxide, greenhouse gases, toxic pollution, shortage of oil production, and excess requirement of electric power throughout the world, fuel cells must be considered for coming generation [6]. In the present condition, the requirement of fuel cells in vehicles has become more in demand. In opposite to the conventional battery, the fuel cell generates electrical energy without storing it and continues the same as long as the hydrogen fuel is supplied [7, 8]. The fuel cell powered vehicles has a longer driving capacity without charging the battery for a long period. It has high energy efficiency and very fewer emissions due to the direct conversion of hydrogen fuel into electrical energy in comparison with the internal combustion engine [9, 10]. Single cell assembly construction as shown in Fig. 1.

2 Components of a PEMFC Air-Cooled Stack 2.1 Anode and Cathode Bipolar Plates Anode in a PEMFC provides the sites for fuel gas to react with O2 ion delivered by the electrolyte. It also conserves the charge neutrality of the complete system. The electrochemical reaction of the fuel occurs at the interface between the anode and electrolyte which depend on the material properties of anode. Due to high electrical conductivity, low ionic conductivity, and high activity of the electrochemical reactions, mostly used anode material is graphite. Similar to the anode bipolar plate,

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Fig. 1 Construction details of single-cell assembly

the cathode bipolar plate material should possess the all properties like high electrical conductivity, high catalytic activity, gas-impermeable commonly used anode material is graphite.

2.2 MEA (Membrane Electrode Assembly) Membrane electrolyte assembly plays the most important role in fuel cell by forming a bridge between cathode and anode through an electrochemical reaction. Therefore, it is designed as the heart of a fuel cell. Sandwiching the membrane between GDLs at a predefined temperature, pressure, and time form a MEA. The inhouse prepared membrane electrode assembly as shown in Fig. 2.

2.3 Sealing Sealing is used for providing compression and leak proof. Silicone material is used widely material for sealing.

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Fig. 2 Membrane electrode assembly

2.4 End Plates As the name suggests, the function of end plates is to hold entire cell components firmly between the two. It is conventionally made of metal to strengthen the hold and therefore less bending of the plates that confirms uniform clamping load over the active area. Nut bolt pairs are normally adopted to clamp the cell outside the active area. End plates also act as current collectors in many cases.

2.5 Current Collectors Current collectors collect the current of the electrochemical reaction in a controlled area. Current collector used copper plate with gold plating.

3 Results and Discussion 3.1 Flow Configuration Developed conceptual design of the graphite plates with four different types flow channels or flow fields is used for anode side flow configuration as shown in Figs. 3, 4, 5, and 6. The designs are namely (a) serpentine flow with uniform curvature, (b) combined pin and parallel flow, (c) serpentine flow with 90-degree uniform curvature, and (d) combination of parallel and serpentine flow channels, respectively.

Performance Study of an Anode Flow Field Design …

Fig. 3 Combination of parallel and serpentine flow

Fig. 4 Combined pin and parallel flow

Fig. 5 Serpentine flow with uniform curvature

Fig. 6 Serpentine flow with 90-degree uniform curvature

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3.2 Numerical Simulation Comparison of velocity profiles for four different flow channels is discussed. Control volume approach is used for solving the velocity and pressure drop using commercial software. Velocity contours and distribution graph are as shown in Fig. 7, 8, 9, and 10. It is clear that the 1st and 2nd case velocity profiles are non-uniform over 3rd and 4th case flow channels. It is evident the 3rd and 4th cases have more uniform flow channels which make the selection flow channels.

Fig. 7 Velocity contours and distribution curve combination of parallel and serpentine flow

Fig. 8 Velocity contours and distribution curve combined pin and parallel flow

Fig. 9 Velocity contours and distribution curve serpentine flow with uniform curvature

Fig. 10 Velocity contours and distribution curve serpentine flow with 90-degree uniform curvature

Performance Study of an Anode Flow Field Design …

a

281

b

Fig. 11 Schematic of the manufactured flow fields structure a combination of parallel and serpentine flow b combined pin and parallel flow

a

b

Fig. 12 Schematic of the manufactured flow fields structure a serpentine flow with uniform curvature b serpentine flow with 90-degree uniform curvature

The cell unit is manufactured with four different anode flow channels configuration keeping same flow geometry of cathode configuration. The single-cell unit considered in this paper is 150 cm2 active area of the membrane electrolyte membrane (MEA). MEA consists of five layers standard membrane electrode assembly prepared. Dispensed type silicon gasket is used for assembly. Its components and assembly are shown in Fig. 13. This paper mainly focuses on the flow field impact in performance of aircooled fuel cell stack unit. Four different flow configuration anode bipolar plates are designed as shown in Fig. 11a, b, 12a, b. The serpentine flow with uniform curvature, combined pin and parallel flow, serpentine flow with 90-degree uniform curvature, and combination of parallel and serpentine flow channels were designed and fabricated.

Fig. 13 Fabricated air-cooled stack

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3.3 Experimental Procedure The test unit consists of single-cell unit assembly along with balance of plant system components, which includes air system blower, hydrogen system regulator and actuator and controlling system. The suction-type blower is used with operating voltage of 24VDC, and hydrogen side pressure is regulated to 0.3 barg for all the operating currents. The flow field impacted in the performance of single-cell unit as observed in the experimental setup. The flow field with the serpentine flow with uniform curvature has higher performance comparatively other flow field designs as shown in Fig. 14. The combined pin and parallel flow design offers lowest performance.

Fig. 14 Polarization curve for four different designs of anode flow channels

Fig. 15 Temperature variation at different locations

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Fig. 16 Polarization curve temperature variation

Similarly conducted experiment for 35 °C, 40 °C, 45 °C, 50 °C with initial ambient temperature of 25 °C. The polarization results of single cell performance as shown in Fig. 16, due to increase in temperature the performance of cell is increased. Combination of parallel and serpentine with uniform curvature flow field is around 30 W, which is 1.4 higher than the worst-case design. To test the temperature impact on the fuel cell stack performance heater pad attached to the end plate and initially ambient temperature is 250 C started heating the cell up to 350 C. Three thermocouples are mounted inside open-cathode channels to measure the temperature the thermocouples are mounted at start, middle, end of the cathode cells. The variation of temperature at three different locations is plotted in Fig. 15.

4 Conclusion The single-cell open-cathode PEMFC single cell designed to study the effects of anode flow geometry. Four different anode flow configurations are designed with 150 cm2 active area. The fuel cell stack unit is manufactured, and results confirmed that flow field with uniform curvature derived best results of 350 mA/cm2 . The tested results indicated that serpentine flow with uniform curvature has maximum power density compared to other three designs. Design offered steady performance for more than 45 min when it was operated at a current of density of 350 mA.cm2 . In addition, serpentine flow with uniform curvature tested for increasing temperature up to 60°C and confirmed that higher temperature is given good results compared to lower operating temperature. Therefore, the flow field designs may be selected

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properly to get improved performance in polymer electrolyte membrane fuel cell stack. Results of this paper certainly provide useful guidelines for anode flow fields used for air-cooled PEM fuel cell stack.

References 1. Squadrito, G., et al.: Design and development of a 7 kW polymer electrolyte membrane fuel cell stack for UPS application. Int. J. Hydrogen Energy 35(18), 9983–9989 (2010) 2. Han, I., Jeong, J., Shin, H.K.: PEM fuel-cell stack design for improved fuel utilization. Int. J. Hydrogen Energy 38(27), 11996–12006 (2013) 3. I. Journal, H. Energy, S. S. Foundation, and M. Road, “Development of polymer electrolyte membrane fuel cell stack 4. L. Mu, W. Cheng, L. Zhi-xiang, and M. Zong-qiang, “The development and performance analysis of all-China- made PEM fuel cell unit and 1 kW level fuel cell stack,” vol. 7, pp. 2–7, 2011 5. R. Bove, T. Malkow, A. Saturnio, and G. Tsotridis, “PEM fuel cell stack testing in the framework of an EU-harmonized fuel cell testing protocol: Results for an 11 kW stack,” vol. 180, pp. 452– 460, 2008 6. D. Chu and R. Jiang, “Comparative studies of polymer electrolyte membrane fuel cell stack and single cell,” pp. 226–234, 1999 7. P. Corbo, F. Migliardini, and O. Veneri, “Performance investigation of 2. 4 kW PEM fuel cell stack in vehicles,” vol. 32, pp. 4340–4349, 2007 8. Han, I., Kho, B., Cho, S.: Development of a polymer electrolyte membrane fuel cell stack for an underwater vehicle. J. Power Sources 304, 244–254 (2016) 9. Sasmito, A.P., Birgersson, E., Lum, K.W., Mujumdar, A.S.: Fan selection and stack design for open-cathode polymer electrolyte fuel cell stacks. Renew. Energy 37(1), 325–332 (2012) 10. H. I. Lee, C. H. Lee, T. Y. Oh, S. G. Choi, I. W. Park, and K. K. Baek, “Development of 1 kW class polymer electrolyte membrane fuel cell power generation system,” vol. 107, pp. 110–119, 2002

Effect of Top Losses and Imperfect Regeneration on Power Output and Thermal Efficiency of a Solar Low Delta-T Stirling Engine Siddharth Ramachandran , Naveen Kumar, and Mallina Venkata Timmaraju

1 Introduction The productive utilization of renewable energy sources is pivotal for all nations due to persistent increase in energy demand and environmental concerns associated with rise in the usage of fossil fuels. Therefore, Stirling engines are better candidates at present as they have no emission, good lifespan, environment-friendly devices, and reasonable competency in converting low-grade thermal energy into mechanical work. Exhaustive studies have been carried out during last two decades on design, optimization, and analysis of concentrating-type Stirling engines [1]. Some authors [2–4] used finite time thermodynamic analysis (FTT) as the tool for design and optimization of solar dish Stirling engines. The performance of a dish-type solar Stirling engine using FTT analysis has been studied and optimized the design parameters for maximum power output condition at high-temperature ranges, i.e., 1300–923 K. Although few authors [5] explored the effective practice of non-concentrating-type Stirling engines, but design parameters were not optimized using FTT. The feasibility of existing low-temperature differential Stirling engines (LTDSE) is very cost effective in terms of small-scale distributed low-grade thermal energy [5]. The thermodynamic performance of LTDSE on a laboratory scale supports the fact that these systems can be implemented on a large scale with watt-level power production. Modified Schmidt models are being used to predict the performance of LTDSE by adding real-time losses and optimized some power influencing geometric parameters such as swept volume ratio, dead volume ratio as well as phase angle for various configurations of Stirling engines [6]. These models are inadequate to predict accurately because of large deviation between the ideal cycle and real cycle

S. Ramachandran (B) · N. Kumar · M. V. Timmaraju Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai 600127, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_28

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[7]. Recently, Grosu et al. [8] introduced a fairly accurate modified Schmidt thermodynamic model, in which various real-time losses were combined and tested on laboratory-level LTDSE model. While accounting various energy losses in Stirling engines, the heat transfer imperfections cause the majority of the losses, i.e., up to 51% of heat loss. Although, thermal losses due to heat transfer (top losses) is a major loss to be considered in solar Stirling engines, literature is not available in this regard. The goal of the current work is to formulate a fairly accurate model to predict the performance of solar LTDSE using FTT analysis. Therefore, a model of a solar non-concentrating-type Stirling engine with thermal losses and thermodynamic irreversibilities is developed. The effect of top loss coefficient and absorption of radiation in the glazing is also incorporated with FTT analysis for the first time. Further, the significance of time for regeneration process on maximum power output and thermal efficiency of the system are also discussed. The modified FTT model with imperfect regeneration is validated with experimental data available from literature [7].

2 Finite Time Thermodynamic Model of LTDSE Thermodynamic cycle of solar LTDSE operates between the temperature of the absorber plate (source) and ambient air (sink). The absorber plate of the solar nonconcentrating Stirling engine is single glazed in order to reduce the top losses (see Fig. 1). Generally, the working fluid is air and material of the absorber plate, displacer, and regenerator are aluminum, plastic foam, and metal wire mesh, respectively. The mechanical prime mover is the unit where the indicated power available from the Stirling cycle is converted to mechanical work/electrical energy with a certain

Fig. 1 Schematic of a non-concentrating solar Stirling engine

Effect of Top Losses and Imperfect Regeneration on Power Output …

287

Fig. 2 T-S diagram of non-concentrating solar Stirling engine combined with the electrical analogy of various heat transfer processes

conversion efficiency. As shown in Fig. 2, the solar non-concentrating Stirling engines consist of a thermodynamic Stirling cycle with different heat transfer processes like conduction, convection, radiation, or combination of these processes. Ideally, processes 1–2 are the isothermal process, in which the working fluid at temperature T c rejects heat Q0 to the atmosphere/ambient at constant temperature T a. Q o = [h C2 (Tc − Ta )]τ12 = mRTc ln rv + mC V (1 − )(Th − Tc )

(1)

where h C2 is the convective heat transfer coefficient between bottom plate (sink) and working fluid. Since the temperature difference between the working fluid at cold end and ambient (process 1–2 in Fig. 2), Tc − Ta is lesser (less than 20 K) in LTDSE, the contribution of convective heat transfer alone is considered. Then, the working fluid passes through the regenerator while absorbing the heat stored in the regenerator QR and this process (2–3) is isochoric in nature. Further, the working fluid gets in contact with higher temperature absorber plate and expands and transfers absorbed heat Qi to the regenerator (3–4). The amount of heat released from the absorber (source) at temperature, T p , is isothermally absorbed by the working fluid (3-4 in Fig. 2) via simultaneous processes of convective and radiative heat transfer.      Q i = h C1 T p − Th + h R1 T p4 − Th4 τ34 = mRTh ln rv + mC V (1 − )(Th − Tc )

(2)

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In process 4–1, only a part of the heat transferred to the regenerator QR is stored and transferred to the working fluid, while the rest is passed onto the working fluid at process 2–3 due to regenerative imperfections. From Eq. (1) and (2), the time for isothermal heat addition process, τ34 , and the time for isothermal heat rejection process,τ12 , can be depicted below, τ34 =

m RTh ln rv +mC V (1−)(Th −Tc) h C1 (T p −Th )+h R1 (T p4 −Th4 )

(3)

τ12 =

m RTc ln rv +mC V (1−)(Th −Tc ) [h C2 (Tc −Ta )]

(4)

Thus, the total cyclic time can be expressed as, τ = τ12 + τ23 + τ34 + τ41 = τ12 + τ34 + 2τ R

(5)

where the τ R is the time for the regeneration process and must be evaluated as a part of internal irreversibility. Additionally, the direct heat leak from the absorber to atmosphere through the engine walls and insulation is generally termed as conductive thermal bridge losses,Q C is also considered [4].   Q C = k0 T p − Ta τ

(6)

This heat loss is assumed to be directly proportional to the temperature difference between absorber and ambient, total cyclic time τ , and a proportionality constant termed as conductive thermal bridge loss coefficient k0 . By applying the finite time thermodynamic approach to the solar Stirling engine by considering heat transferred through both convection and radiation, the maximum power output of the Stirling engine and maximum power thermal efficiency for a cyclic period are given by [4], Pmax =

(1−θ )



1+δ(1−θ) h C1 (Ta −Th )+h R1 T p4 −Th4

(

ηmpt =

)

+h

θ +δ (Th −θ ) 2τ R + m Rlnr v C2 (θ Th −Ta )

Pmax Th +δ(Th −θ )+k0 (T p −Ta )



(7) (8)

whereδ = C V (1 − )/Rlnrv , θ ,θ is the temperature ratio, τ R is the time for the regeneration process, h C1,2 is the Convective heat transfer coefficient between working fluid and heat source, and working fluid and heat sink, respectively. In order to maximize the power output, derivative of Eq. (4) is taken with respect to Th and equated to zero, ∂ P/∂ Th = 0, which gives the optimal working fluid temperature, Th as, Th = (Ta + γ T p )/(θ + γ )

(9)

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289

where,  γ =

θ 2 + δθ (1 − θ ) 1 + δ(1 − θ)

0.5

In order to incorporate the heat transfer losses due to top losses Qtop-loss , an electrical resistance analogy of various heat transfer processes is represented schematically in Fig. 2. To estimate the top loss coefficient Ut , a fairly accurate (less than ±3% error) non-iterative solution proposed by Mullick et al. [9] is considered, ⎡ ⎢ Ut = ⎢ ⎣

(( (

12.75

)

)

1

0.264 T p −Tgi cos β 0.46 0.21 Lg T p +Tgi

)

+

+

(

2 σ T p2 +Tgi

1 1  p + g −1

1

hw +

)(T p +Tgi )

(

4 −T 4 σ g Tgo s (Tgo −Ta )

)

+

tg kg

⎤−1 ⎥ ⎥ ⎦

(10)

where β is the collector tilt angle (°), T p is the absorber plate temperature (K), Ta is the ambient temperature (K),ε p is the emissivity of the absorber plate, εg is the emissivity of the glass cover,αg is the absorptance of the glass cover,k g is the thermal conductivity of the glass cover material (W/mK), tg is the thickness of the glass cover (mm),L g is the air gap space between the absorber plate and glass cover (mm), h w is the wind heat transfer coefficient (W/m2 K), It is the solar irradiation (W/m2 ) and σ is the Stephan–Boltzmann constant (W/m2 K4 ). The instantaneous thermal efficiency of the single-glazed collector is derived from energy balance of absorber plate and expressed as, η0 =

Qu A p It

= (τ0 α0 ) −

Ut (T p −Ta ) It

(11)

The maximum thermal efficiency of the solar Stirling engine is the product of instantaneous thermal efficiency of the glazing and thermal efficiency at maximum power output,  ηm = η0 ηmpt = (τ0 α0 ) −

Ut (T p −Ta ) It



Pmax Th +δ(Th −θ )+k0 (T p −Ta )

 (12)

By using this newly formulated relations (Eq. 9 and 12), the working fluid temperature in solar Stirling engine is predicted and validated with experimental data from elsewhere.

3 Validation Majority of previous investigations on solar Stirling engine dealt with hightemperature regime and only a few authors [7, 10] reported experimental data for

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100

1200

80

1000

60

800

Absorber Plate Working fluid ( Experimental [7] ) Working fluid (Predicted) Irradiaon

40 20 0 7:00

8:00

9:00

10:00

11:00 12:00 Day Time

13:00

14:00

600 400 15:00

16:00

200

Fig. 3 Variation of irradiation and absorber plate temperature with day time

Thermal efficiency (%)

25

ηIdeal Srling ηCurzon-Ahlborn ηT-Experimental ηm-predicted

20 15 10 5 0

L.Grosu et.al [8]

N.Boutammachte et.al [7] A.R Tavakolpour et.al[11]

Fig. 4 Comparison of developed model with various LTDSE designs

N.Martaj et.al [10]

Irradiaon (W/m2)

Temperature ( )

LTDSE. The experimental data provided by N. Boutammachte et al. [7] is considered for validation and performance prediction. The performance of a non-concentrating solar collector coupled with a Stirling engine connected to a water pump (SSM-IV) for rural areas of Meknes/Morocco was evaluated and provided the data of absorber plate temperature and working fluid temperature which was measured in real-time conditions. (see Fig. 3). The data for variation in temperature of the absorber plate and irradiation with respect to day time is taken from the literature [7] and used to solve for Th . The present thermodynamic model could predict the working fluid temperature with an error lesser than ±10% on real-time experimental data. The absorptivity of the glass cover is considered to predict the top loss coefficient of the solar Stirling engine. It has to be noted that the same authors have reported that the existing Schmidt model shows a deviation of five times in ideal and real cycle. By using the absorber plate temperature data [7], the variations in the glass cover (inside and outside) and working fluid temperature are predicted using Eq. (9) and

Effect of Top Losses and Imperfect Regeneration on Power Output …

291

Fig. 5 Variation of different temperatures in solar LTDSE with solar irradiation

shown in Fig. 5. An almost linear increase in the working fluid temperature is noted with the increase in solar radiation. Further, the predicted and experimental working fluid temperatures are reasonably close to each other (coefficient of determination, R2 = 0.9817) indicating this approach can be adopted for calculating the working fluid temperature. From Fig. 3 and 4, it can be observed that the FTT thermodynamic model with thermal losses due to heat transfer is effective in predicting the performance of an LTDSE and can be used for the further parametric study.

4 Results and Discussion In order to evaluate the effect of top loss coefficient and absorptivity of the glass cover on performance of solar LTDSE, all the other parameters are kept constant as θ = 0.95, h C1 = h C2 = 200W/K, h R1 = 4 × 10−8 wK−4 , m = 9 × 10−3 kg, ε = 0.5, Ta = 298K , k0 = 2.5 W/K, αg = 0.08, τ0 α0 = 0.8, C V = 718 J/kgK, R = 218 J/kgK [4]. The results obtained are as follows: The absorptivity of the glass cover is considered in order to predict the top loss coefficient of the solar Stirling engine (See Fig. 6). Individual temperatures of the glass cover are evaluated and the same is further substituted in Eq. (1) to find top loss coefficient. It is found that the deviation in top loss coefficient and optical efficiency with and without considering the absorptivity of the glass cover about 0.3% in the case of LTDSE. From Fig. 4, it is also observed that there is a linear dependency among top loss coefficient and solar irradiation, which is well known. However, due

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0.52

5.3

0.5 5.1

0.48

4.9

0.46 0.44

4.7

Thermal Efficiency

Top loss coefficient (W/m2K)

5.5

0.42

4.5 350

550

750 Solar Radiaon, It (W/m2)

Top loss Coefficient with Absorptance Opcal efficiency with Absorptance

950

0.4

Top loss Coefficient without Absorptance Opcal efficiency without Absorptance

Fig. 6 Variation of irradiation and absorber plate temperature with day time

6

7

5

6 5

4

4

3

3

2 Pmax Thermal Efficiency Srling Cycle Maximum thermal efficiency of solar Srling engine

1 0 7:00

8:00

9:00

10:00 11:00 12:00 13:00 14:00 15:00 16:00 Day Time

Efficiency (%)

Maximum power output (W)

to the direct influence of the absorber plate temperature on Eq. (6), there exists a significant variation (7%) in optical efficiency. When it comes to the maximum thermal efficiency of the solar Stirling engines, the effect of optical efficiency becomes further crucial (see Fig. 7). It is found that there exists a maximum deviation of 3% approximately between thermal efficiency with and without considering top losses. Experimentally, the efficiency of solar LTDSE considered for this investigation is around 1.3%. The maximum thermal efficiency by modified FTT analysis with top losses is around 2–3.5% time and without considering top losses is around 5–7% at peak time. This observation further supports the fact that

2

1 0

Fig. 7 Variation of maximum power output and maximum thermal efficiency with day time

Effect of Top Losses and Imperfect Regeneration on Power Output … 2

25

1.8

Power output (W)

20

1.6 15 1.4 Maximum power output

10

Thermal efficiency 5

Efficiency (%)

Fig. 8 Variation of power output and thermal efficiency with the regeneration process time

293

0

0.0005 0.001 0.0015 Time for regeneraon process τr (sec)

1.2 1 0.002

by incorporating top losses with FTT analysis provides a much realistic prediction of solar LTDSE performance. The effect of time for regeneration process on thermal efficiency and power output of solar LTDSE is illustrated in Fig. 8. The thermal efficiency and power output of the engine increase and reach a peak regenerative time of 0.0005 s and 0.0003 and thereafter decrease, respectively. The decrease in thermal efficiency is negligible whereas the decrease in power output is about 8 W when there is an increase in regenerative time 0.002 s. This is because longer regenerative time duration increases the thermal efficiency by adding more heat during the isochoric process [12]. This can only be achieved by reducing the engine speed, which decreases the power output. Thus, there needs to be an optimal regenerative time with respect to the speed of a practical solar LTDSE. This can be achieved by selecting proper regenerative materials and working fluids for solar LTDSE.

5 Conclusions The influence of top loss coefficient and absorptivity of the glass cover on the performance of solar LTDSE is investigated and found that • the absorptivity of the glass cover has a negligible effect on the thermal efficiency of the solar LTDSE. • the thermal efficiency of solar LTSDE is deviated by 3% if top losses are not considered. • the power output can be increased with negligible drop in thermal efficiency by operating the solar LTDSE at higher speed, i.e., by reducing the regenerative time. • by incorporating top losses to FTT approach, it is now possible to fairly predict the performance parameters of the solar LTDSE.

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Therefore, the modified FTT approach adopted here can be used to design the Solar LTDSE effectively at the preliminary stage of engine design.

References 1. Hachem, H., Gheith, R., Aloui, F., Ben, Nasrallah S.: Technological challenges and optimization efforts of the stirling machine: a review. Energy Convers. Manag. 171, 1365–1387 (2018) 2. Ahmadi, M.H., Sayyaadi, H., Dehghani, S., Hosseinzade, H.: Designing a solar-powered stirling heat engine based on multiple criteria: Maximized thermal efficiency and power. Energy Convers. Manag. 75, 282–291 (2013) 3. Tlili, I.: Finite time thermodynamic evaluation of endoreversible Stirling heat engine at maximum power conditions. Renew. Sustain. Energy Rev. 16, 2234–2241 (2012) 4. Yaqi, L., Yaling, H., Weiwei, W.: Optimization of solar-powered stirling heat engine with finite-time thermodynamics. Renew Energy 36, 421–427 (2011) 5. Wang, K., Sanders, S.R., Dubey, S., Choo, F.H., Duan, F.: Stirling cycle engines for recovering low and moderate temperature heat: a review. Renew. Sustain. Energy Rev. 62, 89–108 (2016) 6. Ahmadi, M.H., Ahmadi, M.-A., Pourfayaz, F.: Thermal models for analysis of the performance of stirling engine: A review. Renew. Sustain. Energy Rev. 68, 168–184 (2017) 7. Boutammachte, N., Knorr, J.: Field-test of a solar low delta-T stirling engine. Sol. Energy 86, 1849–1856 (2012) 8. Li, R., Grosu, L., Queiros-Condé, D.: Losses effect on the performance of a gamma type stirling engine. Energy Convers. Manag. 114, 28–37 (2016) 9. Akhtar, N., Mullick, S.C.: International journal of heat and mass transfer effect of absorption of solar radiation in glass-cover on heat transfer coefficients in upward heat flow in single and double glazed flat-plate collectors. Int. J. Heat Mass Transf. 55, 125–132 (2012) 10. Martaj N, Grosu L, Rochelle P. Thermodynamic study of a low temperature difference stirling engine at steady state operation. Int J Thermodynamics. 10:165–76 (2007) 11. Tavakolpour AR, Zomorodian A, Akbar Golneshan A.Simulation, construction and testing of a two-cylinder solar Stirling engine powered by a flat-plate solar collector without regenerator. Renewable Energy. 33:77–87(2008) 12. Dai DD, Yuan F, Long R, Liu ZC, Liu W.: Imperfect regeneration analysis of Stirling engine caused by temperature differences in regenerator. Energy Convers Manag 158, 60–69 (2018)

Investigations on Recovery of Apparent Viscosity of Crude Oil After Magnetic Fluid Conditioning A. D. Kulkarni

and K. S. Wani

1 Introduction Transportation of viscous crude oil through subsea pipelines is a critical energyintensive job in the oil industry. When the temperature of crude oil falls below the wax appearance temperature (WAT), paraffin wax precipitates which increases the apparent viscosity of crude oil. This requires extra pumping power thereby leading to an increase in the number of pumping stations. The overall result is decreased production rate, equipment breakdown and production shutdown [1]. Heating of pipelines is the most commonly used treatment method. It is an efficient method but the energy requirements make it highly expensive. On the other hand, magnetic fluid conditioning method claims to be economical. It is based on the fact that when crude is treated with magnetic field, the apparent viscosity decreases thereby easing the transportation of oil. The mechanism of reduction in viscosity is based on two different theories, viz. the aggregation theory and the disaggregation theory. The aggregation theory is based on Einstein’s suspension theory as interpreted by Tao [2] which accounts for the localization of paraffin particles when subjected to magnetic field thereby reducing the viscosity. On the other hand, the disintegration theory states that magnetic treatment results in the disaggregation of paraffin particles in crude oil. Under normal circumstances, as temperature approaches WAT, the paraffin particles begin to agglomerate. Energy induced by the magnetic field disintegrates these particles. They acquire weak dipole moments and get aligned in the direction of the magnetic field. These A. D. Kulkarni (B) Department of Petroleum and Petrochemical Engineering, Maharashtra Institute of Technology, Pune, India e-mail: [email protected] K. S. Wani Department of Chemical Engineering, SSBT College of Engineering and Technology, Jalgaon, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_29

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dipoles generate repulsive forces and lead to disturbance in the crystal agglomeration process. The rheological properties change and the viscosity of the oil decreases. Experimental evidence in the literature has been found in the favour of the disintegration theory. Loskutova [3] considered crude oil as a dispersion of paraffins, asphaltenes and resins in lower hydrocarbons. Application of magnetic field leads to the destruction of this colloidal structure and resulted in a decrease in viscosity. Using scanning electron microscopy, Rocha [4] had shown that the embryos of paraffins which just began to grow in size near the WAT underwent disintegration under the action of magnetic field. Experiments by Evdokimov [5] showed that oils when subjected to magnetic field underwent ultraviolet (UV) spectrum extinction. This suggested a decrease in the size of suspended particles as a result of transient break up of hydrogen bonds in the hydrocarbons. Jiang [6] performed tera-hertz time-domain spectroscopy to study the aggregation characteristics of crude oil constituents under the action of magnetic field. The decrease in the extinction coefficient suggested the disaggregation of suspended colloidal particles. The interlocked paraffin particles underwent disintegration when subjected to magnetic field and regained the original state when the field subsided. Moreover, the reduction in viscosity is not permanent. The apparent viscosity tries to regain its original value. But the time for recovery as well as the degree of recovery has been found to vary. Rocha [4] and Zhang [7] had obtained a complete recovery in 8 h irrespective of the composition of crude oil, time of exposure and the type and intensity of magnetic field. Tung [8] had achieved a viscosity recovery of around 95% in 14 h. On the other hand, Loskutova [3] recovered the initial value of viscosity in 24 h which further exceeded by 20%. In another experiment, Loskutova [9] observed that the recovery of viscosity started after 2 h and took more than 24 h to reach the original value. The time required for the viscosity to regain its original value is of prime importance. If the viscosity regain is fast then the magnetic treatment will become less effective. This will result in the deposition of paraffins leading to an increase in the pressure drop. Therefore, its knowledge can help in the design of pipelines along with the number of pumping stations required to affect the transportation of crude oil. The reduction in the heating requirements and number of pumping stations can improve the energy efficiency and overall economics. The objective of this paper was to investigate the recovery pattern of viscosity after magnetic treatment in terms of % regain of viscosity and total time of recovery. Experiments were performed at the most optimum conditions suggested in the literature by varying the magnetic field for crude oils with different wax content. The data thus obtained was used for the explanation of possible mechanism for the trend.

2 Materials and Methods Crude oils (C1 , C2 and C3 ) obtained from western parts of India were characterized for density at 15 °C, 0 API, wax content (%), asphaltene content (%), wax appearance

Investigations on Recovery of Apparent Viscosity …

297

a

b

Fig. 1 Experimental set-up (a) Electromagnet (b) Brookfield viscometer with temperature control

temperature (WAT) (°C) and viscosity at WAT (cP) as per the standard procedures. Magnetic field was generated using electromagnet set-up (EMU-50 V obtained from M/s Scientific Equipment and Services, Roorkee, India) consisting of a U-shaped soft iron yoke with two pole pieces having 50 mm diameter each (Fig. 1a). Accurately measured crude oil samples (C1 , C2 and C3 ) of volume 50 ml were held stationary between the pole pieces of the electromagnet. These were subjected to electromagnetic fields of strength 1000, 3000, 6000 and 9000 gauss for a period of 1 min at the WAT which was the most effective time as per the literature [4]. The viscosities were measured using Brookfield DVII+viscometer with temperature control mechanism (Fig. 1b) at time t = 0, 1, 2, 3, 4, 5 and 6 h. The final readings were taken at 24 h.

3 Results and Discussion Table 1 shows the physico-chemical properties of crude oil samples.

Table 1 Properties of crude oils used in experimentation C1 Density (g/cc) at 15 °C

0.8754

C2 0.877

C3 0.887

Method Hydrometer

0 API

30.1

29.8

28

Formula

Wax content (%)

25.1

35.5

45.7

UOP 46–64

Asphaltene content (%)

12.2

5

7.2

WAT

(0 C)

Viscosity at WAT (cP)

IP 143

30

43

50

Viscometry

69.3

55.4

82.1

Brookfield DVII+Pro viscometer

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Figures 2, 3 and 4 show the trend for regaining of viscosity after magnetic conditioning of samples C1 , C2 and C3, respectively. The viscosity recovery patterns are found to be different for different magnetic fields and crude oils. Preliminary observations show that more the reduction in viscosity, more is the time taken to reach the original value. A thorough investigation reveals that it may not be the case always. It varies for different oils and magnetic fields. Sample C1 having lower wax content has greater viscosity reduction for higher magnetic fields and also takes more time for recovery. Accordingly one may expect that C2 and C3 having higher wax content will give greater viscosity reduction for higher magnetic fields. But it is not observed so. According to Tao [2], reduction in viscosity under the influence of magnetic field is dependent on the paraffin content, intensity of magnetic field and the time of exposure. If the time of exposure is increased, then the local aggregation of the paraffin particles increases thereby increasing the viscosity. Conversely, when the paraffin content and magnetic field increase for a given time of exposure, 75

Viscosity (cP)

70 65 60 55

50

0

2

Crude oil

4

6

8

10 12 14 16 Time elapsed (hours) 3000 gauss

1000 gauss

18

20

22

24

26

9000 gauss

6000 gauss

Fig. 2 Variation of viscosity with time elapsed for crude oil 1 (C1 ) when subjected to magnetic fields for 1 min 60

Viscosity (cP)

50 40

30 20

0

Crude oil

2

4

6

1000 gauss

8

10 12 14 16 Time elapsed (hours) 3000 gauss

18

20

6000 gauss

22

24

26

9000 gauss

Fig. 3 Variation of viscosity with time elapsed for crude oil 2 (C2 ) when subjected to magnetic fields for 1 min

Investigations on Recovery of Apparent Viscosity …

299

Viscosity (cP)

80 70 60 50 40 30

0

Crude oil

2

4

6

1000 gauss

8

10 12 14 16 Time elapsed (hours) 3000 gauss

18

20

22

6000 gauss

24

26

9000 gauss

Fig. 4 Variation of viscosity with time elapsed for crude oil 3 (C3 ) when subjected to magnetic fields for 1 min

the viscosity reduction would decrease. This is exactly observed for samples C2 and C3 . Moreover, paraffins are not the only entities responsible for viscosity reduction. Goncalves [10] had performed NMR spectroscopy on six different crude oils to find out the ratio of aromatic to aliphatic molecules and water content alongwith EPR spectroscopy to detect paramagnetic ions. It was observed that higher was the aromatic-to-aliphatic ratio better was the viscosity reduction. In the present experimentation, the asphaltene-to-paraffin ratio for C2 and C3 (0.14 and 0.16, respectively) is found to be less than that of C1 (0.48) and hence exhibit less viscosity reduction. Although the viscosity reduction is small for C2 and C3 as compared to C1 for higher magnetic fields, the rate of recovery is also less indicating that the effect of magnetic field remains for a longer time. This is in line with Rocha [5] who proposed that higher is the paraffin content more is the interaction with magnetic field.

3.1 Effect of Magnetic Field on % Regain of Viscosity The % regain of viscosity after a certain time interval is important from the viewpoint of transportation. More the time it takes to regain the viscosity, more efficient is the transportation with less pumping power. The % regain has been calculated as follows: % regain =

Increase in viscosity after maximum reduction at a given time interval Total viscosity reduction

Figures 5, 6 and 7 show the % regain in viscosity over a time period of 24 h. It is observed that the % regain varies for different magnetic fields for different crude oils. The viscosity recovery for C1 varies from 68% to 95% for different magnetic fields. Similarly, in case of C3 , it varies from 61% to 74%. The complete recovery of viscosity is not observed in case of C1 and C3 in a period of 24 h. Loskutova [11] had obtained complete recovery of viscosity for two of their samples. The other two

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samples could not attain their original viscosity even after 48 h. In case of C2 , the viscosity after 24 h exceeded the original by around 5%. The result obtained is in line with Goncalves [12] who has attributed the final viscosity increase to various factors like intermolecular forces and packing between the molecules.

3.2 Effect of Wax Content C1 , C2 and C3 have increasing order of wax content. It is observed that for the oils with less wax content, lower magnetic field leads to faster recovery whereas for higher wax content, higher magnetic field gives better recovery. One of the explanations for this behaviour has been by Loskutova [13]. Crude oils are considered as dispersions of asphaltenes, resins and paraffins in lower molecular weight saturates. These dispersions are thermodynamically unstable. The degree of dispersion changes with changes in external factors such as pressure temperature, chemicals and physical fields like magnetic field. When magnetic field is applied, paraffins undergo changes in structural pattern which in turn changes the viscosity. After the excitation subsides, the relaxation process begins and the viscosity regains its original value based on the phenomenon of thixotropy. Thus, more paraffinic crude will require more relaxation time to come to its original state.

3.3 Relation Between Initial Reduction and % Regain of Viscosity Figures 8, 9 and 10 show that less is the initial reduction more is the regain of viscosity. Magnetic field provides certain energy for change in the orientation of the 94.5

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60 40 19.9 20 0

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paraffin molecules. It is the inherent property of any system to regain its original state of rest or equilibrium. The more the equilibrium is disturbed, the faster the system tends to regain it. Hence, all the above cases show that the viscosity reduces with magnetic field and it tries to attain the original value after some time. But the time required for the recovery differs from case to case. The reason for this can be explained as follows. The reduction in viscosity due to magnetic field is due to disaggregation of the paraffin agglomerates in crude oil and subsequent alignment in the direction of the field. It is the inherent property of any system to regain its original state of rest or equilibrium. When the magnetic effect subsides, the paraffin particles lose their alignment and begin to form aggregates. The viscosity begins to increase. But this process has no driving force and is uncontrolled. The paraffin molecules are free to align themselves. The process is from orderliness to randomness. As time progresses, these molecules tend to regain their original state, i.e. random state as quickly as possible. Hence, there is no definite pattern or time interval for viscosity increase. Moreover, the composition of oil especially the asphaltene, paraffin and resin composition will play an important role. The interspecies forces as suggested by

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Original

t = 0 hours

t = 2 hours

Randomly arranged paraffins

Aligned aer magnezaon

Losing alignment

t = n hours

Regaining original random nature

Fig. 11 Schematic of regain of viscosity after initial reduction due to magnetic field

Shiryaeva [14] will affect the process. Hence, the time taken by different samples is different and it may or may not regain the original viscosity. Also, during the recovery process, these molecules may come too close to each other which will further reduce the viscosity during the relaxation process. This is evident from samples C1 and C3 as seen in Figs. 2 and 4. The viscosity increases and decreases in the first 3–4 h and finally continues to increase. The schematic of the process is shown in Fig. 11.

4 Conclusions The pattern for recovery of apparent viscosity of crude oil subjected to magnetic field has been studied. This regain of viscosity has been observed to take place between 8 h to more than 24 h. More is the initial reduction in viscosity, slower is the regain. The regain of viscosity depends on factors like the initial viscosity, the strength of magnetic field and the wax content of crude oil. The viscosity regained can exceed the original value. A mechanism for the same has also been discussed. Similarly, crude oils with higher wax content require more time for relaxation owing to increased interaction between the paraffins and magnetic field. The time for regain of viscosity can also be used to decide the pumping power, number of pumping stations and the heating requirements for transportation. The present findings can thus be used as a basis for performing detailed energy analysis of a crude oil trunk line. The study can be extended further by varying other parameters like the asphaltene content and the time of exposure to magnetic field.

References 1. Frenier, W.W., Ziauddin, M., Venkatesan, R.: Organic deposits in oil and gas production. 1st edn, Society of Petroleum Engineers (2010) 2. Tao, R., Xu, X.: Reducing the viscosity of crude oil by pulsed electric or magnetic field. Energy & Fuels 20, 2046–2051 (2006) 3. Loskutova, Y.V., Yudina, N.V.: Effect of constant magnetic field on the rheological properties of high-paraffinicity oils. Colloid J. 65, 469–473 (2003)

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4. Rocha, N., González, C., Marques, L., Vaitsman, D.S.: A preliminary study on themagnetic treatment of fluids. Pet. Sci. Technol. 18, 33–50 (2000) 5. Evdokimov, I.N., Kornishin, K.A.: Apparent disaggregation of colloids in a magnetically treated crude oil. Energy & Fuels 23, 4016–4020 (2009) 6. Jiang, C., Zhao, K., Zhao, L.J., Jin, W.J., Yang, Y.P., Chen, S.H.: Probing disaggregation of crude oil in a magnetic field with terahertz time-domain spectroscopy. Energy & Fuels 28, 483–487 (2014) 7. Zhang, W., Zhang, G., Dong, H.: The effect of magnetic radiation on pipeline transportation of crude oil. In: Proceedings of International Conference in Digital Manufacturing and Automation-ICDMA 2010. 2, pp. 676–678. IEEE, Changcha, China (2010) 8. Tung, N.P., Vinh, N.Q., Phong, N.T.P., Long, B.Q.K., Hung, P.V.: Perspective for using Nd-FeB magnets as a tool for the improvement of the production and transportation of Vietnamese crude oil with high paraffin content. Phys. B 327, 443–447 (2003) 9. Loskutova, Y.V., Yudina, N.V.: Rheological behavior of oils in a magnetic field. J. Eng. Phys. Thermophys. 79, 105–113 (2006) 10. Gonçalves, J.L., Bombard, A.J.F., Soares, D.W., Carvalho, R.D.M., Silva, M.R., Alcantara, G.B., Pelegrini, F., Vieira, E.D., Pirota, K.R., Izabel, M., Bueno, S., Maria, G., Lucas, S., Rocha, N.O.: Study of the factors responsible for the rheology change of a Brazilian crude oil under magnetic fields. Energy & Fuels 25, 3537–3543 (2011) 11. Loskutova, Y.V., Yudina, N.V., Pisareva, S.I.: Effect of magnetic field on the paramagnetic, antioxidant, and viscosity characteristics of some crude oils. Pet. Chem. 48, 51–55 (2008) 12. Gonçalves, J.L., Bombard, A.J.F., Soares, D.A.W., Alcantara, G.B.: Reduction of paraffin precipitation and viscosity of Brazilian crude oil exposed to magnetic fields. Energy & Fuels 24, 3144–3149 (2010) 13. Loskutova, Y.V., Prozorova, I.V., Yudina, N.V., Rikkonen, S.V.: Change in the rheological properties of high-paraffin petroleums under the action of vibrojet magnetic activation. J. Eng. Phys. Thermophys. 77, 1034–1039 (2004) 14. Shiryaeva, R.N., Kovaleva, L.A., Gimaev, R.N.: Improving the rheological properties of highviscosity crude oil. Modifying additive and high-frequency electromagnetic field. Chem. Technol. Fuels Oils 41, 36–38 (2005)

Investigation on Different Types of Electric Storage Batteries Used in Off-grid Solar Power Plants and Procedures for Their Performance Improvement Anindita Roy, Rajarshi Sen, and Rupesh Shete

1 Introduction Electric storage batteries are the central part of an off-grid solar photovoltaic plant. On-grid solar/wind farms and rooftop installations also need battery storage for ensuring maximum utilization of renewable energy, grid voltage/frequency stabilization and peak load shifting. There are about 14,000 micro-/mini-grids (DC and AC) in India, with many new off-grid and grid hybrid installations coming up [1]. All these plants have solar PV capacity generally in range of 250 W to 100 kW with battery for providing backup for couple of hours to over twelve hours. However, failure of batteries within four years of installation leading to degradation of plant output has troubled customers [1]. From extensive survey of off-grid power plants and revival of some of them, it was found that most of the degradation had been due to lack of proper operating information on batteries [1]. While selection of battery type and sizing were a part of the cause, the problem in understanding battery operation and maintenance in solar PV plants was the main factor leading to capacity degradation. Electrical storage applications in off-grid solar power plants usually employ lead acid batteries. These batteries are typically designed for daily deep cycling (discharge and recharge) applications, and as the solar radiation is available for a limited period for charging the battery, these are prone to periods of low or partial charge followed by very deep discharge. Degradation of lead acid battery capacity on successive cycling is a common phenomenon observed. Positive grid corrosion is one of the main reasons for capacity degradation in stationary batteries which are charged by float current [2]. Other factors contributing to capacity degradation are i) positive plate corrosion, ii) positive active mass (PAM) degradation and contact loss with grid, and iii) sulphation. During normal cycling, corrosion of positive plate naturally A. Roy (B) · R. Sen · R. Shete Pimpri Chinchwad College of Engineering, Pune 411044, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_30

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occurs as metallic lead plate is thermodynamically unstable [2]. However, a layer of corrosion also protects the grid from further fast corrosion. Nevertheless, excessive corrosion reduces conductivity of grid, thus reducing overall capacity. High voltage, acid concentration and high temperature are three major factors instrumental in accelerating corrosion [3]. Adding element like antimony, selenium or calcium with lead to form a compact metallographic structure helps to reduce rate of grid corrosion. Equation (1) shows the reaction occurring during charge and discharge in a lead acid battery: PbO2 + Pb + 2H2 SO4 = PbSO4 + PbSO4 + 2H2 O

(1)

During discharge, lead dioxide (PbO2 ) gets converted to lead sulphate (PbSO4 ) which has larger volume relative to PbO2 . This results in morphological shape change of the PAM [2]. As a result, there is a reduced contact between the PAM and the grid, causing loss of contact. Higher depth of discharge [3] and repetitive cycling are responsible for softening of PAM which may get detached. Shedding (detachment of active mass) and slugging may cause short circuit. Lead antimony grids are less affected by it than antimony free grids [4]. Use of high density pastes and additives can help in reducing active material softening [4]. Further, during discharge reaction, dilute sulphuric acid (electrolyte) reacts with PbO2 and Pb to form soft lead sulphate crystals which are deposited on the plates. If the battery remains in a partially discharged condition, some of the soft lead sulphate crystals do not get converted to lead dioxide/lead and these aggregate into larger and hence hard crystals over time. The plates thus loose porosity and are difficult to convert back to soft active materials, viz., lead and lead dioxide during every day recharge by solar. Formation of irreversible hard sulphate resulting in loss of capacity in active mass is referred to as sulphation. It is mainly caused by two reasons (i) batteries are not fully charged for longer duration of time or left idle for quite long and (ii) electrolyte stratification. Stratification of electrolyte causes charge to be unequally distributed, resulting in undercharging of certain portion of the cell, thereby leading to sulphation [5]. Battery thus loses its capacity partially. Further, this hard sulphate acts as resistance to energy output of solar power plants. Timely equalizing or full charging with proper mixing of electrolyte by using pump is a possible remedy to reduce sulphation. This study reports the results of capacity degradation on cycling under laboratory conditions for different types of solar batteries. Tests were designed and performed by simulating solar charging conditions in the laboratory. A trend of capacity degradation was observed across different types of solar batteries. The remedial action is suggested, applied and found to revive the battery to its full capacity. Further, it was possible to estimate the frequency of servicing the solar power plant batteries so as to get a higher service lifetime.

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2 Methodology Figure 1 illustrates the schematic arrangement of a DC-coupled photovoltaic battery system used for off-grid electrification. In such a configuration, the photovoltaic array is coupled to the DC bus though a DC–DC charge controller. The solar photovoltaic modules are connected in series or parallel, depending on the total voltage and current required to be supplied. The power from the solar array is collected in one or more junction boxes and thereafter fed into one or multiple solar charge controllers. The electric storage batteries are charged by DC current and voltage from these solar charge controllers. The charged batteries in turn, supply pure AC power on demand, through an inverter to the AC load. The objective of the present study is to study the cycling performance of the storage battery and its charge/discharge controls that may lead to the performance, efficiency and life enhancement of the solar power plant. The limited 7.5–8 h of solar charging during a day is insufficient to ensure full charge. In order to study the performance of batteries used in off-grid solar power plants, following methodology was adopted: 1. Selection types of solar application battery 2. Determining capacity of the battery through capacity tests 3. Simulated solar cycling tests to study degradation on cycling. Each of these steps is described in the following sections.

2.1 Battery Selection Lead acid batteries used in solar power plants can be categorized broadly into two major types, viz., flooded lead acid and valve regulated lead acid (VRLA) or sealed batteries. Figure 2 shows the classification of various types of batteries used in solar

Photovoltaic Array AC Load

Inverter Solar Charge Controller

AC Bus

Battery

DC Bus

Fig. 1 Schematic and power flow in a DC-coupled off-grid photovoltaic-battery systems

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Flooded

Flat Plate

Tubular

Thin tubular Plate

Thick tubular Plate

VRLA

AGM lead calcium

Gel electrolyte

Tubular Plate

Lead Carbon

Flat Plate

Fig. 2 Classification of solar lead acid batteries

and wind solar hybrid power plants. While flooded batteries contain dilute sulphuric acid in liquid form as electrolyte which needs to be replenished with distilled water from time to time in order to make up for the water loss by electrolysis and evaporation, VRLA batteries have their electrolyte in the form of a semi-solid gel requiring no maintenance or topping up with distilled water. Flooded batteries are available in two configurations of electrodes, viz., tubular plates and flat plate. Due to increased positive plate surface area, tubular batteries have 20% more electrical capacity than flat plate batteries of comparable size and weight. Further, tubular batteries also provide up to a 30% longer service life than flat plate batteries due to reduced positive plate shedding [6]. The major advantage of flooded battery is their ruggedness and ability to survive abusive conditions as in solar power plants. Valve regulated lead acid (VRLA) batteries are available in three types, viz., absorptive glass mat (AGM), gel electrolyte and lead carbon foam batteries. VRLAAGM batteries have both positive and negative plates of flat plate construction, lead calcium grids and an absorptive glass mat separator, designed for holding electrolyte in its pores and allowing recombination of gases, thus ensuring no water loss during charge. They are primarily used in UPS and telecom applications. However, a large number of them are also deployed in solar PV applications due to the advantage of no regular requirement of topping up with water. VRLA battery with gel electrolyte operates on a gas recombination technology similar to AGM VRLA battery. The difference being that tubular positive plates (different than flooded tubular) are used instead of flat pasted positive plates and the electrolyte absorbed in silica gel is used. There is a charge voltage limit as in AGM VRLA but tubular positive plates provide a high cycle life as in flooded tubular batteries, that is, 1200 cycles or more at 80% DOD, 2500 cycles at 50% and 5000 cycles at 20% DOD. The lead carbon foam battery is similar to AGM VRLA but the negative plates have a carbon foam grid which is designed to aid in very efficient charge and discharge due to the relatively better current conductivity of carbon as compared to the lead antimony or lead calcium used in other batteries.

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It is necessary to know the capacity of the battery in Ah as specified by the manufacturer before undertaking detailed cycling tests. The procedure and results of capacity tests on these batteries are described in the following sections.

2.2 Capacity Test A capacity test is required to determine the battery capacity (Ah) at various discharge currents. Depending on the connected load, a solar power plant may be required to supply current in a certain range. It is necessary to predict the battery cut-off voltage during a discharge at a specific rate. This helps in selecting the inverter cut-off. Wrong setting of inverter cut-off voltage without taking into account the discharge current may lead to either over discharge or low capacity utilization of the battery. This can be done by discharging the battery at various currents and noting down its end voltage and capacity by calculating Ah discharged. For instance, if the discharge rate of a battery is 0.1 C, then for a 100 Ah battery, the discharge current is 0.1 × 100 = 10 A. Similarly, for discharge rate 0.15 C, the discharge current is 0.15 × 100 = 15 A. If the discharge current, its duration and the lowest permissible voltage on discharge rate are known, the required capacity of battery can be calculated. Figure 3 shows the discharge characteristics of a flooded tubular plate solar battery after capacity tests from 0.05 to 0.5 C. It is observed from Fig. 3 that at 10 A (C10) discharge, the battery was able to supply the load for more than 615 min. However, when a higher discharge current was used (20–50 A), the capacity of the battery was observed to be reduced from 78.2% at 20 A to about 50% at 50 A rate. Table 1 summarizes the inferences from the capacity test done at different discharge rates (currents). Discharge duration obtained from laboratory tests and those specified by 2.15

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Fig. 3 Discharge voltage ss.time characteristics of 105 Ah flooded lead acid battery derived from repeated capacity tests in the laboratory

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Table 1 Approximate output as a percentage of C10 capacity Maximum discharge duration in hours for flooded tubular battery (IS13369: 1992)

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IS13369:1992 are compared. It is seen that the battery has given approximately the same or more discharge duration when a constant discharge current was maintained. The approximate output, as a percentage of C10 capacity, is also calculated depicting reduction in capacity at higher discharge rates than C10. Slightly higher discharge durations are obtained in certain tests. This is attributed to the variation in the current during the tests owing to the limitation of charging devices. Overall, it may be concluded that the test results have validated the manufacturer’s specification.

2.3 Simulated Solar Cycling Tests Off-grid photovoltaic plant batteries are charged when sunlight is available and discharged as per load demand. A battery used in a solar photovoltaic energy storage application picks up a little lesser charge in every cycle of discharge and recharge. This is because, the regulation voltage setting in the charge controller limits the charging current after reaching the 80% charge state(referred to as the gassing point voltage) in order to avoid overcharge. This limited current available for about 7– 8 h/day cannot fully recharge the battery. As a result, some of the lead sulphate formed during discharge is not converted back into active materials and becomes harder with repeated cycling. Field experience of the authors in revival of batteries in failed solar power plants has proved that in absence of proper operation and maintenance of batteries; most solar power plants loose almost 50–60% of their output in 3–4 years. By the fifth year, the battery fails catastrophically [1]. In order to avoid failure of the batteries, periodic full charge of the battery is necessary to convert the sulphates into active material. The process of full charge takes about 20 h and is referred to as equalizing charge. Prolonged equalizing charges at low current (3– 4% of battery capacity) help in reducing hard sulphation and revive the battery to improved capacity. The stepwise procedure for equalizing charge is as follows. • Charge the battery with the solar charge controller in boost charge mode and continue till the voltage of 2.4 VPC is reached. While 2.4 VPC setting is preferable

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for quickly charging the battery, many charge controllers have a single setting of 2.25 VPC. The recommended charging cut-off voltage in flooded and sealed VRLA batteries is 2.45 to 2.5 VPC and 2.35 to 2.4 VPC, respectively. Put the controller on equalizing charge mode. For equalizing charging to a flooded battery, a voltage setting of 2.7/2.6 VPC is needed, that is 64.8 V/62.4 volts for a 48 V battery. For VRLA batteries, a voltage of 2.45 VPC or 57.6 for 48 V battery is required. Controllers should have separate settings for flooded and VRLA batteries but most have only one setting at 2.5 VPC. Continue charging at a low constant current till battery voltage reaches and remains constant at the maximum voltage setting for 2–3 h. Equalizing charge current should be limited to 3% of the battery capacity in amperes. For example, the equalizing current for a 100 Ah battery should be 3 A and that for a 600 Ah battery should be 18 A. The solar charge controller should have a current limiting function during equalizing mode. Check temperature rise during the entire process and discontinue charging if it increases by more than 4–5 °C during the equalizing charge. Continue charging after temperature drops. After equalizing, give it a rest for a few hours and then check the battery specific gravity as well as open circuit voltage of flooded and VRLA battery, respectively. If they show full charge, battery backup will increase to original level.

The following section describes in detail the performance of flooded tubular and gel electrolyte VRLA batteries in solar simulated laboratory conditions.

3 Cycling Test on Batteries Used for Solar Power Applications In a cycling test, batteries are discharged up to specific voltage during in every discharge. The battery in every cycle was discharged up to a fixed voltage corresponding to 80% depth of discharge or rather, a 20% state of charge (SOC). Change in capacity in every cycle is calculated from the Ah obtained during the entire discharge time period of Td . This is done by constantly recording the discharge current (I d ) for every time step t (e.g. 2–10 min) stating from an initial time t = t i up to the end of the discharge t = T d . A summation of the all these readings over the entire discharge time horizon of T d gives the Ahout as follows: Ahout =

t=T d

Id t

(2)

t=ti

Solar charging conditions were simulated by setting the charging current to 0.12 C constant current followed by 2.4 VPC constant voltage for total 8 h. During the recharge, the energy fed into the battery in Ah is similarly recorded over a time

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horizon of T c by discretely recoding the charging current(I c ) for every time step t (e.g. 2–10 min). Thus, Ahin is determined as: Ahin =

t=T c

Ic t

(3)

t=ti

Figure 4 illustrates the results of cycling tests conducted on a C10 rated flooded lead acid battery of 105 Ah for about 60 days in laboratory conditions. The charge acceptance and hence the Ah input into the battery is a function of the regulation voltage which is set in the charge controller. In the initial 12 cycles (Fig. 4a), the regulation voltage was set at 2.7 VPC. Hence, a higher charge was put in the battery. As the discharge was limited up to the 80% DOD, this resulted in a lower Ah efficiency. Therefore, in the next set of cycles (i.e. 13–51), the regulation voltage was lowered to 2.4 VPC. This improved the Ah efficiency from earlier ~82 to ~92% as seen from the reduced difference between and charge (input) and discharge (output) obtained (Fig. 4b). It is noted that on the 26th cycle (on 26th day), the battery output diminished from 80 to 56 Ah, which is a drop of 30% capacity in 26 cycles. Equalizing charge was imparted on 27th cycle as per the procedure for equalizing charge mentioned in Sect. 2.3. The charging was possible by raising the charging voltage to 2.75 VPC as against the regular cycling charge voltage of 2.4 VPC to overcome high internal

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Fig. 4 a Capacity degradation of flooded flat plate battery at 80% DOD during cycling with charging regulation voltage at 2.7 VPC b capacity degradation of flooded flat plate battery during cycling at 80% DOD and charging regulation voltage at 2.4 VPC at laboratory solar simulated conditions

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resistance of battery. On the 28th cycle, the battery regained 100% capacity and provided the same 80% output or 80 Ah as on 1st day/1st cycle. The battery performed much better for next 25 days, and then, next equalizing charge was due. Figure 5 shows the effect of cycling on the capacity drop of a VRLA gel electrolyte battery under solar simulated laboratory charge and discharge cycles. The charge controller regulation voltage was set to 2.4 VPC. The discharge capacity reduced from 121 Ah in the first cycle to 105 Ah in the 22nd cycle showing 13.2% degradation. Equalizing charge was imparted on the 23rd cycle and subsequent discharge capacity was obtained as 120.8 Ah. Thus, 100% revival of capacity was obtained on equalizing charge. In order to investigate the effect of regulation voltage, setting the charge controller regulation voltage for charging was set at 2.25 VPC from the 24th cycle (as against 2.4 VPC in the earlier cycles). However, it is noted that the battery charge acceptance is seen to fall drastically to about 26% showing a case of undercharging (Fig. 5b). During cycling from 23 to 31, the capacity degradation is also found to increase to about 21% in comparison to 13.2% drop observed in cycle 1–22. Thus, the charge controller is seen to be unable to provide required energy to Input Ah

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Fig. 5 a Capacity degradation for VRLA gel electrolyte battery at 80% DOD and regulation voltage of 2.4VPC during cycling in laboratory at solar simulated conditions b capacity degradation on cycling at regulation voltage of 2.25 VPC

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Table 2 Observations on capacity degradation on cycling Test performed Battery type

Observation

Inference

Cycling test

• 30% capacity degradation of in 25 days. • Capacity drop of 1.2% per cycle at 2.4 V/cell cut-off voltage.

• Frequency of equalization needed is once per month

Flooded tubular

VRLA gel electrolyte • 13.2% Capacity degradation of in 22 days. • Capacity drop of 0.56% per cycle at 2.4 VPC cut-off voltage. • Lower charge acceptance (undercharging) at 2.25 VPC

• Frequency of equalization needed is once in two months • Optimum charge voltage setting is 2.4 VPC

recharge the battery from last discharge resulting in undercharging and progressive lower output due to sulphation. An undercharge may occur due to lower regulation voltage setting of the charge controller or inadequate capacity of solar array. Table 2 provides an overview of the performance on cycling duty for both the battery types tested. It is clearly noted that while the flooded batteries are rugged, their capacity degradation on cycling is of the order of 1.2% per cycle. However, the VRLA battery subjected to similar charge–discharge cycles degraded by about 0.56% per day. Improved performance of the VRLA gel electrolyte battery may be attributed to better physical contact between electrodes and electrolyte. Further, the capacity drop was also found to be a function of the charging cut-off point. It was noted that when the charging cut-off was lowered to 2.25 VPC, there was a 26% degradation of the battery on cycling.

4 Conclusions The capacity of battery reduces progressively during cycling duty in off-grid photovoltaic power plants. In this work, the capacity degradation of batteries used in solar power applications was studied by through experimentation in laboratory conditions. Through the testing, it has been shown that (i)

The batteries were found to be losing both charge input and energy output in every cycle. Capacity degradation is thus universal in such batteries.

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(ii) It was possible to simulate the capacity degradation process with a battery set and SPV plant in laboratory by daily charging the battery with SPV and discharging the battery up to 20% state of charge in the night. (iii) A pattern of degradation was found—that a flooded tubular battery degrades by 1.2% in each cycle when discharged by 80% each cycle (day), whereas a VRLA gel electrolyte battery degrades by 0.56% per cycle under similar conditions. This provided an indication of the service recharge/equalizing periodicity. (iv) Almost full capacity of battery can be recovered by a service charge process with controlled low current and high voltage charging also known as equalizing charge in common battery parlance—This proved that the degradation was only temporary and can be revived by proper servicing. (v) A proper equalizing charge requires an additional 10 h over the 80% state of charge(SOC) point of the battery or about 20 h from 20% SOC. It is proven through the tests conducted on solar power plant batteries that their life can be prolonged to the designed lifetime by maintaining proper periodicity of equalizing charge. This periodicity depends on the type of battery used and the depth of discharge. Commercial solar charge controllers do have an equalizing mode; nevertheless, it is ineffective as the entire equalization takes place in duration of 1–2 h against the prescribed time of 20 h required for full equalization as obtained from laboratory tests. Acknowledgements This work was supported by Customized Energy Solutions Pvt Ltd., Pune under the Clean Energy Access Network (CLEAN) project, Award Number AID-386-A-14-00013. The authors are thankful to Mr. Viral Patel and Ms. Chaitra Dandavatimath who were instrumental in carrying out all the tests described in this manuscript.

References 1. Clean Energy Access network ‘A Detailed Manual On Lead Acid Battery Operation & Maintenance For Solar PV Plants’ Homepage. http://www.thecleannetwork.org/resources/reports-pub lications/technology/. Last accessed 11 Feb 2019 2. Ruetschi, P.: Aging mechanisms and service life of lead–acid batteries. J. Power Sources 127(1– 2), 33–44 (2004) 3. Brik, K., Ben Ammar, F.: The fault tree analysis of lead acid battery’s degradation. J Electr Syst 4–2, 1–12 (2008) 4. May, G.J., Davidson, A., Monahov, B.: Lead batteries for utility energy storage: a review. J Energy Storage 15, 145–157 (2018) 5. Merrouche, W., Achaibou, N., Bouzidi, B., Kasser, M., Trari, M.: Lead-acid battery degradation mechanisms in photovoltaic systems PVS. In: The 3rd International Workshop on Integration of Solar Power into Power Systems SIW2013, At London, UK, Volume: 2013 6. Comparison between Flat and Tubular Positive Plates, White paper: Storage Battery Systems, LLC Homepage. https://www.sbsbattery.com/PDFs/SBS_WP_101_BattComp-WithRefs.pdf. Last accessed 12 Feb 2019

Saving Electricity, One Consumer at a Time K. Ravichandran, Sumathy Krishnan, Santhosh Cibi, and Sumedha Malaviya

1 Introduction Residential electricity consumption in India has tripled since 2000 [1]. It is further projected to rise by more than eight times under the business-as-usual scenario [2]. Urgent efforts are needed to curtail this rise and mitigate emissions from the sector. Demand Side Management (DSM) is a widely implemented and recognized concept that utilities globally have implemented to counter rising energy demand. “Demand Side Management” means the actions of a Distribution Licensee, beyond the customer’s meter, with the objective of altering the end-use of electricity— whether it is to increase demand, decrease it, shift it between high and low peak periods, or manage it when there are intermittent load demands—in the overall interests of reducing Distribution Licensee costs [3]. Historically utilities in India have promoted energy-efficient lights, ACs, and refrigerators through replacement or buy-back schemes offering the energy-efficient alternative at a discount. However, while these technological interventions may substantially bring down electricity consumption, the role of behavior in selecting those technologies, and using them, to deliver the savings remains crucial. Consumer behavior is complex and routinely deviates from rational economic choices. A growing volume of research on energy-consumption behavior of households points to the deviation from the expected impact [4]. Consumers demonstrate behavior driven by their biases, motivations, and social norms. Understanding and changing these motivations is the key to making Energy Efficiency (EE) policies that respond to different types of customers living under different social, demographic, and cultural situations. K. Ravichandran · S. Krishnan (B) · S. Cibi Technology Informatics Design Endeavour, Bangalore, India e-mail: [email protected] S. Malaviya World Resources Institute India, Bangalore, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_31

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Recognizing this need, academics and researchers in the stream of Behavioral economics across the globe have been studying consumer behavior towards electricity use and have concluded that changes in consumer behavior, attitudes, and practices can contribute to electricity savings if the right nudges are provided to consumers [5]. Studies done by utilities in collaboration with behavior scientists in developed countries have experimented with nudges of different types to manage electricity use. But behavior is local and not global in character, so such programs have limited replicability across geographies. This challenge is accentuated in a diverse country like India, requiring a disaggregated approach (based on income levels, size of houses, etc.) to influencing behavior choices. Unfortunately, data or evidence to guide such approaches are missing. Also, behavior change focused programs need long-term implementation to warrant evaluation of the interventions. VidyutRakshaka promoted by a civil society organization and a research organization is a first of its kind attempt in India to use behavior change strategies for sustained reduction in electricity consumption among residential electricity consumers in Bangalore. Its uniqueness comes from the fact that it is an ongoing and growing field-level program, uses bottoms-up data for designing customized nudges, and has a partnership with the utility.

2 Review of Behavior Change Initiatives for Electricity Conservation In an analysis done by European Environment Agency and other partners [6], savings from behavior change programs typically range from 5 to 15% and comprise of both antecedent (pre-program) interventions like information, goal-setting and commitment and consequent (post-program) measures like feedback and rewards [7]. A JPAL study reports a two-percentage reduction in an analysis of Home energy reports on energy consumption by the company OPOWER in the United States [8]. This program has gone beyond the pilot stage and is operational in twelve utilities in the US. A small pilot in Bangladesh reported 9% savings based on nudges like the feedback given to consumers [9]. Researchers in India have captured large variations in electricity consumption even among the consumers holding similar assets and indicate the role of consumer behavior in these differences [10]. Most recently, in partnership with Oracle, Delhi-based utility, BSES Rajdhani rolled out a behavior change program for 5 lakh consumers in New Delhi in 2018. The only other energy use behavior study in India with a small sample of households

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In Delhi, reported that nudges in the form of comparison of electricity use with neighbors resulted in 7% energy savings [11]. The same study reported an increase in electricity use in households given the same nudge but with additional financial rewards. VidyutRakshaka is recognized as DSM initiative by the public electricity utility BESCOM in Bengaluru [12].

3 VidyutRakshaka VidyutRakshaka was conceptualized and is being implemented as an action-oriented program. It has evolved from a pilot of about 500 consumers in two residential neighborhoods of Bangalore in 2014–15. Based on the experience and pilot’s results, a larger city-wide enrollment drive was initiated in 2016 and has resulted in about 3800 voluntary signups for the program as of January 2019. The basic tenets of the program are: • Conservation forms the foundation for any energy efficiency efforts. Impacts of energy efficiency without promoting energy conservation are not sustainable and are known to have a rebound effect [13]. • Energy conservation is primarily driven by behavior change; and positive nudges can encourage conservation. • Continuous reinforcement of conservation measures combined with knowledge on efficient appliances and renewable energy can lead to long-term changes towards sustainable consumption. The program’s unique strengths are building capacity in local communities through one-on-one customized engagement and leveraging social/community influences. The program invites engagement from various stakeholders (consumers, consumer groups, utility) and has helped create a data-driven platform for deeper research to drive policies. Most importantly, it enables consumers to take control of their electricity consumptive actions.

3.1 The Rationale VidyutRakshaka combines both antecedent (information and goal-setting) and consequent interventions(feedback) as highlighted in the schematic Fig. 1 describing the process flow. Feedback is primarily the report with recommendations and nudges which were carefully worded to avoid paternalism or bias to a particular solution.

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Fig. 1 Energy conservation through behavior change

4 Methodology The methodology adopted in VidyutRakshaka (See Fig. 2). arises from the goal of having a continually running program where multiple stakeholders are contributors as well as the receivers of benefits accrued, and the need to make it cost and resourceefficient in the long run.

4.1 Sign up VidyutRakshaka consciously adopted a process of nurturing champions by reaching out to various forums, Resident welfare associations, corporate and educational institutions. Some of them were trained as stewards, those who would do consumer outreach, and sign up consumers for the program. The data input, processing, and report generation have been standardized through an android app [14].

4.2 Data Processing There were two streams of data: (1) profile and asset-related from the consumer and (2) the electricity consumption data for the same consumer from the utility. The latter ensured data accuracy and continuous availability of data removing the dependency on consumers to provide this. The two streams are cleaned up and merged for further analysis.

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Fig. 2 Stepwise implementation of VidyutRakshaka

4.3 Data Analysis The consumer data is first profiled on BHK (bedroom, hall, kitchen has used a surrogate instead of household for practical reasons.) categories: 1, 2, 3, and 4+. The consumption of the consumers is then analyzed on a per month basis (averaging over a year) and on a per capita basis (based on the occupancy details shared by the participants). The annual averages are used to avoid any biases due to variations across months. Consumption was not normalized for seasons as data did not show direct correlation between seasons and consumption for Bangalore. However, for individual participants, information is provided on their seasonal consumption variations. VidyutRakshaka then applies three unique self-iterative models to benchmark each participant as shown in Table 1 1. Neighborhood model 2. Historical consumption model 3. Optimal use model.

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Table 1 Data models for VidyutRakshaka Neighborhood model

Historical model

Optimal—use model

*BHK as classifier in all models Compares use with the average consumption in the neighborhood

Captures use trend from previous years (eliminates seasonality)

Compares split of different enduses against an “optimal use” model

Participants categorized into: • Energy saver—using less than average • Champion—using just at the average • Future champion—above average

Participants categorized into: • Consistent saver • Spender to saver • Consistent spender • Saver to saver • Random behavior

Participants were given comparison of their use and optimal use as per end uses below and recommendations against each: • Lighting • Cooling • Heating • Appliances • Entertainment • Miscellaneous

*BHK (Bedroom Hall Kitchen) is used as a proxy indicator for the house size

Neighborhood Model Among the myriad factors that influence household electricity consumption, normative social influence is found to be playing a definite role [15]. In VidutRakshaka, this aspect is built-in through the neighborhood benchmarking. Every household is benchmarked in his BHK category in his immediate neighborhood and is categorized both on monthly consumption and per capita consumption as follows: • Energy Saver—Those consuming below the neighborhood average • Champion—Those consuming at the neighborhood average • Future Champion—Those consuming above the neighborhood average Historical consumption model Historical consumption model is constructed for each consumer, unlike the neighborhood and optimal model. It captures the electricity consumption trend for the last 3 years, prior to joining the program (Table 2). Optimal use model The average ownership and usage of different types of electrical assets (classified into lighting, heating, cooling, appliances, and entertainment) is modeled for each BHK category. Each consumer is then benchmarked in its BHK category based on this optimal model. This optimal model is iterated periodically based on current data/ usage patterns.

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Table 2 Categorization based on the historical trend Category

Description

Consistent savers

Historically was showing a decreasing trend which is continuing after joining the program

Saver to spender

Historically was showing a decreasing trend but started increasing after joining the program

Spender to saver

Historically was showing increasing trend but started decreasing after joining the program

Consistent spender

Historically was showing an increasing trend which is continuing after joining the program

Inconsistent

Fluctuating or random behavior

4.4 Feedback to Consumers To optimize report generation for VidyutRakshaka participants, an MS Excel based automation has been introduced which helps in generating customized reports for each consumer by using specific criteria. A report template fed with metadata is used to generate customized reports. The final report is divided into the following sections: • • • • • •

Profile data including program ID, date of joining, contact details Benchmarking (against the three models described above) Best practices already followed by the participant Recommendations customized for each participant Goal-setting Other details including contact, resource section, disclaimers.

4.5 Report Duration The program is attempting to settle into a quarterly report cycle with dependence on the utility for the data.

4.6 Aggregate-Level Analysis While individual consumer reports are the main focus of the program, the data available at the aggregated level is emerging to be of great value and use to understand the trend at the residential sector level and to assess the impact of the nudges provided.

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4.7 Utility Engagement VidyutRakshaka is aligned with the utility’s DSM goals. While providing the muchvalued consumer outreach, and promoting DSM, VidyutRakshaka has balanced the interest of the consumer and the utility. While the consumer receives reports to save energy, the aggregated data helps utility understand the residential consumption patterns in Bangalore city. The data can inform both DSM program and energy efficiency policies. Savings have been calculated for participants who have been in the program for more than 1 year. 1553 households thus qualified for assessing the savings. In order to ensure that factors like billing errors, non-occupation, etc. do not affect the savings calculation, some data had to be removed resulting in a final set of 1255 households for this calculation as of January 2019. A summary of indicative results as on date is provided below 1. Benchmarking results: Out of the 1255 consumer data available, 444 are categorized as Energy Savers, 94 as Champions, and 707 as Future Champions (Table 3). 2. Per capita electricity consumption across BHKs is calculated by the total electricity consumption and the number of people within the household (Table 4). 3. In Fig. 3, the annual per capita consumption across different BHK categories is plotted against the number of occupants in the house. This shows the large variation in per capita electricity consumption across BHKs and based on occupancy. In the case of the 4+ BHK houses, the per capita electricity consumption without data set is as high as 2001 units per year and this is almost double India’s per capita electricity consumption value of 1149 units per year [16]. At an aggregate-level, our analysis of 1255 households we find 599 households have reduced consumption by an average of 31 units monthly, approximately, 22% of their cumulative monthly consumption. Table 3 Split of consumption trend across BHKs in numbers Consumption Trend

1 BHK

2 BHK

3 BHK

4 BHK

Energy saver

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320

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Fig. 3 Occupancy versus per capita electricity consumption

5 Conclusion Our program “VidyutRakshaka” shows an electricity savings potential of about 26% (or 19 MU per annum) if adopted at Bangalore city level by all residential consumers. The success of this program showcases the potential of such low investment behavior change programs for DSM. This is a good case study for utilities, State electricity regulatory commissions and Forum of Regulators (FoR). In addition, programs like this provide data-based evidence to address many hitherto unanswered research questions in the area of residential electricity consumption. In addition, programs like this provide data-based evidence to address many hitherto unanswered research questions in the area of residential electricity consumption. • Study of the patterns of electricity consumption based on the trends in the ownership of different appliances and equipment and their variation across household sizes. This can give inputs to the Standard and Labeling program by Bureau of Energy Efficiency, India. • Study the demographic impact on residential electricity consumption and socioeconomic inequalities in consumption within a city. This can help in the design of energy efficiency policies and programs catered to the varying needs of different communities. • For Bangalore, seasonal index calculation did not point to clear seasonal consumption changes. However, a better understanding of variations in the seasonal consumption helps in better power purchase planning opportunity for the utility based on the reliable ground-level data. • Government through various schemes has promoted various efficient appliances programs. This kind of consumer-driven behavior change programs addresses the opportunity to neutralize the rebound effect of energy-efficient appliances. To conclude, developing countries like India with diverse demographics need disaggregated field-level data to plan energy policies and programs like VidyutRakshaka fill this gap.

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Acknowledgements Authors are thankful to the Bangalore Electricity Supply Company (BESCOM) for providing data and guidance in support of VidyutRakshaka. Authors would like to acknowledge the Corporate Citizenship support and funding from SocieteGenerale Global Solution Centre, Bangalore.

References 1. Prayas homepage. http://www.prayaspune.org/peg/trends-in-india-sresidentialelectricitycons umption. Last accessed 14 Feb 2019 2. Global buildings performance network homepage. https://www.gbpn.org/newsroom/reportresidential-buildings-indiaenergyuseprojections-and-savings-potentials. Last accessed 11 Feb 2019 3. Forum of regulators. http://www.forumofregulators.gov.in/Data/study/Model%20DSM%20R egulations.pdf 4. Elisha, R.: Household energy use: applying behavioral economics to understand consumer decision-making behavior. Renew Sustain Energy Rev. 1–10 (2015) 5. Anant: Nudges in the marketplace: the response of household electricity consumption to information and monetary incentives. J. Econ. Behav. Organ. 117 (2017) 6. European environment agency (EEA) homepage. https://www.eea.europa.eu/publications/ach ieving-energy-efficiencythroughbehaviour/file. Last accessed 2019/02/11 7. Abrahamse W: The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. J. Environ. Psychol. 1–10 (2007) 8. J-PAL homepage. https://www.povertyactionlab.org/evaluation/opowerevaluatingimpact-hom eenergy-reports-energy-conservation-united-states. Last accessed 09 Feb 2019 9. Khan, I.: Electrical energy conservation through human behavior change: perspective in Bangalore. Int. J. Renew. Energy Res. 1–10 (2015) 10. CPR India news page. http://www.cprindia.org/news/6585. Last accessed 14 Feb 2019 11. EPIC. https://epic.uchicago.in/wp-content/uploads/2017/05/UCH-022117_NudgesInTheM arketplace_final.pdf 12. BESCOM DSM page. https://bescom.org/wexena-project-details. Last accessed 01 Feb 2019 13. IDC. http://www.idconline.com/technical_references/pdfs/electrical_engineering/Side_effe cts_of_energy_efficiency_measures.pdf. Last accessed 14 Feb 2019 14. Google play store. https://play.google.com/store/apps/details?id=in.exuber.vidyutrakshakau ser&hl=en. Last accessed 15 Feb 2019 15. Frederiks, E.R., Stenner, K., Hoban, E.V.: Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain Energy Rev. 41, 1385–1394 (2015) 16. Executive summary of power sector, January 2019. http://cea.nic.in/reports/monthly/executive summary/2019/exe_summary-01.pdf. Last accessed 02 Aug 2019

Study of Effects of Water Inlet Temperature and Flow Rate on the Performance of Rotating Packed Bed Saurabh and D. S. Murthy

Nomenclature k [J/kg] P [N/m2 ] Gk [m2 /s2 ] Gb [m2 /s2 ] U [m/s] x [m]

Turbulence kinetic energy Pressure Generation of Turbulence kinetic energy due to mean velocity gradients Generation of Turbulence kinetic energy due to buoyancy Stream-wise velocity Cartesian axis direction

Special characters α [m2 /s] ε [m2 /s3 ] ρ [kg/m3 ] τ [N/m2 ] μ [N.s/m2 ]

Thermal diffusivity Turbulence dissipation rate Physical density Stress tensor Molecular viscosity

Saurabh (B) · D. S. Murthy Department of Mechanical Engineering, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India e-mail: [email protected]

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Subscripts b eff i, j

Buoyancy Effective expression Component in Cartesian direction

1 Introduction The evolution of rotating packed beds as gas–liquid contacting device makes use of centrifugal acceleration field to achieve the intensification, which is far greater in magnitude (100–1000 times) than the conventional gravitational acceleration [1, 2]. This intensification facilitates for interaction between multi-phase fluids flowing in the counter-current radial direction. The use of rotating packed bed gained significant appreciation in the replacement of giant distillation towers used in chemical and processing industries, thereby attaining overall volume reduction up to 2–3 order of magnitude. Several other distinctive fields of applications include oil and refinery industries, drug and pharmaceutical industries, post-combustion carbon capture [3], preparation of battery grade lithium bicarbonate [4], etc., wherein rotating packed beds are amicably subjected to a range of processes viz. adsorption, biosorption, dehumidification, degasification, vacuum distillation, stripping, scrubbing, etc. [5–8].

2 Background The preliminary literature on rotating packed bed refers to the work presented as Higee, an acronym for high-gravity [9–12]. The journey started with basic fluid stripping apparatuses, e.g., Podbelniak’s reactor, Chamber’s centrifugal reactor, spinning disc reactor, etc., and drastically evolved with the advent of rotating packed bed which further progressed to rotating zig-zag bed, split bed packing [13–19]. Some prominent visual investigations regarding the fluid flow in rotating packed bed are reported in [20, 21]. The study of rotating packed bed performance, based on number of independent variables viz. fluid inlet conditions, the packing, and rotational parameters is presented in [2, 22]. An appraisal of prominent parameters involved in operation of rotating packed bed has been discussed in [23]. Operating pressure drop characteristics are mentioned elsewhere [24]. However, these explorations solely belong to the mass transfer domain. Furthermore, intruding techniques used in the experimental approach often deviate the flow making it impossible to capture the intricate and original fluid profiles. For this reason, computational fluid dynamics (CFD) simulation for inquisition of water

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Fig. 1 Schematic diagram of rotating packed bed

inlet temperature and flow rate effects on the pressure, velocity, and temperature distribution across the rotating packed bed have been taken up in this segment of communication.

3 Rotating Packed Bed Setup The setup for rotating packed bed consists of packing material duly secured between two perspex discs of inner and outer diameters as 60 mm, 310 mm respectively and separated 25 mm apart. Liquid inlet is provided at the eye of the rotor and provision of liquid outlet is made at casing bottom. Contrarily, air inlet is from top of the casing whereas outlet is drawn from an annular arrangement with respect to the duct for liquid inlet. Casing is an enclosing structure that secures the liquid from splashing and helps in collecting the liquid for easy drain from the bottom. A simple sketch of the same is shown in Fig. 1.

4 Geometry and Method The geometric modeling and meshing corresponding to the packing structure have been performed in the design modeler and ICME meshing modules of ANSYS Fluent code, respectively. The inner diameter 60 mm, outer diameter 310 mm and height 25 mm, packing porosity 0.95, and specific area 4000 m2 /m3 have been considered for the cylindrical packing comprising meshing of 1,000,000 hexahedral elements. The re-normalization group (RNG)-based k-ε model has been retained in solving the instantaneous Navier-Stokes equation because of its ability of incorporating the swirl effect along with the flow turbulence. The governing equations for solution variables viz. pressure, momentum, and turbulent kinetic energy are given below:

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Continuity Eq. ∂ρ + ∇(ρU ) = 0 ∂t

(1)

∂ (ρU ) + ∇(ρU × U ) = ∇ p + ∇.τ + Sm ∂t

(2)

Momentum Eq.

Equation for turbulent kinetic energy   ∂ ∂k ∂ ∂ αk μe f f + G k + G b − ρε − Ym + Sk (ρkU ) = (ρk) + ∂t ∂ xi ∂x j ∂x j

(3)

Eq. for turbulent dissipation   ∂ε ε ∂ ∂ ∂ αε μe f f + C1ε (G k + C3ε G b ) (ρεu i ) = (ρε) + ∂t ∂ xi ∂x j ∂x j k − C2ε ρ

ε2 − Rε + Sε k

(4)

The solution strategy made via pressure–velocity coupling, Green-Gauss cellbased spatial discretization, pressure scheme PRESTO, second-order upwind schemes for momentum and other variables have been adopted. The validation plot has been shown in Fig. 2, showcasing the comparison of current work with the previous literature [22, 25]. 0 rpm Sandilya et al. 0 rpm current work 950 rpm Llerena-Chavez et al. 1420 rpm Sandilya et al. 1420 rpm current work

Fig. 2 Validation plot showing variation of pressure drop versus gas flow rate

0 rpm Llerena-Chavez et al. 950 rpm Sandilya et al. 950 rpm current work 1420 rpm Llerena-Chavez et al.

600 Pressure drop (Pa)

500 400 300 200 100 0

6.5

13.1

19.5

Air flow rate (m3/h)

25.3

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Fig. 3 Pressure contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

5 Results and Discussion The effect of fluid flow rates and inlet temperatures through rotating packed bed have been analyzed in this section. The performance of rotating packed bed primarily depends on the pressure distribution and velocity profiles developed inside the packing when subjected to with or without rotation. The thermal performance however includes the study of temperature dissipation throughout the rotating packed bed. For this purpose, the results have been assembled.

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5.1 Effect of Water Inlet Temperature The water inlet temperature has been taken in the range of 313–328 K. Figure 3 shows the combined effect of water inlet temperature and packing rotation on the pressure distribution across the rotating packed bed. It is clearly shown from the contours that the pressure inside the packing increases in the radially outward direction with increase in the water inlet temperature against the overall range 18.5–2210 Pa taken for the plot. For inlet temperatures 313–318 K, the pressure first decreases from stationary condition to 600 rpm and later on increases up to packing rotation 1200 rpm. However, beyond 318 K, a regular increase in the pressure is observed up till 328 K for all the values of rotation from 0 to 1200 rpm. The range of variation in lower limits is very marginal, whereas there is significant variation in the upper limits of pressure with a range of 393.1 Pa. The lower most value for maximum pressure is recorded as 1816.1 Pa corresponding to 313 K, 600 rpm, whereas the highest value turns out to be 2209.3 Pa for 328 K, 1200 rpm under the range of investigation. The effect of water inlet temperature on the velocity plots obtained for rotating packed bed can be viewed vertically along the columns of Fig. 4. Moreover, a horizontal glance presents more interesting insight into the pattern of velocity distribution along the radial direction attributing the effects of rotation from 0–1200 rpm. Under the stationary rotor condition, i.e., N = 0 rpm, the velocity decreases in the radially outward direction for all the inlet temperatures. It holds for the reason that as the flow area increases, the velocity decreases in that direction. Contrary to this, with the onset of rotation, the pattern of velocity completely reverses following the general rule, V = ω × r . For this reason, the velocity is lower at inner radius of packing and gradually increases toward the outer radius of packing. The range of velocity from 1.0 to 18.3 m/s has been selected for comparison of all the profiles. Altogether, the velocity distribution inside the rotating packed bed seems to be independent of water inlet temperature. However, the influence of rotation bears significant discernment to the maximum attainable velocity inside the packing. The value of maximum velocity for 0, 600, 1200 rpm are 7.2, 9.23, and 18.28 m/s, respectively. Figure 5 shows the distribution of temperature across the rotating packed bed under the influence of water inlet temperature ranging from 313 to 328 K. The minimum temperature in the figure corresponds to that of air inlet at 298 K and the maximum value varies showcasing the combined effect of water inlet temperature and packing rotation as a whole. The first column corresponds to stationary rotor condition and reveals incomplete mixing of the two fluids flowing in counter-current direction inside the packing. However, the increase in water inlet temperature can easily be observed while moving down the columns. With the onset of rotation, the intermixing of air–water fluids tunes in smooth contours for packing temperature decreasing radially outwards in direction. This can be assimilated from the fact that, rotation allows the fluid streams to cover helical trajectories providing better time for intermixing and thus facilitating heat transfer, unlike the straight radial path in stationary condition.

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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(k)

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Fig. 4 Velocity contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

5.2 Effect of Water Flow Rate The effect of water flow rate on pressure and temperature distributions across the rotating packed bed has been illustrated in Figs. 6 and 7, respectively. The range of flow rate 0.5–1.5 kg/s has been considered for this purpose. The pressure drop inside the packing increases with increase in fluid flow rate for stationary rotor condition. Nevertheless, with the onset of rotation, the pressure drop first decreases up to 600 rpm and then increases to attain a maximum value of 999.15 Pa at 0.5 kg/s, 1200 rpm. The maximum pressure corresponding to water flow rate 1.0 kg/s is 945.12 Pa and that for 1.5 kg/s is 971.44 Pa, both at 1200 rpm. It is easily inferred that the use of 1.5 kg/s flow rate assisted in compliance with 1200 rpm facilitates lower pressure drop across the rotating packed bed as compared to 0.5 kg/s flow rate. The effect of liquid flow rate, however, remains marginal on the velocity profiles as compared to the effect of rotation, as already been discussed in the previous section. The temperature distribution shows abrupt mixing of the two fluids under 0 rpm

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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Fig. 5 Temperature contours (a–l) showing combined effect of water inlet temperature and RPM. For 313 K a 0 rpm, b 600 rpm, c 1200 rpm; For 318 K d 0 rpm, e 600 rpm, f 1200 rpm; For 323 K g 0 rpm, h 600 rpm, i 1200 rpm; For 328 K j 0 rpm, k 600 rpm, l 1200 rpm

condition. After the introduction of rotation, the temperature profiles start following continuously uniform pattern along the radial direction for every run of flow rates. However, the consistency of temperature dissipation is highest at 0.5 kg/s flow rate, 1200 rpm.

6 Conclusions From the above discussion, it can be summarized that the use of higher temperatures at water inlet implies higher pressure drop across the packing, although it assists in attainment of maximum value of pressure itself. The use of higher flow rate 1.5 kg/s along with 1200 rpm fairly supports for lower pressure drop. The effect of packing rotation eventually predominates rendering almost negligible effect of water inlet temperature and flow rate on the velocity profile inside the packing. A maximum range of temperature shredding has been reported for the use of higher water inlet

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 6 Pressure contours (a–i) showing combined effect of water flow rate and RPM. For 0.5 kg/s a 0 rpm, b 600 rpm, c 1200 rpm; For 1.0 kg/s d 0 rpm, e 600 rpm, f 1200 rpm; For 1.5 kg/s g 0 rpm, h 600 rpm, i 1200 rpm

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 7 Temperature contours (a–i) showing combined effect of water flow rate and RPM. For 0.5 kg/s a 0 rpm, b 600 rpm, c 1200 rpm; For 1.0 kg/s d 0 rpm, e 600 rpm, f 1200 rpm; For 1.5 kg/s g 0 rpm, h 600 rpm, i 1200 rpm

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temperature 328 K at 600 rpm. On the other hand, lower flow rate 0.5 kg/s and 1200 rpm facilitates consistency in intermixing of fluids thereby providing uniform temperature drop. These results on rotating packed bed can well be customized in the domain of heat transfer for addressing giant and voluminous size of conventional cooling towers [26].

References 1. Ramshaw, C., Mallinson, R.H.: Mass transfer process, U.S. Patent (1981) 2. Kumar, M.P., Rao, D.P.: Studies on a high-gravity gas-liquid contactor. Ind. Eng. Chem. Res. 29, 917–920 (1990). https://doi.org/10.1021/ie00101a031 3. Wang, M., Joel, A.S., Ramshaw, C., Eimer, D., Musa, N.M.: Process intensification for postcombustion CO2 capture with chemical absorption: a critical review. Appl. Energy 158, 275– 291 (2015). https://doi.org/10.1016/j.apenergy.2015.08.083 4. Liu, W., Chu, G., Li, S., Bai, S., Luo, Y., Sun, B.: Preparation of lithium carbonate by thermal decomposition in a rotating packed bed reactor. Chem Eng. J. 1–7 (2018). https://doi.org/10. 1016/j.cej.2018.09.090 5. Liu, Z., Liang, F., Liu, Y.: Artificial neural network modeling of biosorption process using agricultural wastes in a rotating packed bed. Appl. Therm. Eng. 140, 95–101 (2018). https:// doi.org/10.1016/j.applthermaleng.2018.05.029 6. Gu, Y., Zhang, X.: A proposed hyper-gravity liquid desiccant dehumidification system and experimental verification. Appl. Therm. Eng. 2019(113879), 1–9 (2019). https://doi.org/10. 1016/j.applthermaleng.2019.113871 7. Li, W., Song, B., Li, X., Liu, Y.: Modelling of vacuum distillation in a rotating packed bed by Aspen. Appl. Therm. Eng. 117, 322–329 (2017). https://doi.org/10.1016/j.applthermaleng. 2017.01.046 8. Li, W., Yan, J., Yan, Z., Song, Y., Jiao, W., Qi, G., et al.: Adsorption of phenol by activated carbon in rotating packed bed: Experiment and modeling. Appl. Therm. Eng. 142, 760–766 (2018). https://doi.org/10.1016/j.applthermaleng.2018.07.051 9. Tung, H.H., Mah, R.S.H.: Modeling liquid mass transfer in HiGee separation process. Chem. Eng. Commun. 39, 147–153 (1985). https://doi.org/10.1080/00986448508911667 10. Chen, J.: The recent developments in the HiGee technology. In: Presented at the GPE-EPIC Conference, Venice, Italy (2009) 11. Li, Y., Yuli, Y., Xuli, Z., Lili, X., Liu, X., Ji, J.: Rotating zigzag bed as trayed HIGEE and its power consumption. Asia-Pacific J Chem Eng 8, 494–506 (2013). https://doi.org/10.1002/apj. 1688 12. Zhang, D., Zhang, P., Zou, H., Chu, G., Wu, W., Zhu, Z., et al.: Application of HIGEE process intensification technology in synthesis of petroleum sulfonate surfactant. Chem. Eng. Process Process Intensif 49, 508–513 (2010). https://doi.org/10.1016/j.cep.2010.03.018 13. Podbielniak, W.J.: Centrfugal, countercurrent contact apparatus, U.S. Patent (1954) 14. Siptrott, F.M. Chamber’s Centrifugal Reactor, U. S. Patent, 1969 15. Brechtelsbaurer, C., Lewis, N., Oxley, P., Ricard, F., Ramshaw, C.: Evaluation of a spinning disc reactor for continuous processing. Org. Process Res. Dev. 5, 65–68 (2001) 16. Munjal, S., Dudukovic, M.P., Ramachandran, P.: Mass-transfer in rotating packed beds-I: development of gas-liquid and liquid-solid mass-transfer correlations. Chem. Eng. Sci. 44, 2245–56 (1989). https://doi.org/10.1016/0009-2509(89)85159-0 17. Munjal, S., Dudukovic, M.P., Ramachandran, P.: Mass-transfer in rotating packed beds-II: experimental results and comparison with theory and gravity flow. Chem. Eng. Sci. 44, 2257–68 (1989). https://doi.org/10.1016/0009-2509(89)85160-7

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18. Wang, G.Q., Xu, Z.C., Yu, Y.L., Ji, J.B.: Performance of a rotating zigzag bed—a new HIGEE 47, 2131–9 (2008). https://doi.org/10.1016/j.cep.2007.11.001 19. Chandra, A., Goswami, P.S., Rao, D.P.: Characteristics of flow in a rotating packed bed (HIGEE) with split packing. Ind. Eng. Chem. Res. 44, 4051–4060 (2005). https://doi.org/10.1021/ie0 48815u 20. Burns, J.R., Ramshaw, C.: Process intensification: visual study of liquid maldistribution in rotating packed beds. Chem. Eng. Sci. 51, 1347–1352 (1996). https://doi.org/10.1016/00092509(95)00367-3 21. Guo, K., Guo, F., Feng, Y., Chen, J., Zheng, C., Gardner, N.C.: Synchronous visual and RTD study on liquid flow in rotating packed-bed contractor. Chem. Eng. Sci. 55, 1699–1706 (2000). https://doi.org/10.1016/S0009-2509(99)00369-3 22. Sandilya, P., Rao, D.P., Sharma, A., Biswas, G.: Gas-Phase mass transfer in a centrifugal contactor. Ind. Eng. Chem. Res. 40, 384–392 (2001). https://doi.org/10.1021/ie0000818 23. Rao, D.P., Bhowal, A., Goswami, P.S.: Process intensification in rotating packed beds (HIGEE): an appraisal. Ind. Eng. Chem. Res. 43, 1150–1162 (2004). https://doi.org/10.1021/ie030630k 24. Kevyani, M., Gardner, N.C.: Operating characteristics of rotating beds 1989:48 25. Llerena-Chavez, H., Larachi, F.: Analysis of flow in rotating packed beds via CFD simulationsdry pressure drop and gas flow maldistribution. Chem. Eng. Sci. 64, 2113–2126 (2009). https:// doi.org/10.1016/j.ces.2009.01.019 26. Saurabh, Murthy, D.S.: Analysis and optimization of thermal characteristics in a rotating packed bed. Appl. Therm. Eng. 165, 114533 (2020). https://doi.org/10.1016/j.applthermaleng. 2019.114533

Integrated Thermal Analysis of an All-Electric Vehicle Vinayak Kulkarni and Shankar Krishnan

1 Introduction Electrical vehicles are becoming increasingly popular nowadays. Simulating electric vehicles and its components can greatly help to analyze the system. It is important to estimate range, energy consumption of vehicle for the given vehicle specifications and environmental conditions. Thermal issues associated with electric vehicle battery packs can significantly affect performance and life cycle of electric vehicle [1]. There are two main vehicle simulation approaches namely backward-facing approach and forward-facing approach. Vehicle simulators using a backward-facing approach answer the question “Assuming the vehicle met the required trace, how must each component perform?” No model of driver behaviour is required in such models. Instead, the force required to accelerate the vehicle through the time step is calculated directly from the required speed trace. The required force is then translated into a torque (often by assuming some efficiency) that must be provided by the component directly upstream, and the vehicle’s linear speed is likewise translated into a required rotational speed. Component by component, this calculation approach carries backward through the drivetrain, against the tractive power flow direction, until the electrical energy use that would be necessary to meet the trace is computed. Figure 1 shows an overview of the backward faced drivetrain model. Vehicle simulators that use a forward-facing approach include a driver model, which considers the required speed and the present speed to develop appropriate throttle and brake commands (often through a PI controller). The throttle command is then translated into a torque provided by the motor and an energy use rate. The torque provided by the motor is input to the transmission model, which transforms the torque according to the transmission’s efficiency and gear ratio. In turn, the computed torque is passed forward through the drivetrain, in the direction of the physical power flow in the V. Kulkarni · S. Krishnan (B) Department of Mechanical Engineering, IIT Bombay, Mumbai 400076, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_33

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Fig. 1 Overview of drivetrain model [3]

vehicle, until it results in a tractive force at the tire/road interface [2]. In this work, we have used backward-facing model for electric vehicle simulation

2 Electric Drivetrain Model 2.1 Model Assumptions • • • • •

Transmission efficiency of gears is 100%. Regeneration is 100% Battery cells are balanced Cell internal resistance do not change with SOC and temperature. Open-circuit voltage of cell do not vary with SOC and temperature.

2.2 Model Description Figure 2 shows overall model built-in MATLAB/Simulink® . Worldwideharmonized light vehicle test procedure (WLTP) driving cycle is used in the current model. WLTP is global harmonized standard for determining level of energy consumption and electric range from light-duty vehicles. Automotive experts from the European Union, Japan, and India, under guidelines of UNECE World Forum for Harmonization of Vehicle Regulations, developed the standard with a final version released in 2015 [4]. Simulation time for the driving cycle is 1477 s and it covers 14.664 km. The forces acting on a vehicle are namely inertia force due to acceleration of vehicle, aerodynamic force, gradient force due to road gradient and road friction force, which arises due to rolling friction between road and tire. Traction force is the addition of all the above forces. Finertia = m v av

(1)

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341

Fig. 2 Overall electric and thermal model of electric vehicle

Faero =

ρ Acd (vv + vw )2 2

(2)

Fgradient = m v g sin(α)

(3)

Ffriction = μm v g; v = 0 = 0; v = 0

(4)

Ftraction = Finertia + Faero + Fgradient + Ffriction

(5)

Ftraction = m v av +

ρ Acd (vv + vw )2 + m v g sin(α) + μm v g 2

(6)

where vv is vehicle velocity which is obtained from driving cycle and av is vehicle acceleration obtained by differentiating vehicle velocity. Table 1 shows the nomenclature and vehicle parameter values used in the model development. These values approximately describe first-generation Chevy Volt plug-in hybrid electric vehicle Table 1 Vehicle and environmental parameters [5]

Parameters

Units

Description

Value

mv

[kg]

Mass of vehicle

1600

μ

[–]

Rolling friction coefficient

0.01

A

[m2 ]

Frontal area

1.84

cd

[–]

Drag coefficient

0.22

P

[kg/m3 ]

Density of air

1.2

vw

m/s

Wind speed

0

g

m/s2

Standard gravity

9.81

A

[radians]

Road gradient

0

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from General Motors Corporation operating in pure electric mode, based on public information prior to vehicle release. Traction power is the multiplication of traction force and velocity. Total power is the addition of traction power and auxiliary power. Auxiliary power includes power required for complete battery management, cooling of electric motor, cooling of power electronics, cabin cooling, fans, pumps, lighting, and entertainment. We have assumed auxiliary power as constant 2 kW as a first-order worst-case approximation. Ptraction = Ftraction × velocity

(7)

Ptotal = Ptraction + Pauxillary

(8)

Subsequently, Ptotal is fed as input to the electrical motor. Hence, electric motor has to provide power equal to Ptotal /ηmotor where ηmotor is the efficiency of the electric motor. We have assumed efficiency of electric motor to be constant and in worst-case equal to 0.8 in our model. Power Ptotal /ηmotor goes as an input to power electronics. If ηpe is the efficiency of power electronics, then output power from power electronics which will be demanded power from battery will be Ptotal /(ηmotor ηpe ). We have assumed ηpe as 0.95 in our model. Table 2 shows the specifications of the battery that we have used in our simulation. We have assumed that all the cells are balanced and hence total battery power equally divided among the cells. Hence, power demanded from individual cells Pd is Ptotal /(N × ηmotor ηpe ). Figure 3 shows the battery model used in the model. In our model, we have assumed that open-circuit voltage (OCV) is constant and does not vary with time. Terminal voltage v(t) is given by: v(t) = OCV − i(t)R0

(9)

Table 2 Assumed battery parameters [5, 6] Parameter

Unit

Description

Value

Configuration

[–]

Lithium-ion battery-3 cells in parallel, 96 in series

3p96s

N

[–]

Total cells in battery pack

288

OCV

[V]

Constant open-circuit voltage

3.7

R0

[]

Internal cell resistance

0.005

C

[Ah]

Cell capacity

15

ηcoloumb

[–]

Cell columbic efficiency

0.98

SOC0

[%]

Initial state of charge

75%

B

[kwh]

Battery capacity

16

DOD

[%]

Allowable depth of discharge

50

Integrated Thermal Analysis of an All-Electric Vehicle

343

Fig. 3 Schematic of battery model employed [7]

Pd = v(t)i(t) = (OCV − i(t)R0 )i(t)

(10)

Simplifying above expression, we get quadratic expression in i(t) a solution of which can be given as  i(t) =

SOCfinal

OCV −



(OCV2 − 4Pd (t)R0

 (11)

2R0

100 × ηcoloumb = SOC0 − C × 3600

sim  time

i(t)dt

(12)

0

where simtime is simulation time in seconds. This above method to calculate SOCfinal is known as Coulomb counting. Note that battery current is 3 × i(t) in this case as three cells are connected in parallel. We can obtain range of a vehicle, battery energy efficiency and other output parameters like energy consumed in the current cycle and energy consumed per 100 km using the following Eqs. 13–16, respectively. Range(km) =

(SOCfinal − SOC0 ) × Distance covered in current cycle(km) (13) DOD sim  time

Energy consumption(kwh) =

(OCV)i(t)dt

(14)

time ∫sim v(t)i(t)dt 0 simtime ∫0 (OCV)i(t)dt

(15)

0

Battery energy efficiency = Energy consumption/100 km(kwh) =

100 × Energy consumption(kwh) Distance covered in current cycle(km) (16)

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Fig. 4 Schematic of component-level energy balance

3 Integrated Thermal Model 3.1 Model Assumptions • • • •

Lumped capacitance model is used. Electric vehicle components are not in direct contact with the environment. Only cooling requirement of the battery is considered. Liquid cooling system is utilized for cooling and coolant absorbs constant value of heat from components.

3.2 Model Descriptions Using energy balance as shown in Fig. 4, we can arrive at the equation for power loss in a component. Assuming power loss equal to heat generation inside a component, using a lumped capacitance model, the component temperature Tm (t) can be obtained by solving the differential equation. mcpm

dTm = Q rem + Q loss dt

(17)

Q rem = kA.(T∞ − Tm )

(18)

where Q rem is the heat removed by thermal management system. Q loss is heat generated inside the component due to power loss. mcpm is thermal mass of the component [3]. Table 3 provides values of thermal mass assumed for the calculations. Note that there is no ambient temperature term in the differential equation for the component as no direct contact of component and environment is allowed. Table 3 Allowed temperature range, thermal mass and heat generated and removed [3] Battery

Tmin (◦ C) [3] −5

PE

−30

Component

Motor

−30

Tmax (◦ C)[3]

mc p (J/K ) [3]

Q loss (W )

35

130,000

i2 R

85

2000

65

60,000

0

(1−ηpe ) Ptotal ηpe ηmotor

(1−ηmotor )Ptotal ηmotor

kA(W/k) 3 10 1

Integrated Thermal Analysis of an All-Electric Vehicle Table 4 Range and energy consumption per 100 km for different driving cycles

345

Driving cycle

Range (km) Energy consumption per 100 km (kwh)

FTP72

48.31

HWFET

16.88

57.81

14.11

WLTP Class2 52.03

15.67

NEDC

17.49

46.64

4 Model Validation In electric model development, we assumed vehicle parameters (Table 1) and battery parameters (Table 2) similar to series hybrid electric vehicle first-generation Chevrolet Volt. So, we can validate our results by comparing it with full electric range and energy consumption per 100 km for electric-only mode for first-generation Chevrolet Volt. Table 4 shows our results for a range of electric vehicles in electriconly mode for different driving cycles. In model specification of Chevrolet Volt, the company claims 56 km range for only electric mode [8]. As electric vehicle range varies for different driving cycles, our range estimated are approximately close enough to company’s specification. Another important parameter that can be validated from our model is energy consumption per 100 km. We can observe that values estimated by our model are approximately close to specifications [9].

5 Results and Discussion Simulations are carried out for WLTP class2 driving cycle in MATLAB/Simulink® . The results are shown in Fig. 5. Table 5 shows output parameters for WLTP class2 driving cycle. Simulation results for ambient temperature 30 °C are shown in Fig. 6. Initially, it is assumed that all the components are at ambient temperature. We can observe that all the temperature are below maximum temperature limit specified in Table 3. From Fig. 6, we can see that temperature rise is maximum for power electronics and minimum for battery. This is clear from Table 3 that thermal inertia (mc p ) for power electronics is minimum among three-component and battery has maximum value for thermal inertia. So we can conclude that power electronics need more alert controller than the other two components as its temperature can increase/decrease rapidly as compared to other two components for the thermal inertia values specified in Table 3. This analysis helps us to check whether the value taken for Q rem is sufficient to maintain temperature limits provided in Table 3. We can also find out minimum

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Fig. 5 a Variation of SOC during drive cycle and local ups is due to regenerative braking. b Battery current versus time. c Variation of total power with time and current and total power are in phase due to resistive only circuit. d WLTP class2 driving cycle

Table 5 Output parameters for WLTP class 2 driving cycle

Output parameter

Unit

Value

Battery efficiency

[–]

0.9769

Average battery heat

[kw]

0.1293

Average motor heat

[kw]

1.478

Average PE heat

[kw]

0.3889

constant value Q rem in order to keep each component in the prescribed range for the given driving cycle. This analysis also helps us to rank the components based on their cooling requirements. Table 5 shows that average motor heat loss is more than that of battery and power electronics.

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Fig. 6 a Power loss for motor, power electronics and battery. b Variation of battery temperature. c Power electronics temperature. d Variation of motor temperature in driving cycle WLTP class2

6 Conclusions This paper presents a combined electrical and thermal model of an all-electric vehicle. We started with vehicle dynamics to get traction force, traction power, total required power for electric motor, power electronics and battery. We calculated discharge current of battery by using Rint battery model. We used these outputs to get SOC, range, energy consumption of battery. In thermal model, we formulated differential equation for component temperature and formulated heat generated in every component. Finally, we calculated temperature of drivetrain components over the period in the given driving cycle.

References 1. Karimi, G., Li, X.: Thermal management of lithium-ion batteries for electric vehicles. Int. J. Energy Res. 13–24 (2013) 2. Wipke, K., Cuddy, M., Burch, S.: A user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans. Vehicul Technol. (1999) 3. Weustenfeld, T., Bauer-Kugelmann, W., Menken, J., Straser, K., Koehler, J.: Heat flow rate based thermal management for electric vehicles using a secondary loop heating and cooling system. In: Vehicle Thermal Management Systems Conference and Exhibition (VTMS) (2015) 4. Worldwide harmonized light vehicles test procedure-Wikipedia. https://en.wikipedia.org/wiki/ Worldwide_harmonized_light_vehicles_test_procedure. Last accessed 12 Mar 2019 5. Co-simulating battery and electric-vehicle load week 5-equivalent circuit cell model simulation MOOC offered by University of Colorado—Colorado Springs. https://www.coursera.org/learn/ equivalent-circuit-cell-model-simulation. Last accessed 13 Mar 2019 6. Tamaro, C.: Vehicle powertrain model to predict energy consumption for ecorouting purposes. Virginia Polytechnic Institute and State University, Master of Science Thesis (2016)

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7. How to model voltage polarization? Week1-equivalent circuit cell model simulation MOOC offered by University of Colorado—Colorado Springs. https://www.coursera.org/learn/equiva lent-circuit-cell-model-simulation. Last accessed 12 Mar 2019 8. Green car Congress. https://www.greencarcongress.com/2010/11/volt-20101124.html#more. Last accessed 13 Mar 2019 9. Electric car energy efficiency-Wikipedia. https://en.wikipedia.org/wiki/Electric_car_energy_eff iciency#cite_note-EPA2016MY-1. Last accessed 12 Mar 2019

Computation of Higher Eigenmodes Using Subspace Iteration Scheme and Its Application to Flux Mapping System of AHWR B. Anupreethi, Anurag Gupta, Umasankari Kannan, and Akhilanand Pati Tiwari

1 Introduction India being rich in thorium reserves is designing advanced heavy water reactor (AHWR) to demonstrate the usage of thorium for commercial power production. AHWR is designed as a 920 MW (thermal), 300 MW (electric) heavy water moderated and boiling light water-cooled thermal reactor [1]. AHWR employs pressure tube concept in vertical core orientation, and the heat is removed from the core by boiling light water under natural circulation. The core is neutronically very large and gives rise to high azimuthal neutronic decoupling making it susceptible to slow-induced Xenon oscillations. Hence, reactor monitoring is necessary even during normal operating conditions such as on-power refueling and reactivity device movements [2]. The flux mapping system (FMS) computes the spatial flux distribution online for every 2–5 min and gives the complete picture of the reactor core. The point measurements of neutron flux from the in-core self-powered neutron detectors (SPNDs) B. Anupreethi (B) · A. Gupta · U. Kannan · A. P. Tiwari Homi Bhabha National Institute, Mumbai, India e-mail: [email protected] A. Gupta e-mail: [email protected] U. Kannan e-mail: [email protected] A. P. Tiwari e-mail: [email protected] A. Gupta · U. Kannan Reactor Physics Design Division, Bhabha Atomic Research Centre, Mumbai, India A. P. Tiwari Knowledge Management Group, Bhabha Atomic Research Centre, Mumbai, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_34

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and computations from neutron diffusion theory are combined to provide the threedimensional neutron flux map of the reactor core [3]. This 3-D flux map can be used to derive important information such as zonal powers (to be used by reactor regulating system) and channel powers (to be used by channel monitoring system) which are vital for the reactor operation. In this paper, three methods for the FMS have been described: flux synthesis method (FSM), modified flux synthesis method (MFSM) [3] and a proposed improved MFSM (IMFSM). FSM is the most common method used for FMS. All these methods combine the in-core detector readings and pre-computed flux modes of a reference reactor configuration using least-squares principle to compute the threedimensional flux map. Conventionally, the fundamental mode is computed using power method and the subsequent higher eigenmodes using subtraction method. In this paper, the flux modes are generated using subspace iteration (SSI) technique [4]. This scheme generates a large set of dominant modes simultaneously. The proposed IMFSM uses the fundamental and few higher eigenmodes of the snapshot configuration generated at once by the SSI scheme to estimate the three-dimensional flux distribution of the reactor. For this study, few reactor operational scenarios have been considered. The accuracy of the estimation depends on the number of flux modes used, number, and location of in-core SPNDs in the reactor. AHWR consists of 513 lattice locations in a square lattice of uniform 22.5 cm pitch. The fuel assemblies occupy 452 locations, and the rest is for reactivity control devices and shut-off rods (SORs) [5], and the arrangement can be seen in Fig. 1. The in-core SPNDs are placed in 32 interstitial lattice locations known as neutron flux units (NFU) for point measurement of neutron flux. A maximum of 10 SPNDs (sensitive length 30 cm) can be used in each NFU along the axis, giving a total of 320 SPNDs [6]. In this paper, the effect of variation of number of SPNDs and the set of flux modes on the accuracy of FMS for AHWR have been studied.

2 Flux Mapping Algorithm The basic idea of the flux mapping algorithm (FMA) is that the flux shape at any time in an operating reactor can be represented by a linear combination of fundamental and dominant ‘k’ eigenmodes of the reference configuration of the reactor [3]. The k-eigenvalue equation of the reference state is written as Mφnref =

1 Fφnref , n = 1, 2, 3 . . . kn

(1)

where the operator M accounts for leakage, absorption, and group-to-group transfer and F accounts for fission production. φnref is an eigenmode vector containing flux at all meshes in the reactor, and the superscript ‘ref’ denotes the reference configuration of the reactor. Here, k1 is called the fundamental eigenvalue and k2 , k3 , . . . are called the higher eigenvalues. φ1ref corresponding to k1 is called the fundamental eigenmode

Computation of Higher Eigenmodes Using Subspace Iteration … X\Y 1

2

3

4

5

6

7

8

351

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

A B

S1

C

S2

D

S3

S4

AR1

E

S5

RR1

F

RR2

S6

G

S7

S10

S8

S9

SR1

H

S11

AR2

J

AR3

RR3

K

RR4

S12

SR2

L

S13

SR3

S14

S15

S16

M N

S17

AR4

SR4

S18

S19

S20

SR6

S25

SR7

SR5

AR5

S21

O P Q

S22

S23

S24

R

RR6

S T

S26

RR5 AR6

AR7

S27

U

SR8 S29

S30

V

S28 S31

S32

RR7

W

RR8

S33

X

AR8

S34

S35

Y

S36 S37

Z

Shim Rod S1-37 Absorber Rod RR Regulating Rod SR

Shut off Rod

AR

47500 MWd/te 37500 MWd/te 33500 MWd/te

Fig. 1 AHWR core layout with all reactivity control devices [7]

and the subsequent φ2ref , φ3ref , . . . are the higher eigenmodes of the reference state. The ‘k’ eigenmodes can be pre-computed offline by solving Eq. (1). During reactor operation, the snapshot flux  at any time can be approximated as =

Nm 

an φnref

(2)

n=1

where an is the combining coefficient and Nm denotes the number of ‘k’ eigenmodes. In order to estimate , the combining coefficients have to be calculated. To obtain them, Eq. (2) is assumed to be valid at all points inside the reactor and hence considered valid at detector locations too [2]. This condition leads to the following set of N D (number of in-core detectors) linear equations with Nm (number of eigenmodes) unknowns [3]. φd j ≡ φ(r j ) =

Nm  n=1

  an φnref r j

(3)

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  where r1 , r2 , . . . are the in-core detector locations, φ r j is the computed flux at the jth detector location, and φd j is the measured flux. The above equation can be written in the matrix form as,   [φd ] N D ×1 = φnref N D ×Nm [a] Nm ×1

(4)

As Nm is less than N D , the system becomes overdetermined and such systems can be solved by a least-squares technique,  T    ref T φn Nm ×N D [φd ] N D ×1 = φnref Nm ×N D φnref N D ×Nm [a] Nm ×1

(5)

Equation (5) is similar to system of linear equations (Ax = b) and hence can be solved for combining coefficients using linear solvers. Once the combining coefficients are determined, it can be used in Eq. (2) to compute flux at all points in the reactor. Various flux mapping schemes, as described below, are based on the exploitation of modes from different configurations (such as reference or snapshot) to determine the three-dimensional flux map. FSM is the conventional method for flux mapping. In this method, the fundamental and higher ‘k’ eigenmodes are found a priori for the reference state of the reactor and stored for the use by FMS for online computations during the reactor operation [3]. However, the snapshot configuration of the reactor during operation is different from the reference configuration. To account for this, MFSM method uses the fundamental mode of the snapshot state of the reactor along with the higher ‘k’ eigenmodes of the reference state which are calculated a priori and stored [3]. The flux in the reactor is approximated as  = a 1 φ1 +

Nm 

an φnref

(6)

n=2

With SSI technique, multiple higher eigenmodes can be simultaneously computed for any reactor configuration. Therefore, as an extension to MFSM, IMFSM has been proposed which not only uses the fundamental but also few higher harmonics of the snapshot configuration and the rest from the reference configuration. The neutron flux is approximated for this method as =

NL  n=1

a n φn +

Nm 

an φnref

(7)

n=N L +1

where N L represents the number of higher ‘k’ eigenmodes considered from the snapshot configurations. The above methods follow the same procedure as in Eqs. (3)–(5) to compute the three-dimensional flux map.

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Fig. 2 Eigenmodes of AHWR: a fundamental b first azimuthal c second azimuthal

3 Computation of Flux Modes The set of flux modes is obtained by solving 3D steady-state neutron diffusion equations using subspace iteration technique. This computation is carried out with 3D reactor core analysis code ARCH [8]. The subspace iteration technique is a generalization of power iteration method in which instead of single initial guess vector, multiple initial guess vectors are used and orthonormalized after each iteration. This results in large set of dominant modes simultaneously instead of successive evaluation of higher eigenmodes. The convergence problem associated with degenerate eigenvalues is not faced in this method. The set of flux modes used for FMS of AHWR is fundamental and higher eigenmodes. These eigenmodes (Fig. 2) are generated considering the nominal reactivity device configurations using code ARCH.

4 Simulation of in-Core SPND Readings Since AHWR is in design phase, the in-core detector measurements are not available. The procedure is to compute the fluxes from two-group neutron diffusion equation using ARCH for fine mesh structure. Since the in-core SPNDs are located in the interstitial locations, the readings are obtained by averaging the (diffusion coefficient weighted) neighboring fine mesh thermal fluxes [9]. φd =

Ns 

D dp φ dp /

p=1

φd

thermal neutron flux of dth detector

Ns  p=1

D dp

(8)

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D dp diffusion coefficient corresponding to neighboring mesh ‘p’ of dth detector Ns number of meshes around the dth detector (usually 8) φ dp thermal neutron flux corresponding to neighboring mesh ‘p’ of dth detector.

5 Performance of FMS The root-mean-square (rms) error is used as performance measure for FMS [3]. It is given by   2 N

1  φi − φ¯ i × 100 rms error(% ) = N i=1 φ¯ i

(9)

φ¯ i Reference thermal flux φi Thermal flux estimated from the various FMS methods N Total number of meshes This performance measure is used for comparison of various FMS methods and also for variation of number of in-core SPNDs and set of flux modes.

6 Results and Discussion The nominal configuration of AHWR refers to all eight regulating rods (RRs) 67% IN, all 8 absorber rods (ARs) fully IN and all 8 shim rods (SRs) fully OUT and all SORs being fully OUT. The fundamental and ‘k’ higher modes at all points in the reactor are generated for this reference configuration. Similarly, the developed FMA is applied for various reactivity device configurations, and the variation of eigenmodes and in-core detectors (320 and 168 detectors [2]) are studied. It can be inferred from Figs. 3 and 4 that with increase in number of eigenmodes added to the FMA, the RMS error decreases across all methods. MFSM and IMFSM give better results compared to FSM. Also, it is seen that the range of RMS error remains almost the same with reduced number of in-core SPNDs. It is always preferred to have minimum number of in-core SPNDs to accurately estimate the three-dimensional flux map to save the computational and economic burden. Further, investigation is in progress to conclude on number of eigenmodes and number of in-core detectors to be used in FMS by studying various operational scenarios and transient cases. Figure 5 shows the reconstructed flux at midplane of the reactor for the case of one RR taken fully OUT.

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Fig. 3 Variation of RMS error for different methods with number of ‘k’ eigenmodes considering 320 in-core SPNDs for the test case of one RR taken fully OUT. Here IMFS (1) represents the addition of 1st harmonic to FMA along with fundamental and IMFS (2) represents the addition of 1st and 2nd harmonics to FMA along with fundamental

Fig. 4 Variation of RMS error for different methods with number of ‘k’ eigenmodes considering 168 in-core SPNDs for the test case of one RR taken fully OUT. Here IMFS (1) represents the addition of 1st harmonic to FMA along with fundamental and IMFS (2) represents the addition of 1st and 2nd harmonics to FMA along with fundamental

7 Conclusion The flux mapping system based on eigenmodes can provide information on the detailed power distribution of the reactor. In this paper, subspace iteration technique is applied to generate multiple higher eigenmodes. Several higher eigenmodes can be generated simultaneously by using this technique. Using this advantage, MFSM has been further extended as IMFSM by the addition of fundamental and few higher eigenmodes of snapshot configuration to the set of reference flux modes. A comparative study on various computational schemes for the FMS has been done for AHWR. It is observed that the MFSM and IMFSM provide better results compared to FSM.

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Fig. 5 Reconstructed flux at midplane for the test case of one RR taken fully OUT

The applicability of IMFSM method to FMA has to be studied extensively for various operational configurations. A study on variation of number of in-core SPNDs and set of flux modes have also been carried out. Further tests have to be performed for various reactor configurations and transient situations to conclude on number of modes and number of in-core SPNDs to be used in AHWR.

References 1. Sinha, R.K., Kakodkar, A.: Design and development of AHWR—the Indian thorium fuelled innovative reactor. Nucl. Eng. Des. 236, 683–700 (2006) 2. Naskar, M., Verma, Y., Tiwari, A.P.: Selection of optimum set of modes for flux mapping in AHWR with flux synthesis method. In: Proceedings of National Conference on Virtual Intelligence and Instrumentation (NCVII), 6A.4, BITS Pilani, Rajasthan (2009) 3. Mishra, S., Modak, R.S., Ganesan, S.: Computational schemes for online flux mapping system in a large-sized pressurized heavy water reactor. Nucl. Sci. Eng. 170, 280–289 (2012) 4. Modak, R.S., Jain, V.K.: Subspace iteration scheme for the evaluation of lambda modes of finite-differenced multi-group neutron diffusion equations. Ann. Nucl. Energy 23(3), 229–237 (1996) 5. Thakur, A., Singh, B., Gupta, A., Duggal, V., Bhatt, K., Krishnani, P.D.: Performance of Estimation of distribution algorithm for initial core loading optimization of AHWR-LEU. Annals Nucl. Energy 96, 230–241 (2016) 6. Ananthoju, R., Tiwari, A.P., Belur, M.N.: A two-time-scale approach for discrete-time kalman filter design and application to AHWR Flux Mapping. IEEE Trans. Nucl. Sci. 63(1), 359–370 (2016) 7. Arvind, K., et al.: Safety analysis report—preliminary physics chapter. RPDD/AHWR/130/2008. Rev 1, Dated Nov 10 (2009)

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8. Gupta, A.: A 3D space-time analysis code in cartesian and hexagonal geometries. In: 19th National symposium on radiation physics (NSRP-19), Mamallapuram, TN, India (2012) 9. Ezure, H.: Estimation of most probable power distribution in BWRs by least squares method using in-core measurements. Nucl. Sci. Technol. 25(9), 731–740 (1988)

ESCO Model for Energy-Efficient Pump Installation Scheme: A Case Study Saurabh Khobaragade, Priyanka Bhosale, and Priya Jadhav

1 Introduction Several studies and projects have indicated that ~30% energy savings are costeffectively possible in energized irrigation, through the use of energy-efficient pumpsets and other improvements in the pumping system [1–4]. An analysis of the various technical approaches, challenges, and cost-benefits has been covered in the literature. In this work, we consider the implications of a third-party or ESCO (Energy Service Company), implementing an energy-efficient pumps upgrade and thereafter maintaining the systems too. This is interesting because several of the problems arise due to farmer behavior, and the project’s efficiency gains may be affected by this over time. We investigated such a project conducted in Solapur district in Maharashtra state from 2010 to 2017. The project implementation agency CRI pumps replaced 2209 existing pumps with energy-efficient pumps and were expected to maintain them for 5 years. The payments to the company were expected to be covered by the resultant energy savings. The project was carried out on five agricultural feeders, four in Mangalvedha block and one in Pandharpur block of Solapur. Our study was carried out in Mangalvedha. The following data was used for analysis: first-hand surveys of 22 beneficiaries; consumption and load data of the feeders collected at the substation; data collected from the local implementing pump repair shop; four Monitoring and Verification reports, prepared by the Monitoring and Verification Agency, immediately after installation, 1 year later, 2 years later, and the final one in February 2018; and first-hand observation of the final set of efficiency measurements while they were being conducted by the Project Implementing Agency. We investigated the following factors affecting the efficacy of the scheme: financial feasibility, energy savings, efficiency of pumpsets, effect of voltage, and farmers’ behavior. S. Khobaragade · P. Bhosale · P. Jadhav (B) Centre for Technology Alternatives for Rural Areas, IIT Bombay, Mumbai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_35

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The paper is divided into the following sections: (1) Introduction (2) Description of the scheme and the geographical area of implementation (3) Analysis of factors affecting the efficacy of the scheme (4) Discussion and conclusion.

2 Description of the Scheme The scheme was launched in 2009 [5]. 2209 old inefficient pumps were replaced with 4 and 5 star rated pumps, over a period of 2 years, on four agricultural feeders in Mangalwedha block: Bhose, Borale, Nandeshwar, Brahmapuri feeders, and one in Pandharpur block: Kharatwadi feeder. The installation, and 5 years of maintenance, was provided free to the farmers who agreed to sign up for the scheme. Maharashtra State Electricity Distribution Company Limited (MSEDCL) played the role of facilitating agency for this project. CRI pumps were the Project Implementing Agency (PIA) which carried out all the work with the help of their authorized local distributor. MITCON Consultancy and Engineering Services Ltd. is the third-party Monitoring &Verification agency appointed by Bureau of Energy Efficiency (BEE) and supervising the Ag DSM project.

2.1 Geographical Location District has dry (arid and semiarid) climate, and most of it, including Mangalvedha block, falls in the Scarcity (Rainfall) Zone. Jowar is a major crop in the district as well as the block, and sugarcane is taken depending upon water availability and dominant in northern region of Mangalwedha along the river side. The average annual rainfall in the block is about 510 mm according to 2011 figure. The average annual fluctuation in groundwater is about 3 m with pre-monsoon average level being 7.85 mbgl and post-monsoon being 4.22 mbgl [6–8]. Fig. 1 shows the location of the district, block and the relevant villages. The feeder in Pandharpur block has not been considered in this study. The irrigation sources in the area are borewells, dugwells, and river water. The scheme covered 805 monoblock pumps (dugwells and river), 640 dugwell pumps, and 764 submersible pumps.

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Fig. 1 Pilot project map—Mangalwedha [9]

3 Analysis of the Factors Affecting Efficacy of the Scheme 3.1 Energy Savings Ideally, energy meters should be used to measure the change in energy consumption after installation of new pumps. For the purposes of this scheme, meters were installed on all pumps, but farmers bypassed meters, or they stopped working. The last point of energy consumption is at the feeder level in the substation. Feeder energy consumption data is recorded in a register at half-hourly intervals at all substations in Maharashtra. We have obtained the data for Borale feeder from the substation for 2016. We have also considered the data for 2006–07 and 2008–09 from the Detailed Project Report of the scheme (DPR). 672 pumps, or 30% of the total number of pumps replaced on all five feeders, are on Borale feeder, out of a total of 682 connections on the feeder. Hence, the Borale feeder data is a good representation. The energy consumption per pump for these three years data is given in Sect. 3.2. In this section, we consider hours of pumping obtained through various methods. A survey of 22 beneficiaries, indicated an average of 1136 h of pumping annually based on memory. Four of these farmers cultivated sugarcane, two had sugarcane, and rabi jowar, and the others all cultivated. The rabbi jowar farmers had an average irrigated area of 5.4 ac with 639 h of pumping. Famers who grew sugarcane were closer to the riverbanks, with an average irrigated area of 9.8 ac, and 2010 h of pumping. This is commensurate with values obtained from other methods as shown in Table 1. Also, according to the farmers, there hasn’t been any change in their hours of pumping with the new pumps. We also tried to estimate the energy consumption based on the water requirement of crops grown in the area, depth of the water sources, and average landholdings. We assume a landholding of 6 ac (average in the region), water table of 16 m, and a pumpset efficiency of 25%, and cropping of 70% area for jowar and 30% for

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Table 1 Annual hours of pumping estimated through different means

Scenario

Annual hrs of pumping

Feeder energy (Borale feeder), substation data

1164

From DPR, substation data

1154

From survey of 22 farmers

1136

sugarcane. The cropping pattern is based on the cropping pattern acquired from the Krishi office of the block. Based on this average configuration, we estimate annual energy consumption of 2638 kWh. There is a fair bit of variation in these estimates, and ideally, energy savings should be based on meters installed on farmers’ pumps.

3.2 Financial Feasibility The proposed project budget for implementation was Rs. 442 lakhs which increased to about Rs. 839 lakh as existing accessories like piping, wiring, panels, etc. were in poor shape and needed to be replaced to maximize the performance of the star rated pumps. An additional Rs. 141 lakh per year was given by MSEDCL to the PIA for operation and maintenance from a special fund called Load Management Charges (LMC). One local pump repair shop in Mangalvedha was designated for all repairs under the maintenance contract. Local mechanics brought in all pumps under this scheme for repair to this shop. Maintenance costs based on information from this shop for pumps being brought in for repair over 2 months, were estimated at an average of Rs. 2218 per repair, and an estimated 0.275 breakdowns per pump per year. Table 2 shows the estimated energy savings based on three different years’ baseline energy consumption data for Borale feeder. The first two years of data was obtained from the Detailed Project Report for the project [9]. The third year’s data, 2016, was Table 2 Estimated energy consumption before replacement for three different years, and the calculated internal rate of return under various conditions Energy consumption without energy-efficient pumps (kWh)

No reduction in efficiency

5% reduction in efficiency/year

Annual Average Hours of pumping

IRR for 10 years project period (%)

IRR for 5 years project period (%)

IRR for 10 years project period (%)

IRR for 5 years project period (%)

2006–07

9195

48

39

38

31

1164

2008–09

7529

38

28

28

20

953

2016

5410

24

11

13

3

807

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obtained directly from the substation where energy consumption data is recorded hourly. The energy consumption without the efficient pumps upgrade for 2016 is adjusted for the fact that the energy-efficient pumps are already in operation. We’ve considered all 3 years in this analysis to show the sensitivity of the financial feasibility of the project to the initial energy consumption or pump usage. Annual energy consumption per pump Feeder energy consumption(1 − loss on feeder) = Number of pumps on feeder The per pump energy consumption was calculated assuming a total feeder loss of 15%, the overall AT&C loss in Maharashtra. Usually, rural feeders are expected to be more lossy, hence this is an optimistic IRR calculation. The savings were calculated based on energy efficiency measurements before and after pump replacement. The energy has been estimated assuming that the total energy output remains the same. The other way to estimate energy savings is to assume that the hours of pumping do not change, and the reduction in power consumption per pump is what makes a difference. But since this is a region with frequent water scarcity, we assume that farmers are limited by the amount of water and not the hours that electricity is available. Unfortunately, agricultural loads are rarely metered reliably, if at all, hence these estimates are used. The IRR calculation has been done using project periods of 5 years and 10 years. This pilot project had a five-year project period, but the pumps could have a lifetime of up to 10 years, and hence we have also done the same calculation for such a project period. We have considered the capital costs incurred by the PIA, and we have also considered the estimated maintenance cost based on the frequency of breakdowns observed, and not the amount given by MSEDCL to the PIA from the LMC fund. We have considered the cost per unit of energy saved to be Rs. 5.51, reached by considering an average cost of supply of Rs. 6 per unit, an average billing rate of Rs. 2.40 per unit, [10] and an estimated collection rate of 20%. We see that for a project period of 5 years, it is barely a good project even for the highest consumption figure which is for 2006–07. For a project period of 10 years, the higher consumption figures are remunerative but the 2016 year consumption data does not indicate a good enough payback for a private company as PIA. In the actual project monetary benefits from estimated energy savings were shared between CRI & MSEDCL on 30:70 ratios, respectively, considering a saving of Rs. 2.70 per unit. Energy savings were then estimated based on the average hours of pumping, which were taken as 1640 h, which is very high considering any of the estimates found in Sect. 3.1. In addition, the company was given a flat fee of Rs. 141 lakhs per year for maintenance. Hence the model has not been tested since the payments to the PIA are not being made on the basis of true energy savings, but projected energy savings which are unrealistic, and additional maintenance charges.

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3.3 Efficiency of Pumpsets Energy efficiency measurements were carried out on all old pumps that were replaced, and all new pumps after installation. Thereafter 10% of the pumps were selected for efficiency measurement each year. Given below is the average efficiency data for the four feeders taken just before and after replacement (Table 3). The change in efficiency for Borale feeder pumps has been used in Sect. 3.1. Bhose and Nandeshwar have similar efficiencies before and after replacement. Brahmapuri has a smaller change in efficiency. There is also the question of how efficiency changes over time, and with breakdowns and repairs. TERI examined electric pumpsets several months after rectification and found an average fall in efficiency of approximately 5% [2]. Under this scheme 30+type of pumpset models were replaced. Openwell 5HP pumpsets are widely used by the farmers in the Mangalwedha area. As it is more accurate to measure head in dug well over borewell, a single pumpset model of category (CRI CSM-3S(5)) is selected from the available data for analysis. We have looked at the efficiency decline in a set of submersible pumpsets in dugwells through the Monitoring and Verification data collected by the PIA. The efficiency measurements were conducted by taking three sets of instantaneous power consumption and flow rate readings. Head was estimated based on water depth and piping system dimensions. The actual water depth in borewell is hard to estimate accurately, hence for our analysis, we decided to only consider dugwell models. In addition, we found that there was much incongruity in the M&V data, because some pumps showed unrealistically high-efficiency numbers, and for some the total head was greater than the cut-off head of the model. Hence, we analyzed the data for just one model, the CRI CSM-3S(5), whose characteristics we could acquire. And within this model, only those pumps were considered with flow rate- head combinations which were within the vendor stated performance characteristic of the model, i.e., the output hydraulic power at a certain head was not more than what was expected. We also left out pumps with efficiency measurements conducted at lower than the operating voltage in the vendor specifications, or pumps that had been repaired. These factors are likely to affect the efficiency adversely, but we wanted to normalize these factors as much as possible to get some minimum level of efficiency reduction.

Table 3 Average efficiency and power consumption measurements collected by CRI pumps [5] Number of pumps replaced

Bhose

Borale

Brahmapuri

Nandeshwar

339

672

266

500

Old avg eff (%)

22.5

22.9

20.5

21.9

Old avg load per pump (kW)

6.39

5.83

7.29

7.16

New avg eff (%)

40.7

41.5

33.6

40.0

New avg load per pump (kW)

4.75

4.37

5.52

5.24

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Table 4 Average variation in efficiency of 13 different pumpsets Approx. age of pumpset (days)

Average efficiency—variation with time 403–410

776–808

1326–1374

No. of IPS considered

5

5

3

Avg. baseline operating eff. (%)

37.3

35

45.6

Standard deviation (baseline eff.)

6.3

10.2

8.7

Average new efficiency at M&V

34.8

29

35.6

Standard deviation (new eff.)

5.8

9.7

8.0

Avg. drop in eff. per year (%)

6.03

7.43

5.95

Table 4 has the resultant pumps’ data, in three different groupings, with the number of days after installation when the measurement was conducted was spaced by about 1, 2, and 4 years. Another factor that would affect efficiency calculations is the operating head. This will vary over time. The vendor specified operating head for the model is 20–35 m, 40 m is the cut-off head. At M&V the total heads—static+dynamic for these pumps varies between 20 and 40 m. The average annual fluctuation in groundwater is about 3 m, hence that should not affect the efficiency significantly [6–8]. Since this is such a small dataset of 13 pumps, and there is so much ambiguity in the M&V data, we cannot depend on the efficiency measurements or calculations by M&V. Sensitivity of efficiency to pipe lengths Another source of ambiguity in the efficiency arose from the pipe lengths and tapping point error in the efficiency measurements. Some of the fields, are quite long and the delivery pipe has several openings or chambers—see Fig. 2. It is not clear if the efficiency measurements have been carried out at the same outlet each time. For instance, in one configuration, with a static head of 21 m, chambers existed between a distance of 10–392 m from the well. A measured flow rate of 7 l/s, input power of 4.7 kW, resulted in calculated efficiency varying between 47 and 77%.

Fig. 2 Schematic showing outlets along the length of a pipe

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Table 5 Sample voltage measurements Voltage

No of pumps

V > 240

1

200 < V < 220

11

180 < V < 200

8

V < 150

1

Table 6 Average sanctioned HP versus connected HP for replaced pumps on the four feeders [5] Feeder

No. of pumps replaced

Sanctioned HP

Old Pump HP

New Pump HP

Bhose

339

4.4

5.1

4.6

Borale

672

4.2

4.9

4.4

Brahmapuri

266

5.9

6

5.8

Nandeshwar

500

4.7

5.9

5.2

3.4 Effect of Voltage Voltage levels were low but were very low only at the tail end of the feeder in Bhose village which had low voltage issues in general. Table 5 has sample measurements taken at 21 beneficiaries’ pumpsets. According to the M&V reports, the pumps being used before the replacement were oversized, and farmers did not allow downsizing of pumps even if the flow rate matched their old pumps. Hence the system was overloaded, resulting in lower voltages. Besides resulting in ineffective operation of the pumps, this would affect the efficiencies adversely overall. In addition to causing deterioration in the pumps. Table 6 shows that the average HP of the replaced pumps is greater than the sanctioned HP on three out of four feeders, and barely less than, on the fourth feeder.

3.5 Effect of Farmer Behavior 33 farmers and 30 mechanics were interviewed to find out about farmer behavior and causes for pumps failures. The reasons for the problems and can be categorized into three main categories. Figure 3a shows how much each category of problem accounts for the breakdown. Some of the farmer misuse of technology are as follows: Increasing the relay setting on the starter, using wire with higher current capacity for fuse, putting a mechanical obstruction so that the starter does not trip—all of these allow the motor to fail at low voltages, thus resulting in large currents through the motor. 50% of the surveyed 33 farmers, had bypassed the capacitors installed. This alone could have reduced the current significantly, possibly leading to better voltages. Some of the

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Fig. 3 Categorization of all the problems

connections were loose. The pumps were moved—for instance when there was not enough water in the well, a farmer might move the pump to the river. This new head may not correspond to the pump characteristics and so that the pump runs at a low efficiency. The technical problems mainly include the wear and tear of the equipment due to usage. The scheme did not have any provision for regular maintenance, and farmers did not do it either. The supply-side problems are low and/or unbalanced voltage, and it is possible that these could also be aggravated or caused by farmers’ usage behavior. Figure 3b shows farmers’ behavior towards maintenance of pumping system, observed through survey.

4 Discussion and Conclusion The model of this pilot project wherein the PIA installs as well as maintains the pump and gets paid on the basis of energy savings if successfully implemented, could get around all the problems that encumber Agricultural Demand Side Management initiatives. Unfortunately, in this pilot project, the payments to the PIA are not based on realistic energy savings. The two main reasons being, error in efficiency measurements (verified with data analysis), and consideration of inappropriate hours of pumping. The estimated energy savings were based on 1640 h of pumping annually, an approximate reduction of load of 1.2 kW per pump, a savings of Rs. 2.70 per unit of energy saved, divided up in a ratio of 30:70 between the PIA and MSEDCL achieving breakeven at end of 5th year, resulting in an IRR of 2%. The additional benefit to the PIA not considered here is the sale and brand recognition through the scheme. From our calculations, we get an IRR of 39–11% based on 1164–807 h of pumping, which reduces to 31–3% considering a reduction in operational efficiency of 5% annually.

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The average efficiency of EEPS was found to be 32.34% (n = 21, SD = 13.6%), at the end of 5 years, which is less than the targeted value of 34.5%. But the baseline efficiency was 22% (from M&V report, SD not available), hence energy savings are going to continue for a few more years after the project period resulting in a gain for the utility. Unfortunately, there is no reliable account of the energy savings actually achieved through this scheme. Hence this pilot project is not implemented in a way that seriously tests the ESCO model. But the analysis of this data, and field conditions, throw up several questions about a rational design for an ESCO model. For instance, seemingly, the efficiency difference between an old and a new pump, and the energy consumption or hours of pumping should be enough to calculate the energy savings due to the replacement. However, efficiency is not easy to calculate in the case of borewells, since head measurement is hard. Also, many conditions that affect the operational efficiency of the pump vary in the field, such as voltage and water table level. Drought conditions, as seen in this case, will reduce hours of pumping and hence energy savings. More useful than measuring instantaneous power consumption and flow rate measurement would be a measurement of water and energy consumption over a period of a year, of a sample set of pumps. This would give the utility as well as the ESCO the required information to make a decision that leads to optimum performance and gains for both parties. This measurement should be carried out after replacement too, to determine the energy savings that have occurred due to efficiency improvement. Many effects of farmer behavior, like oversized pumps or removal of capacitors, are felt at the low tensions network level. Hence, an ESCO model at the Distribution Transformer level may also be a good possibility. There may be more uniformity in water and energy consumption, total energy consumption is easily measured at the DT, and farmers connected to a DT can be sensitized to using the right sized pump, and to following certain practices like use of capacitors, regular maintenance, and appropriate current limiting devices to ensure any energy-efficient pumps to function at their maximum potential thus improving the probability of success of such schemes. Also, smaller interventions may provide opportunities for rural entrepreneurs.

References 1. CORE: Best practices for agricultural pumpsets and rural Demand Side Management (DSM). Core International, Inc (2005) 2. Reidhead, W.: Achieving agricultural pumpset efficiency in rural India. J. Int. Dev. 13(2), 135–151 (2001) 3. Planning commission: report of high level panel on financial position of distribution utilities. Planning Commission, Government of India (2011) 4. WB: India: Power supply to: agriculture, Report No. 22171-IN. World Bank (2001) 5. MITCON: Monitoring and verification report for 4th year. AgDSM Pilot project in Solapur Circle, BEE (2017) 6. CGWB: Report on ground water information Solapur District Maharashtra. Central Groundwater Board, Government of India (2013)

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7. Feedback ventures: Report on estimation and segregation of distribution loss in Solapur circle electricity distribution network. MERC (2010) 8. GSDA: A report on dynamic ground water resources of Maharashtra 2011–12. Groundwater surveys and development agency Pune, Government of India (2012) 9. DPR: pilot AgDSM project at Solapur Maharashtra, detailed project report. Bureau of energy efficiency (2009) 10. MERC: Order, Maharashtra electricity regulatory commission, Case No.121 of 2014

Transient Numerical Model for Natural Convection Flow in Flat Plate Solar Collector Nagesh B. Balam , Tabish Alam , and Akhilesh Gupta

Nomenclature Ar Bi Cp g Gr h H L K K n N Nu p Pr q” Q R Ra S t T

Aspect ratio (L/H) Biot number Specific heat capacity Gravitational force Grashof number Convective heat transfer coefficient Height of respective domain Length of enclosure Thermal conductivity Diffusion coefficient Time step Number of tubes Nusselt number Pressure Prandtl number Heat flux (W/m2 ) Non-dimensional Heat flux Residual Rayleigh number Transport equation source term Time Temperature

N. B. Balam (B) · T. Alam CSIR-Central Building Research Institute, Roorkee 247667, India e-mail: [email protected] A. Gupta Indian Institute of Technology Roorkee, Roorkee 247667, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_36

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u u, v U U, V x, y X, Y α β γ ε θ ν ρ τ ψ ω   ∇2 

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Velocity vector Velocity components Non-dimensional velocity Non-dimensional velocity components Coordinates Non-dimensional coordinates Thermal diffusivity Thermal expansion coefficient Angle of inclination of enclosure Numerical tolerance limit Non-dimensional temperature Kinematic viscosity Density Non-dimensional time Stream function Non-dimensional stream function Vorticity Non-dimensional vorticity General conservation variable Laplacian operator Order of discretization

Subscripts/Superscripts o amb f fp fg g p S t w

Reference condition Ambient Fluid Fluid to plate Fluid to glass Glass Plate Solar Tube Water

1 Introduction Natural convection losses in flat plate solar collectors (FPSC) are the major heat losses constituting above 70% of the overall heat losses in the solar collector. So, natural convection flow optimization offers a maximum potential to reduce the heat

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losses and improve the overall efficiency of the FPSC. Several numerical models have been developed to describe overall heat transfer process in the solar collectors. Duffie and Beckman [1] developed a one-dimensional model to evaluate the overall heat transfer coefficient and collector efficiency as a function of fluid inlet temperature to characterize the steady-state performance of FPSC. However, the requirement of steady-state conditions makes experimental comparison tests more complicated, inaccurate and expensive. The behaviour of FPSC can be more accurately characterized by its transient response to input parameters such as solar radiation intensity, weather variation, shadow factors, inlet fluid temperature which are transient in nature. Lumped-capacitance models are developed to model the transient behaviour of FPSC [2, 3]. Lumped capacitance assumes there are no significant temperature spatial gradients along the spatial dimensions of the solid body. A 1-node model was first attempted by close [4] by developing an energy balance equation at a node on the absorber surface. The absorber plate, collector fluid temperature, and cover temperatures are assumed uniform and equal making the study completely steady state at each time step. Further, Klien [5] developed a 2-node model by creating two nodes at absorber plate and cover glass. The lumped capacitance of the absorber plate and collector fluid is differentiated from the lumped capacitance of the glass covers. Thus, two energy balance equations at absorber plate and glass cover are developed. Assumptions in the study include a perfect thermal coupling between absorber plate and collector fluid giving a uniform temperature. A three-node model is developed by Morrison and Ranatunga [6] separating the thermal capacitance of the absorber plate and collector fluid resulting in a three equation model. It is assumed that the temperature gradient from the inlet to outlet of the tube is varying linearly. Later multinode models were developed that contain multiple nodes in the collector fluid regime, absorber plate and glass covers. All of these models assumed evenly distributed incident solar radiation, negligible edge effects and negligible receiver conductivity. One of the major disadvantages of these lumped-capacitance models is neglecting the distribution of temperature gradients in the annulus air gap between absorber plate and solar collector. Natural convection in the air gap is taken into account by empirical expressions only [7] without actually simulating the fluid flow behaviour. But, as pointed out previously more than 70% of the overall heat loss occurs through natural convection between absorber plate and ambient air via the glass cover. So modelling the fluid flow phenomenon inside the annular air gap is necessary to optimize the natural convection losses. The partial differential equations that arise in modelling this fluid behaviour are governed by Navier–Stokes equations in addition to energy conservation equation. These are nonlinear in nature and highly computer-intensive. So very few attempts have been carried out to rigorously model the temperature distribution in the annulus air gap. Recently with the advances of digital computation techniques, these studies have become possible. Steady-state simulations of 2D and 3D FPSC models to simulate the fluid flow behaviour are studied by fluent and other commercial softwares [8–16]. These studies have primarily focussed on various structural and operating parameter optimization to increase the FPSC efficiency.

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A transient model that characterizes the natural convection flow behaviour within the annulus gap of the FPSC is not available in the literature. Here we develop a 2D model to optimize various structural and operating parameters.

2 Governing Equations 2.1 Assumption (i) (ii) (iii) (iv) (v)

The flow and temperature changes are uniform along Z-direction limiting the flow to 2D. Since the flow is 2D, the temperature of heat transfer fluid in the pipe is constant. Radiation heat transfer is neglected and, however, could be easily be incorporated in the present model. Fluid and material properties are evaluated as a function of constant temperature throughout the transient simulation. Side and bottom heat transfer losses are neglected by considering perfectly insulated.

FPSC is divided into three zones as shown in Fig. 1. The plate domain (p) which designates the absorber plate section of the FPSC, the fluid domain (f ) which is filled

Fig. 1 Schematic diagram of FPSC with boundary conditions

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375

with air and the glass domain (g). The governing equations which define the heat transfer behaviour in each section are presented below.

2.2 Governing Equations The governing equations are non-dimensionalized using the following dimensionless quantities. y L αt ωH 2 x ; Y = ; AR = ; τ = 2 ;  = ; H H H H α gβq Smax H 4 ψ ν ∂ ∂ = ; Pr = ; Ra = ;U = + ;V = − ; α α αν K ∂Y ∂X  2  ∂ ∂ 2 (T − Tamb )   ; Ra = Gr ∗ Pr; =− ;θ =  + ∂ X2 ∂Y 2 q Smax H K q  K1 hH − → ; Q S =  S ; r K 12 = ; U = (U, V ); Nu = Bi = K q Smax K2 X=

By applying the above-defined non-dimensional quantities, the governing Eqs. 15 and its associated BC’s are defined. For the sake of clarity, the suffixes are dropped in the governing equations as shown in Eq. 1.

2.3 Plate Domain (P) ∂ 2θ p ∂θ p ∂ 2θ p ∂ 2θ ∂θ ∂ 2θ = = + ⇔ + ∂τ ∂ X2 ∂Y 2 ∂τ ∂ X2 ∂Y 2 Boundary conditions: Plate bottom ∂θ = 0, ∀(X, Y ) ∈ (0 → L − n Dt , 0) ∂Y Tube ∂θ = −Bi t (θ − θw ), ∀(X, Y ) ∈ (N Dt , 0) ∂Y Plate sides     ∂θ = 0, ∀(X, Y ) ∈ 0, 0 → H p and L , 0 → H p ∂X

(1)

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Plate top   ∂θ ∂θ = −r K f p + Q S , ∀(X, Y ) ∈ 0 → L , H p ∂Y ∂Y

2.4 Fluid Domain (F) ∇ 2 = −

 2  ∂θ ∂ − ∂θ →  + U .∇  = Pr ∇  + Ra . Pr + cos γ . − sin γ . ∂τ ∂x ∂y   ∂θ − → + U .∇ θ = ∇ 2 θ ∂τ

(2) (3) (4)

Boundary conditions: Fluid bottom   θ f = θ p , U , V , = 0∀(X, Y ) ∈ 0 → L , H p Fluid sides     ∂θ   , U , V , = 0, ∀(X, Y ) ∈ 0, H p → H f and L , H p → H f ∂X Fluid top θ f = θg , U , V , Ψ = 0 ∀(X, Y ) ∈ (0 → L , H f )

2.5 Glass Domain ∂ 2θ ∂θ ∂ 2θ = + ; 2 ∂τ ∂X ∂Y 2 Boundary conditions:   ∂θ ∂θ Glass bottom ∂Y = −r K f g ∂Y , ∀(X, Y ) ∈ 0 → L , H f     Glass sides ∂∂θX = 0, ∀(X, Y ) ∈ 0, H f → Hg and L , H f → Hg   ∂θ Glass top ∂Y = −Biamb (θ − θamb ), ∀(X, Y ) ∈ 0 → L , Hg

(5)

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3 Numerical Implementation 3.1 Algorithm Step 1: Initialize all the dependant variables θ p , θ f , θg , U , V , ,  at time step ‘n’. Step 2: Update the dependant variables to time step ‘n + 1’. Plate domain: Evaluate θ pn+1 including boundaries fluid domain: Evaluate θ n+1 , n+1 excluding boundaries f f glass domain: Evaluate θgn+1 including boundaries Step 3: Evaluate boundary conditions for θ n+1 , n+1 at time step ‘n + 1’. f f Step 4: Repeat steps 2 and 3 until the solutions are converged

3.2 Validation Two case studies are selected to validate the model developed to simulate the natural convection flow, Case1: Differentially heated square cavity described in Vahl Davis et al., [17] Case 2: Top heat loss coefficient in solar collector by Samdarshi et al., Subiantaro et al. [18, 19], Case1: The results for simulation of natural convection flow in a square domain of case described in Vahl Davis et al. are compared in Table 1. We present the results for Rayleigh numbers in the range of 103 –106 . The quantities considered here are the maximum horizontal velocity u max on the vertical midplane Table 1 Comparision of natural convection flow results in square domain with benchmark results of Davis et al. [17] Ra

Reference

103

Benchmark Present study

104

vmax

max

mid

Nu y

3.649

3.697



1.174

1.117 1.119

3.635

3.692

1.180

1.180

0.3

0.1



0.5

0.1

Benchmark

16.17

19.61



5.071

2.238 2.260

41 × 41

16.10

19.48

5.080

5.080

|%| Difference

0.4

0.6



0.1

1

Benchmark

34.73

68.59

9.612

9.111

4.509 4.645

Present study 106

41 × 41

umax

|%| Difference Present study 105

Mesh

41 × 41

34.44

66.81

9.675

9.153

|%| Difference

0.84

2.6

0.66

0.46

3.0

Benchmark

64.63

219.36

16.750

16.32

8.817

64.70

215.42

17.064

16.62

9.184

0.1

1.8

1.87

1.84

4.16

Present study |%| Difference

61 × 61

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Table 2 Top heat loss coefficient comparison (U t : W/m2 /K) with Samdarshi et al. [18] Rayleigh no.

1

100

1000

2500

Samdarshi et al.

13

6.5

5.5

5.4

Present study

11.5

6.0

5.3

5.2

|%| Difference

13

8.3

3.8

3.85

of the cavity, the maximum vertical velocity vmax on the horizontal midplane, the maximum absolute value of the stream function |ψ|max , the absolute value of the stream function at the midpoint of the cavity |ψmid |, the average Nusselt number Nu0 on the hot wall, and the maximum and minimum values Numax and Numin of the local Nusselt number on the hot wall. Case 2: Samdarshi and Mullick have proposed an empirical correlation to evaluate the top heat loss coefficient of a solar collector. We compare the evaluated heat transfer coefficient from the present model with Samdarshi et al. [18] to validate the present model (Table 2).

4 Results The transient simulation results are presented for the following non-dimensional parameters. Since the flow is symmetric along the X-axis direction, we simulate the flow using 2 pipes only, but the study can be extended to any number of pipes. For the above-defined non-dimensional parameters, the simulations were carried out using finite difference programming in MATLAB. The isotherm contours are shown in Figs. 2 and 3. Initial conditions of the transient simulation were all the temperature profiles in three domains which are equal to ambient temperature and a

Fig. 2 Isothermal contours of absorber plate, annulus gap and glass cover at time 0.5 s (top), 1.5 s (middle) and 2.5 s (bottom)

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Fig. 3 Isothermal contours of absorber plate, annulus gap and glass cover at time 3.5 s (top), and 4.5 s (bottom)

solar radiation intensity of 500 W/m2 that corresponds to QS = 0.5 is given as the initial condition. It can be observed from Figs. 2 and 3 that the copper plate temperature reaches to a maximum non-dimensional temperature of 20 with time. As expected, the temperature is maximum in the copper plate as seen in Fig. 3 at the mid-portion between both the fluid tubes. Initially, at time = 0.5 s heat flow starts raising up at 4 different locations. As the steady state is reached the central contours collapse into single strong rotating contour with bulk of the heat transfer happening at this location. By varying various non-dimensional parameters in the given model, it is possible to predict the time required to reach the steady-state limits in all the three regions. One can observe many interesting phenomena that are occurring in the annulus gap of FPSC which are not possible in either lumped-capacitance modelling or steady-state simulations.

5 Conclusion A transient 2D numerical model is developed to simulate the natural convection flow in the annulus gap of the solar collector. The model is validated against the standard results available in the literature. It has been found that the present model could simulate the steady-state response of the solar collector within 13% error limit when compared with Samdarshi et. al. [18] The error could be further reduced by including the radiation heat transfer effects also. The developed model is tested for a sample problem with parameters discussed in Table 3. With this model, the transient behaviour of the FPSC can be estimated, time taken for the flow to achieve steady state in each of the domains. The study can be further extended to double and triple glazing as well. A variety of parameters can be varied with the present study and its influence on the overall performance of the solar collector. Parameters such as the effect of tube water temperature, bond conductance, heat transfer coefficient of tube and glass, solar radiation intensity, plate, tube and glass conductivity, plate and tube wall thickness, tube diameter, no of tubes, gap between the tubes, glass thickness,

380 Table 3 Non-dimensional (ND) parameters of FPSC for simulation

N. B. Balam et al. ND parameter

Value

Collector length (L/H)

8

Annulus air gap (H f –H p )/H

1

Absorber plate width (H p /H)

0.1

Cover glass width (H g– H f )/H

0.1

ND tube diameter (DT /H)

1.2

Number of tubes (N)

2

Rayleigh number (Ra)

105

Prandtl number of fluid (Pr)

0.71

ND solar heat flux (QS )

0.5

ND HTF temperature (θ w )

0.0

ND ambient temperature (θ amb )

0.0

Biot number of glass (Biglass )

10

Biot number of tube (Bitube )

7.5

annulus air gap, overall aspect ratio of FPSC, wind heat transfer coefficient, ambient air temperature, fluid and material thermal properties. Presently, the model is limited to convection heat transfer only, but it can be easily extended to include radiation heat transfer by modifying the governing equations to include radiation heat transfer also. The water temperature is considered constant which also can be assumed varying across the cross section of the pipe. Side and bottom heat losses can also be included in the present simulation by modifying the boundary conditions.

References 1. Duffie, J.A., Beckman, W.A.: Solar engineering of thermal processes. Wiley, Hoboken, NJ (2013) 2. Smith, J.G.: Comparison of transient models for flat-plates and trough concentrators. J. Sol.Energy Eng. 108(4), 341 (1986). https://doi.org/10.1115/1.3268117 3. Tagliafico, L.A., Scarpa, F., De Rosa, M.: Dynamic thermal models and CFD analysis for flat-plate thermal solar collectors—a review. Renew. Sustain. Energy Rev. 30, 526–537 (2014). https://doi.org/10.1016/j.rser.2013.10.023 4. Close, D.: A design approach for solar processes. Sol. Energy 11(2), 112–122 (1967). https:// doi.org/10.1016/0038-092x(67)90051-5 5. Klein, S.: Calculation of flat-plate collector loss coefficients. Sol. Energy 17(1), 79–80 (1975). https://doi.org/10.1016/0038-092x(75)90020-1 6. Morrison, G., Ranatunga, D.: Transient response of thermosyphon solar collectors. Sol. Energy 24(1), 55–61 (1980). https://doi.org/10.1016/0038-092x(80)90020-1 7. Oliva, A., Costa, M., Segarra, C.: Numerical simulation of solar collectors: the effect of nonuniform and nonsteady state of the boundary conditions. Sol. Energy 47(5), 359–373 (1991). https://doi.org/10.1016/0038-092x(91)90030-z

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8. Ouzzane, M., Galanis, N.: Numerical Analysis Of Mixed Convection In Inclined Tubes With External Longitudinal Fins. Sol. Energy 71(3), 199–211 (2001). https://doi.org/10.1016/s0038092x(01)00030-5 9. Ramírez-Minguela, J., Alfaro-Ayala, J., Rangel-Hernández, V., Uribe-Ramírez, A., MendozaMiranda, J., Pérez-García, V., Belman-Flores, J.: Comparison of the thermo-hydraulic performance and the entropy generation rate for two types of low temperature solar collectors using CFD. Sol. Energy 166, 123–137 (2018). https://doi.org/10.1016/j.solener.2018.03.050 10. Allan, J., Dehouche, Z., Stankovice, S., Harries, A.: Computational fluid dynamics simulation and experimental study of key design parameters of solar thermal collectors. J. Sol. Energy Eng. 139(5), 051001 (2017). https://doi.org/10.1115/1.4037090 11. Cerón, J., Pérez-García, J., Solano, J., García, A., Herrero-Martín, R.: A coupled numerical model for tube-on-sheet flat-plate solar liquid collectors. Analysis and validation of the heat transfer mechanisms. Appl. Energy 140, 275–287 (2015). https://doi.org/10.1016/j.apenergy. 2014.11.069 12. Jiandong, Z., Hanzhong, T., Susu, C.: Numerical simulation for structural parameters of flatplate solar collector. Sol. Energy 117, 192–202 (2015). https://doi.org/10.1016/j.solener.2015. 04.027 13. Dovi´c, D., Andrassy, M.: Numerically assisted analysis of flat and corrugated plate solar collector thermal performance. Sol. Energy, 86(9), 2416–2431 (2012). https://doi.org/10.1016/ j.solener.2012.05.016 14. Allan, J., Shah, L.J., Furbo, S.: Flow distribution in a solar collector panel with horizontally inclined absorber strips. Sol. Energy 81(12), 1501–1511 (2007). https://doi.org/10.1016/j.sol ener.2007.02.001 15. Martinopoulos, G., Missirlis, D., Tsilingiridis, G., Yakinthos, K., Kyriakis, N.: CFD modeling of a polymer solar collector. Renew. Energy 35(7), 1499–1508 (2010). https://doi.org/10.1016/ j.renene.2010.01.004 16. Selmi, M., Al-Khawaja, M.J., Marafia, A.: Validation of CFD simulation for flat plate solar energy collector. Renew. Energy 33(3), 383–387 (2008). https://doi.org/10.1016/j.renene.2007. 02.003 17. De Vahl Davis, G.: Natural convection of air in a square cavity: a bench mark numerical solution. Int. J. Numer. Meth. Fluids 3(3), 249–264 (1983). https://doi.org/10.1002/fld.165003 0305 18. Samdarshi, S.K., Mullick, S.C.: Analysis of the top heat loss factor of flat plate solar collectors with single and double glazing. Int. J. Energy Res. 14(9), 975–990 (1990). https://doi.org/10. 1002/er.4440140908 19. Subiantoro, A., Ooi, K.T.: Analytical models for the computation and optimization of single and double glazing flat plate solar collectors with normal and small air gap spacing. Appl. Energy 104, 392–399 (2013). https://doi.org/10.1016/j.apenergy.2012.11.009

Rice Paddy as a Source of Sustainable Energy in India Mohnish Borker and T. V. Suchithra

1 Introduction India is a densely populated country with a population of about 1.25 billion people. Of all, a large margin of 300 million people still lives without electricity. Thus, it makes India as one of the largest un-electrified populations in the world. Only 55% of households in rural areas have electricity compared to 93% in urban areas. As seen from Fig. 1, almost 70% of India’s electricity comes from coal-based power plants, 16% from hydro power, and another 4% from nuclear. The remaining 10% comes from the renewables depending on daily conditions [1]. GDP of India is growing at a larger pace, and to sustain this, it is important that corresponding growth in demand of primary energy as well as electricity and plans to meet the demands are considered [2]. The grid-tied renewable energy capacity in India has reached 42 GW, wherein wind energy contributes to 66 and 15% from solar PV. The rest is by biomass and small hydro power plants. Biomass power has attained a total installed capacity of 4.5 GW [3]. Agriculture and other allied sectors account for 14% of India’s GDP. The economic contribution of agriculture to India’s GDP is steadily decreasing due to the broadbased economic growth of the country. Over 58% of the rural household depends on agriculture as their principle means of livelihood. India achieved a record high rice production of 104.32 million tons in 2017. Incorporating crop cultivation with sustainable energy generation can lead to a breakthrough in solving the demand crisis [4]. Bioelectricity generation without competing with crop production is advantageous to meet the demand of the rising population. This breakthrough is possible by using M. Borker (B) Padre Conceicao College of Engineering, Verna, Goa, India e-mail: [email protected] T. V. Suchithra National Institute of Technology Calicut, Calicut, Kerala, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_37

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Fig. 1 Energy scenario of India

Indian Energy Scenario 2017 Coal & Natural Gas Hydro power

Nuclear Renewables Oil

a plant microbial fuel cell. Unlike a conventional fuel cell, a microbial fuel cell exploits microorganism by using waste organic matter as a source of substrate. Plants release a considerable number of organic compounds (C6 H12 O6 ) during the process of photosynthesis. Bacteria which are electrochemically active present around the roots breakdown this organic matter, releasing electrons. Electricity is generated when an electron acceptor or an electrode is placed in the vicinity of these bacteria. The one with a higher potential is used as an anode. The plant is placed in the anode region where the anodic environment is made favorable for the plant growth. Figure 2 provides the basic design of a plant microbial fuel cell. Electricity generation takes place in two steps. (i) In the anode section, electrons are released wherein they are taken up by the anode and transferred to the cathode by an eternal circuit through a load. (ii) The electrons combine with the protons passing through the membrane forming water in the cathode region. The reactions at the anode and cathode are as follows: Anode: 2C6 H12 O6 → 2C6 H10 O6 + 4H+ + 4e− Fig. 2 Basic design of a plant microbial fuel cell

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Cathode: O2 + 4H+ + 4e− → 2H2 O Net Reaction: 2C6 H12 O6 + O2 → 2C6 H10 O6 + 2H2 O There are basically three designs of plant MFC (a) sediment-type microbial fuel cell [5], (b) plant microbial fuel cell [6], and (c) flat-plate plant microbial fuel cell [7]. In general, a plant MFC is classified in two categories, single chamber and dual chamber. The classification is based on the incorporation of a proton exchange membrane which is absent in a sediment MFC. In this study, rice paddy (Oryza sativa) is used in three different PMFC models; sediment PMFC, rooftop PMFC, and dual-chamber PMFC. The compatibility of the plant in the three models was tested, and its performance parameters were measured. Performance parameters like the open-circuit voltage (V oc ), generated voltage (V ), current density (I), and power density (P) w.r.t. the anode geometric area and the internal resistance are considered. Based on the different working conditions, the proof of claim tests was carried out. To determine the performance of the electrogenic bacteria in the PMFC system, microbial isolation was carried out by repeated striking on an agar-based petri plate media.

2 Experimental Setup The performance evaluation of rice paddy in a plant microbial fuel cell was carried out using three different PMFC models. The rice paddy was sown in the three PMFC models at the end of July 2017. The three models were placed in the natural environment of National Institute of Technology, located at Kozhikode, Kerala (11.3217° N, 75.9342° E). Kozhikode experiences hot humid summers with temperatures ranging from 34 °C maximum to 23 °C minimum. The rainfall is mostly from the southwest monsoons starting from June till September, while the northeast rains arrive in the second half of October through November.

2.1 Sediment PMFC Sediment PMFC is a single-chamber module with only anode chamber. The cell lags a proton exchange membrane and a cathode chamber, where the cathode is exposed to the atmosphere. The plants are cultivated in the anode region, and the electrodes are separated by maintaining a suitable potential difference.

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Fig. 3 Sediment PMFC with rice paddy

The sediment PMFC considered in this study is a simple plant in beaker sediment MFC. Carbon cloth (90 × 70 mm) is used as the electrode material. The anode is placed at the base of the beaker, while the cathode is placed at the top, exposed to the atmosphere. A 10 cm gap is maintained between both the electrodes by placing a thermo-foam sheet around the plants which also maintains the stability of the plant. The two electrodes are connected to an external circuit consisting of a multimeter and load by using crocodile clips. Figure 3 depicts the plant in beaker sediment PMFC model.

2.2 Rooftop PMFC An innovative technique of combining green roofs with electricity generation is a rooftop PMFC. The system incorporates large covers of land or combination of small modules for electricity generation. It is considered to be an advanced form of sediment PMFC made up of a single chamber with no proton exchange membrane and a cathode chamber. The rooftop PMFC system in this study consisted of a single module in form of a box-shape container (300 × 200 × 150 mm). The cathode and anode consisted of graphite sheets, wherein suitable slots were provided on the cathode sheet to accommodate the plants. Cocopeat as growth media is used in the anode. Using crocodile clips both the electrodes are connected to the external circuit. Figure 4 displays the rooftop PMFC system with and without the plants.

Rice Paddy as a Source of Sustainable Energy in India

a)

387

b)

Fig. 4 Rooftop PMFC a blank module b with rice paddy

2.3 Dual-Chamber PMFC A dual-chamber PMFC consists of two different anode and cathode chambers separated by a proton exchange membrane. The rate of contamination is high in singlechamber sediment PMFCs due to no proper channeling of the electrons and the protons. In the dual-chamber system, the electrons and protons are filtered due to the PEM. Thus, the output of a dual-chamber PMFC is high. In this study, two graphite sheets (150 × 150 mm) were placed in the anode and cathode chambers, where the plants are cultivated on the sheet in the anode chamber. The anode and cathode are separated using Nafion 117, a proton exchange membrane (100 × 100 mm). The electrons having affinity toward the anode will be absorbed at the anode, while the protons will pass through the PEM to the cathode chamber, consisting of still water. Fig. 5 Dual-chamber PMFC model

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The electrodes are connected to the external circuit by using gold wire. Figure 5 elaborates the dual-chamber PMFC model.

3 Results and Discussion 3.1 Electron Cycle Determination Grass species like rice paddy that can survive in waterlogged conditions are more suitable for a PMFC; the claims have been made by research scholars at Wageningen University, Netherlands [8]. Grass species have fibrous roots which enables them to evenly disperse the rhizodeposits in the lower soil. Determining the electron cycle is the primary step in selecting a plant on trial and error basis for a plant MFC. Exudates released by the plants in the rhizosphere are broken down by electrochemically active bacteria (EAB) to release electrons. Theses electrons are attracted to the anode which acts as an electron acceptor. The electrons are then directed through an external circuit consisting of a load and are released near the cathode, where it combines with the protons forming water with O2 from the atmosphere. During this process, the electrons travel from one chamber to another in form of a cycle, and during this cycle, it produces electricity. Rice paddy was tested in a plant in beaker sediment type PMFC to determine the electron cycle. Carbon cloth (90 × 70 mm) is used as an electrode material for both anode and cathode, maintaining a distance of 70 mm between the two. The plant is plotted with loam soil, wherein the anode is placed at the bottom of the beaker and the cathode is at the top of the coil cover, exposed to the atmosphere. Both the electrodes are connected using crocodile clips to the external circuit. The electron cycle is generally obtained by measuring the open-circuit voltage (V oc ) and the short-circuit current (I sc ) manually using a multimeter (DT830D Digital Multimeter). An initial incubation period of 50 days or unless the V oc reading reaches 200 mV (whichever approaches first) is strictly maintained [6]. After an initial incubation period, the systems show considerable increase in the open-circuit voltage value. According to Schamphelaire et al. the possible reasons such as life cycle dependency of the rhizodeposits release, omission of the nutrients into the lower soil, release of oxygen through the aerenchyma, scavenging the electrons already collected at the anode, and lack of an adapted anodic microbial consortium are necessary for the initial incubation period [3].

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3.2 Polarization Curves The different plant MFC models were tested to evaluate the performance parameters like current density, power density, and internal resistance. Polarization curves were obtained for different values of resistances like 50, 100, 500, 1000, 1500, and 2000 . V oc : Open-circuit voltage (V) I sc : Short circuit current (mA) V: Generated voltage (V) Rext : External resistance () I: Current density (mA/m2 GA). I =

V Aanode RExt

(3.1)

Ri: Internal Resistance (/m2 GA). Rint =

(Voc − V ) I

(3.2)

P: Power density (mW/m2 GA). P=V×I

(3.3)

6.00

1.2

5.00

1

4.00

0.8

3.00

0.6

2.00

0.4

1.00

0.2

0.00

0

200

400

600

800

0

Power density (W/m2)

Fig. 6 Polarization curve of rice paddy in dual-chamber PMFC

Current density (A/m2)

The polarization curves of all the three different models, dual-chamber, sediment, and rooftop PMFC are depicted in Figs. 6, 7, and 8, respectively. Table 1 displayed below gives the overall performance parameters like max current density, max power density, and the internal resistance of the three PMFC models.

Voltage (mV)

I (A/m2) P (W/m2)

M. Borker and T. V. Suchithra 400 350 300 250 200 150 100 50 0 600

6.00

Fig. 7 Polarization curve of rice paddy in sediment PMFC

I (A/m2)

5.00 4.00 3.00 2.00 1.00 0.00

0

200 400 Voltage (mV)

P (mW/m2)

390

I (A/m2)

Current density (mA/m2)

Fig. 8 Polarization curve of rice paddy in rooftop PMFC

2000

60 50

1500 40 30

1000

20 500 10 0

0 0

200

400

Voltage (mV)

Table 1 PMFC polarization details

Power density (mW/m2)

P (mW/m2)

600

I (mA/m2) P (mW/m2)

Model

Max current density (A/m2 )

Max power density (W/m2 )

Internal resistance ()

Sediment PMFC

3.59

0.364

1500

Rooftop PMFC 0.51

0.053

500

Dual-chamber PMFC

1.042

1500

3.74

It depicts the high-power density of a dual-chamber PMFC in comparison with the lower internal resistance of the rooftop PMFC.

3.3 Dependence on Solar Radiation Plant microbial fuel cell utilizes the organic compounds produced by the plant during photosynthesis as the organic substrate. The rate at which these organic compounds are released into the system depends on the rate of photosynthesis, which in turn depends on the incident radiation. PMFCs are indirectly dependent on the incident

Fig. 9 Voltage variation of PMFC

Open circuit voltage (mV)

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800 700 600 500 400 300 200 100 0 0

5

10

15

20

25

Time (hours) Dual Chamber PMFC Sediment MFC

Rooop PMFC

solar radiation, and to prove this claim, the open-circuit voltage (V oc ) was measured on daily basis at different intervals of time. Figure 9 depicts the voltage variation on daily basis for the three PMFC models. It is been observed that the V oc values of the three modules have seen a high increase post-noon. The value decreases by nightfall and remains low during night.

4 Conclusion Incorporating living plants in microbial fuel cell to reduce the rising demand for energy has laid a large impetus on bioenergy research. Three different PMFC models were tested for electricity generation using rice paddy (Oryza sativa). From the polarization details, the dual-chamber PMFC generated high-power density (1.042 W/m2 ). The models were tested for their performance at different intervals of time in a day, and their electron cycles were also evaluated. For large-scale application, the effect of growth enhancers was studied and found to be effective. The practical application of the PMFC depends mostly on the solar insolation and life cycle of the plant. Due to in situ power generation, the dualchamber system cannot be implemented for large-scale applications. This is mainly a concern during harvesting of the crop and incorporation of the expensive Nafion 117 a membrane. Rooftop PMFCs can be exploited with various other grass species as they have high potential for power generation.

References 1. Richard, M.: India’s Energy Crisis. MIT Technology Review (2015) 2. Mahapatra, G.: Renewable Energy in India. In: Conference Proceedings of Economics for Ecology (2016)

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3. Grover, R.B., Chandra, S.: Scenario for growth of electricity in India. J. Energ. Policy 34, 2834–2847 (2006) 4. Havliek, P, Schneider, U.A.: Global land-use implications of first-generation and secondgeneration biofuel targets. Energ. Policy 39, 5690–5702 (2011) 5. Schamphelaire, L.D., Bossche, L.V.D., Dand, H.S.: Microbial fuel cell generating electricity from Rhizodeposits of rice plants. J. Environ. Sci. Technol. 42, 3053–3058 (2008) 6. Strik, D., Hamelers, H.: Green electricity production with living plants and bacteria in a fuel cell. Int. J. Energ. Res. 32, 870–876 (2008) 7. Helder, M., Strik, D.: Year-round performance of the flat plate plant microbial fuel cell. J. Appl. Biol. Sci. 76, 55–57 (2012) 8. Helder, M., Strik, D., Hamelers, H.: Concurrent bioelectricity and biomass production in three plant microbial fuel cells. J. Biores. Technol. 101, 3541–3547 (2010)

Cost and Emission Trade-Offs in Electricity Supply for the State of Maharashtra Pankaj Kumar, Trupti Mishra, and Rangan Banerjee

1 Introduction The Indian power sector is coal dominated and responsible for the largest share of India’s energy-related emissions. In the year 2017, coal thermal power plants generated 1133 TWh of electricity which was 74% of total production [1]. In the year 2014, nearly 63% of India’s energy-related emissions were from electricity generation [2]. However, India’s per capita electricity consumption in 2015 stood at 1 MWh/capita in 2017 in comparison to the global average of 3.2 MWh/capita. With the growing population and increasing electricity consumption, India’s power sector needs to be decarbonized to mitigate climate change. In this context, India’s Paris Agreement targets aimed at 40% non-fossil share of cumulative power generation and 33–35% reduction in emission intensity by 2030 [3]. Emission reduction has also been a priority of the Indian government as reflected in recent policies [4, 5]. The average plant load factor for Indian electricity sector had fallen to 63% as of March 2015 [6]. Hence, large operational flexibility can be utilized at national and sub-national scales to reduce emissions. In this study, we analyze trade-offs between cost and emissions for the state of Maharashtra for one recent day of operation.

P. Kumar (B) Interdisciplinary Programme in Climate Studies, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India e-mail: [email protected] T. Mishra SJM School of Management, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India R. Banerjee Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_38

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2 Literature Review Many studies were conducted in past to provide insights on emission reduction costs using optimization models. Anandarajah and Gambhir [7] used TIMES integrated assessment model to explore India’s emission reduction potential with renewables using CCS and non-CCS scenarios. Gambhir et al. [8] used TIMES to explore fossil fuel impacts and benefits of mitigation in a carbon permit trading scheme. Phadke et al. [9] used a capacity expansion and production cost model (PLEXOS) to analyze the implications of India’s ambitious 175 GW renewable target for Paris Agreement. Palchak et al. [10] created a production cost model to analyze the implications of high penetration of renewables. Both Phadke et al. [9] and Palchak et al. [10] used models with detailed load curves for India along with operational constraints. All these studies conducted in the past used technology interventions to achieve decarbonization. However, there is a need for insights on emission reduction potential of existing power sector infrastructure using a low carbon operating strategy. This study attempts to address this research gap using a dispatch model in TIMES framework with unit commitment and dispatch features. We created the power sector model with hourly temporal resolution and unit-wise representation of power plants allocated to Maharashtra. Further, one recent day of operations (January 15, 2019) were analyzed for minimizing cost and emissions.

3 Methodology 3.1 Overview of Electricity Generation for Maharashtra The total electricity supply for India stood at 12,06,306 MU in 2018–19 [11]. As of March 2018, India had 620 coal thermal (197.1 GW) and 239 gas thermal (24.8 GW) power plants operational with a total power sector capacity of 344 GW. Here, the power generation capacity available for Maharashtra on January 15, 2019, included 21.67 GW of coal thermal power capacity out of 34.735 and 3.03 GW out of 4.45 GW gas thermal capacity [12]. Within this, the coal and gas power generation capacity allocated under power purchase agreements was 24.70 GW. The thermal power plant capacity allocated to Maharashtra is shown in Fig. 1 and summarized in Table 1. The average heat rate and emission factor of coal thermal units were 2468 kcal/kWh (author compiled from various tariff notifications) and 0.99 tCO2 /MWh [13]. The electricity demand for January 15, 2019, stood at 365,189 MWh [12]. The average age of coal thermal power plants available for generation on the day of analysis was 15.4 years (refer Table 2).

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Fig. 1 Maharashtra map with allocated power plant capacity Table 1 Electricity generation capacity available for January 15, 2019 Technology type

Total capacity available (GW)

Allocated capacity under PPAs (GW)

Capacity available on January 15, 2019 (excluding maintenance schedule)

Coal

34.81

21.67

34.31

Gas

3.952

3.033

1.985

Solar

0.1

0.04

0

Nuclear

0.544

1.52

0

Hydro

0.391

1.45

0

Table 2 Descriptive statistics of coal power plants available for Maharashtra (age, emission factor, cost of electricity, heat rate) (author compiled from MERIT India database [12], CEA emission factors [13] and various tariff notifications) Plant unit characteristics

Mean

Stdev

Max

Min

Capacity (MW)

413.3

190.9

800

200

Net efficiency (%)

32.3

3.1

39.9

22.2

Emission factor (tCO2 /MWh)

0.99

0.09

1.2

0.8

Cost of electricity (Rs./kWh)

3.38

1.01

5.98

1.89

Age (years)

15.4

12.14

40

2

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3.2 Reference Energy System The reference energy system for this study represents all the available technologies for electricity generation and commodity flows in the system. For this analysis, all the available coal and gas thermal power plants were modeled unit-wise. All the available power plants for Maharashtra (fully or partially allocated) were considered for electricity generation. Nuclear, solar and hydro were not scheduled for electricity generation on 15 January and hence are not included for analysis (in line with MERIT India [12]). The model resolution was set at hourly temporal scale. The average load curve shape for January was taken for Maharashtra [14] and represented with actual demand for January 15, 2019. The typical load duration curve for Maharashtra has twin peaks which arise due to commercial electricity demand in daytime and evening due to high residential electricity demand. The electricity demand was exogenously specified in line with actual demand for the day. The fixed and variable cost of power plants were taken from MERIT India website. The reference energy system for Maharashtra is modeled on The Integrated MARKAL-EFOM System (TIMES) framework. TIMES is a bottom-up, linear programming model generator with a partial equilibrium framework which minimizes the total cost of electricity production. The model used for this study uses unit commitment and dispatch features in TIMES with operational constraints. The framework is used for this study due to its technology explicit features and linear programming framework which suits well for minimizing cost and emissions. Objective Function a. Cost minimization The objective function of TIMES minimizes the total cost of electricity production in the planning horizon. Here, the cost of electricity includes fixed and variable costs. PRODCOST(t) = Min

t= p 

((FIXOM(t, k) + VAROM(t, k))

t=1

 ∗ (ACTL(t, k, s)

 (1)

s

where PRODCOST (t) = total cost of electricity production on average day in year t FIXOM (t, k) = fixed cost component of tariff for electricity production from technology k VAROM (t, k) = variable cost component of tariff for electricity production from technology k ACTL (t, k, s) = activity level of technology in time period t, technology k and timeslice s.

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b. Emission minimization The objective function here minimizes the total carbon tax from electricity sector. (PRODCOST(t)) = Min

t= p 

(CT(t, k))

(2)

t=1

where CT(t, k) = carbon tax from electricity production for technology k, in time period t. Here, unit carbon tax is imposed for electricity sector and all fixed and variable costs are ignored. Thus, this objective function minimizes total carbon emissions. Constraints The constraints are to be satisfied by the model while minimizing total system costs. The constraints in the model are as follows a. Demand satisfaction 

ACTL(t, k, s) ≥ D(t, d, s)

(3)

k

The total supply ACT (t, k) at every given timeslice should be at least equal to total electricity demand D(t, d). b. Capacity utilization ACTL(t, k, s) ≤ AF(t, k, s) ∗ CAPUNIT ∗ CAP(t, k)

(4)

The total activity ACTL(t, k, s) of a particular technology k under operation at a given timeslice level s should not exceed its availability factor AF(t, k, s). The detailed equations for ramp rates, and minimum online and offline times can be found at Loulou and Labriet [15] and Loulou [16]. The ramp rates of all power plants were assumed in line with CERC norms.

4 Results As of January 15, 2019, there were 84 coal thermal and 25 gas thermal power plants allocated to the state of Maharashtra for electricity generation. Within the given set, we consider all power plants available for electricity production (83 coal thermal and 16 gas thermal) on the given day. Out of these coal thermal power plants, 26 units had emission factors above 1 tCO2 /MWh. Figure 2 summarizes the cost of electricity, emission factor and heat rate distribution of coal thermal power plants of the reference energy system.

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Fig. 2 Cost of electricity, heat rate and emission factor distribution of thermal power plant units

4.1 Electricity Dispatch As represented in Fig. 3, coal plays a dominant part in electricity supply in both cost and emission minimization scenarios (CM and EM) for Maharashtra. In cost minimization scenario, coal thermal power plants supply 95.5% of electricity amounting to 349.06 GWh, while in emission minimization scenario coal-based electricity

Fig. 3 Electricity dispatch: a Cost minimization and b Emission minimization

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reduces to 86.9% of the total at 317.53 GWh due to higher emission factor associated with coal. The cost minimization scenario prioritizes older power plant units for electricity generation, while emission minimization scenario prioritizes power plant units with lesser emission factors.

4.2 Active Power Plants in Cost Minimization and Emission Minimization Scenarios There are 37 coal power plant units of 17.14 GW active in cost minimization scenario. All of these units have cost of electricity lower than 2.93 Rs./kWh. In contrast, there are 33 coal units of 16.49 GW and all gas units are active in emission minimization scenario. Highest emission factor of power plant units is 0.96 tCO2 /MWh in emission minimization. The distribution of active coal thermal units is summarized in Figs. 4, 5 and 6. There are 20 coal units of 11.2 GW and 6 gas units of 0.112 GW operating in both cost and emission minimization scenarios and providing a win-win strategy for emission reduction. The weighted average age of thermal power plants reduces from 15.07 years in cost minimization scenario to 10.25 years in emission minimization scenario. The youngest coal unit active in cost minimization scenario is of 6 years, while the oldest unit active in emission minimization scenario is of 12 years. It can be observed from Fig. 5 that power plant units with lower emission factors are working in emission minimizing scenario while units with lower costs are operating in cost minimizing scenario. Thus, the cost minimizing scenario results in operation of a large number of older units with higher emission factors. The older units get prioritized in cost minimization because they are more economical to operate and have recovered most of their fixed cost component. In contrast, the newer plants are cleaner and have lesser emission factors than older plants. However, they have higher cost of electricity since their fixed cost needs to be recovered.

Fig. 4 Operating units in scenarios: a Cost minimization and b Emission minimization

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Fig. 5 Cost of electricity and emission factor of units in a Cost Minimization b Emission Minimization

Fig. 6 Operating capacity in a Cost minimization and b Emission minimization

4.3 Emissions and Cost of Electricity The emissions from power plants amount to 331.14 kt for cost minimization scenario. The cost of electricity in this scenario is 2.40 Rs/kWh. The emissions in emission minimization scenario reduce by 9.2% to 300.67 kt, while the cost of electricity increases by 30.8% to 3.14 Rs/kWh. This emission reduction is attributed to higher operations of cleaner gas and coal units. The cost of electricity is higher in emission minimization scenario due to higher cost of gas thermal electricity and higher cost of electricity from newer coal thermal units. The total cost of power generation per day increases from 876.4 million INR in cost minimization scenario to 1145.41 million INR in emission minimization scenario. The cost of carbon abatement in emission minimization scenario amounts to 8827 Rs/ton. This cost of carbon abatement is comparatively higher than carbon prices currently in developed countries [17].

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5 Conclusion We created a power sector model with unit commitment and dispatch features consisting of all available power plant units for Maharashtra. This model was used to analyze cost and emission minimizing generation for one recent day with existing power plant performance characteristics. In our results, we found that the emissions from thermal power can be reduced by 9.2% in emission minimization as compared to cost minimization scenario. The cost minimization scenario has the cost of electricity at 2.40 Rs/kWh and emissions at 331.14 kt. In contrast, the cost of electricity in emission minimization scenario increases by 30.8% to 3.14 Rs./kWh while emissions decrease to 300.67 kt. The cost minimization scenario has all plants active below cost of electricity of 2.93 Rs/kWh, while emission minimization scenario has all plants below 0.96 tCO2 /MWh. There are 20 coal thermal units of 11.2 GW and 6 gas units of 0.112 GW operating in both scenarios providing a strategy to reduce emissions with lower costs. The cost of carbon abatement for Maharashtra is 8827 Rs/ton which is much higher than the cost of carbon abatement in most of the developed countries. The insights of this study can be helpful in mitigating emissions from the power sector irrespective of additional investments in retrofits or new capacity. There is a trade-off in cost and emission minimizing generation strategies in terms of cost and emissions. This trade-off in cost and emission at national and subnational scales can be useful in prioritizing power plants for electricity dispatch. The additional cost of mitigation here can be financed by Green Climate Fund. Acknowledgements The authors would like to thank the Department of Science and Technology for supporting fellowships of students at the Interdisciplinary Programme in Climate Studies. We would also like to thank the Industrial Research and Consultancy Centre (IRCC) at Indian Institute of Technology Bombay for their funding support.

References 1. International Energy Agency. https://www.iea.org/countries/India. Last accessed 2019/3/10 2. Ananthakumar, M., Rachel, R., Lakshmi, A., Malik, Y.: Energy Emissions. Version 2.0 dated 2017/9/28 from GHG platform India: GHG platform India-2005–2013 National Estimates2017. Available at http://ghgplatform-india.org/data-and-emissions/energy.html. Last accessed 2019/3/10 3. UNFCCC Homepage. https://www4.unfccc.int. Last accessed 2019/3/10 4. CERC: CERC RE Tariff Order 2017–18. New Delhi (2017) 5. CEA: National Electricity Plan. Central Electricity Authority, New Delhi (2018) 6. CEA: Executive Summary Power Sector March-15. Central Electricity Authority, New Delhi (2016) 7. Anandarajah, G., Gambhir, A.: India’s CO2 emission pathways to 2050: what role can renewables play? Appl. Energ. 131, 79–86 (2014) 8. Gambhir, A., Napp, T., Emmott, C., Anandarajah, G.: India’s CO2 emissions pathways to 2050: energy system, economic and fossil fuel impacts with and without carbon permit trading. Energy 77, 791–801 (2014)

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9. Phadke, A., Abhyankar, N., Deshmukh, R.: Techno-economic assessment of integrating 175 GW of renewable energy into the Indian Grid by 2022. Ernest Orlando Lawrence Berkeley National Laboratory (2016) 10. Palchak, D., Cochran, J., Deshmukh, R., Ehlen, A., Soonee, S., McBennett, B., Milligan, M., Chernyakhovskiy, I., Narasimhan, S., Joshi, M., Sreedharan, P.: Greening the grid: pathways to integrate 175 GW of renewable energy into India’s electric grid, vol. 1. National Study (2019) 11. Ministry of Power.: Power Sector at a Glance ALL INDIA. https://powermin.nic.in/en/content/ power-sector-glance-all-india. Last accessed 2019/3/14 12. MERIT-Merit Order Dispatch of Electricity for Rejuvenation of Income and Transparency. http://meritindia.in/. Last accessed 2019/3/10 13. CEA: Central Electricity Authority: CO2 baseline database. New Delhi (2016) 14. POSOCO: Electricity Demand Pattern Analysis, vol. 1. Power System Operation Corporation Ltd (2016) 15. Loulou, R., Labriet, M.: ETSAP-TIAM: the TIMES integrated assessment model part I: model structure. Comput. Manag. Sci. 5(1–2), 7–40 (2007) 16. Loulou, R.: ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation. Comput. Manag. Sci. 5(1–2), 41–66 (2007) 17. Ernst & Young LLP: Discussion Paper on Carbon Tax Structure for India. Shakti Sustainable Energy Foundation (2015)

Technological Interventions in Sun Drying of Grapes in Tropical Climate for Enhanced and Hygienic Drying Mallikarjun Pujari , P. G. Tewari , M. B. Gorawar , Ajitkumar P. Madival , Rakesh Tapaskar , V. G. Balikai , and P. P. Revankar

1 Introduction Agriculture supports about 58% of rural India for livelihood and contributes 4th largest export commodity equivalent to 10% monetary share [1]. The food safety and security ensures prevention of hygiene loss through industry-scale preservation. Of all preservation methods, drying has better features to reduce post-harvest loss with sun drying as widely adopted practice [2]. The solar and hot air drying of agrifood products has extensively reported literature [3] on grape-drying kinetics in solar dryer using pre-treatment [4–8]. Indian grape-grown area includes subtropical, hot tropical and mild tropical agro-climatic zones. The hot tropical agro-climatic zone has 70% of grape cultivation covering parts of the states of Maharashtra, Andhra Pradesh and Karnataka in region between 15 and 20° N latitudes. In India, grape is mainly dried in shed with 5–10 racks, each 50–100 m long, 2.5 m high and 1.5 m width [9] in N–S orientation.

2 Literature Review The literary reviews have extensively reported on OSD and SDA grape drying by pre-treatment with K2 CO3 solution and dipping oil for improved quality of grape. Pangavhane et al. reported that fresh hand-harvested Thompson seedless grape has an average sugar level of 23 on Brix scale (1 Bx is equivalent to 1 g of sucrose per 100 g of solution). The sorted grapes need water wash to remove dust and dirt due to cultivation and reaping for subsequent drying using air at 60 °C with M. Pujari · P. G. Tewari · M. B. Gorawar · A. P. Madival · R. Tapaskar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, K.L.E. Technological University, Hubballi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_39

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0.5 m/s flow rate [2]. Rathnayake et al. developed a compact drying cabinet for 50–100 kg range of produce, housed in 1.25 × 0.92 m space with trays of 0.73 × 0.6 × 0.025 m size adjusted at 5 cm inter-tray space. The device dried pepper at 57 °C in 23 h time with even airflow to limit temperature variation within 3 °C [3]. Fadhel et al. investigated sultana grape drying in natural convection dryer, tunnel greenhouse and open sun. The grapes soaked in 1% NaOH solution were heated to 90 °C for a 2–3 s soak time, twice or thrice before cleaning with distilled water [4]. Doymaz studied black grape pre-treatment in solutions of ethyl oleate plus potassium carbonate, potassium carbonate plus olive oil, ethyl oleate–potassium hydroxide and ethyl oleate–sodium carbonate. It exhibited multi-level drying time with ethyl oleate–potassium carbonate pretreated sample showing lowest dry time of 25 h. The lower specific energy for moisture removal in agricultural produce makes it fit for long-term storage [5]. Akoy et al. designed 16.8 m2 natural convection solar dryer for 100 kg of sliced mango. The 20 h drying attained 10% moisture from initial 81.4% on wet basis with emphasis on sustainability [6]. Vania et al. studied twolevel factorial designs for pretreated sample of Rubi grape in K2 CO3 solution at 50 °C [7]. Forson et al. developed mixed-mode natural convection solar crop dryer (MNCSCD) with air heater, drying chamber and chimney for 160 kg of cassava and other crops. The drying time was 30–36 h for solar irradiance of 400 W/m2 , 25 °C, and 77.8% RH [8]. Ehiem et al. developed 258.64 kg/batch industry-scale fruit/vegetable dryer and tested tomato sample to be 84% efficient. The airflow of 18.3 m/s, 18.8 m/s and 19.5 m/s was selected, respectively, for small-size, medium-size and large-size tomato. The larger-size tomato needed larger heat and mass transfer-driven moisture removal possible at high velocity [9]. Stephen and Emmanuel reported on proper selection of power source, material and alternate design strategies for faster drying. This mode had a provision to regulate drying with controlled heat input and airflow. The large-scale implementation of solar energy drastically reduces carbon footprint of excessive fossil fuel usage [10]. Babagana et al. developed a forced/natural convection solar dryer for vegetables and food with black corrugated aluminum plate. The collector (0.72 × 0.6 × 0.25 m) supplied heated air to gmelina wood drying cabinet (1.2 × 0.6 × 1.8 m). The experiments indicated 45% collector efficiency with sensible heat storage of 48.9 W/m2 K sufficient for 6 h drying during night [11]. Bhuiyan et al. utilized effect of both heat and mass diffusions in combined solar-mechanical dryer. The diffusion coefficient (De ) gave maximum activation energy in kcal/g-mole for potato as 7.656 and 8.252, respectively, for mechanical and solar conditions. The drying is a combined process of heat and mass transfer, and hence, dryer design should account both these effects [12]. Askari et al. reported on Rabat–Casablanca raisin quality on basis of Enterobacteriaceae stain samples as indicators of microbial flora with standard counts of plate, coliform, fecal coliform, yeast and mold averaged as 2.8 × 107 , 3 × 103 , 2.3 × 103 , 3 × 103 and 4.6 × 104 CFU/g, respectively [13]. Basumatary et al. developed low-cost wood flank dryer with cubical and triangular prism geometry and drying chamber with non-corrosive GI sheet. The 77 °C maximum was attained in drying chamber at noon with 64–66 °C as day-average. The local materials for solar dryer established sustainability into construction and

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operation of solar dryers [14]. Adiletta et al. studied white and red grape variety for abrasive pre-treatment in sheet-coated shaker using 50 °C air current at 2.3 m/s. The drying kinetics of treated samples was better as pre-treatment aids to dislodge bounded moisture [15]. Ubale et al. have reported on forced convection that utilizes both mass and heat transfers in moisture removal, but needs additional power to drive heated air. The crop energy requirements justify improvement in drying rate on account of forced convection driven by a SPV system [16]. Singh et al. experimented on Thompson seedless grape at 60 °C with a 0.82 m/s flow in laboratory-scale hot air dryer. Pre-treatment with 25 g of potassium carbonate and 15 mL ethyl oleate per liter of distilled water gave 3 min dip time. The dry time for 40 °C dipping solution was 19 h, against 28 and 25 h, respectively, for solutions at 20 and 30° [17]. Abay et al. adopted indirect passive conventional solar dryer for a batch of 10 kg tomato. The collector used corrugated absorber to create turbulence in airflow, and good insulation reduced heat losses. The absorber temperature was 77 °C at 12:00 am with 1021 W/m2 solar radiation. The payback and benefit-to-cost ratio (BCR) were 8 months and BCR of 11.8 [18]. Ubale et al. have reported on dryer parameters like air temperature, relative humidity, drying chamber humidity, air velocity and mass of grapes to present that indicated average evacuated tube efficiency of 24.5% against insulated dryer with 37.1% efficiency [19]. Sekyere et al. investigated hybrid dryer suitable for uniform drying coupled with an accelerated drying rate for the product. The mixed-mode natural convection solar crop dryer with backup heater dried pineapple in four different modes to get desired moisture between 106 and 184% (d.b) [20].

3 Drying Methods and Materials in Experimental Investigations The details of materials and methodology in Open sun drying (OSD) and Shed drying (SD) for grape samples namely untreated (UT) and chemically treated (CT) is presented in this section. The study investigated four options of grape drying to figure out best option to yield quality raisins. The OSD used clean polythene sheet spread on unshaded open ground with adequate sunlight to dry the grape sample laid on it as bunches. The shed accommodated about 5 tons of fresh grapes layered with adequate spacing in between over 11 tiers that extended to a width of 2 m and running over a length of 60 m. The shed preferably constructed on open land ensured no obstruction to airflow owing to properly selected spacing and configuration that were important for better air circulation through grape-loaded racks. The top region of the shed, built in steel sheets, formed the plane cover that facilitated protection from excessive sunbeam and occasional rains. Figure 1 shows details of both OSD and SD for large-scale drying of grapes with wire mesh netted rack having 2–3 cm mesh spacing for loading pretreated grapes.

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Fig. 1 Details of shed drying and open sun drying along with grape samples used in study

The shed had plastic or metallic roof with side overhang on top racks to protect grapes from rain and excessive sunlight. The rack spacing in shed dryer ensured adequate direct solar radiation during entire day, except at noon when sun was at zenith. The side curtains were occasionally used to prevent contamination by dust/rain.

3.1 Materials Used for Pre-treatment of Grapes The Thompson seedless grapes grown in a local farm were used to test on mass basis. The pre-treatment with 25 g of potassium carbonate and 15 ml of ethyl oleate (dipping oil) provided attractive golden brown color and accelerated drying. The cold-dipping pre-treatment was preferred to hot-dipping process [9, 10].

3.2 Measuring Equipment and Methodology The experiments conducted as per specified standards used equipment in Table 1 for sunshine data from 8:00 am to 6:00 pm. The macro-climatic factors are air temperature, wind speed, solar radiation and rainfall-affected moisture removal rate. The drying rate was co-related with air temperature and relative humidity as they accelerated grape drying. The adequate sunlight and temperature influenced dried grape quality. Table 1 Specifications of measuring equipment used for the study Name of equipment

Specification (range, least count, model and make)

Pyranometer

0–1999 W/m2 , ±10 W/m2 or ±5%, Megger Irradiance Meter, PVM210

Temperature indicator (°C)

0–250, 2.2 above 0, Samson Automation, Bangalore

Humidity sensor

0–100, 0.1, EQTRH304W, EOUINOX

Anemometer

0.4–45, ± (2% + 0.1 m/s), MEXTECH AM-4208

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The solar insolation, wind speed, air temperature and relative humidity were recorded using pyranometer, anemometer, probe thermometer and humidity sensor, respectively, at regular time ranges between 30 s and 10 min.

4 Results and Discussion The experimental observations are made to grape drying by two alternate methodologies to evolve better grape drying practices for enhanced revenue in raisin production.

4.1 Variation of Macro-climatic and Micro-climatic Grape Drying Parameters The grapefruit temperature in solar drying was directly proportional to intensity of solar insolation and duration of exposure. The site solar radiation intensity exhibits diurnal and seasonal variations; hence, dryer design should be based on data of climatic factors at the test site under investigation. The macro-climatic site parameters (maximum, minimum and average values) of wind speed, solar insolation and air temperature as indicated, respectively, in Figs. 2, 3 and 4 reveal their variable nature during the test duration. The wind speed averaged between 0.7 and 0.8 m/s influenced drying characteristics by assisting movement of humid air from berry surface. The observations pointed in Fig. 5 revealed shed temperature to be lower by 3–4 °C compared to ambient air temperature on account of shading effect produced inside the shed. The reduction in shed temperature prevented grape berry overheating and thereby its deterioration in quality. The micro-climatic parameter of relative humidity as indicated in Figs. 6 and 7, respectively, showed similar variations as recorded on a typical day and averaged values. The maximum berry temperature was 60 °C for grape drying when air temperature was 45 °C that Fig. 2 Variation of wind speed

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Fig. 3 Variation of solar insolation

Fig. 4 Variation of air temperature

Fig. 5 Variation of shed and air temperature

revealed lower wind speed due to which air circulation led to rapid heat loss from berry to air. The drying indicated in Fig. 8 is summarized in the form of drying curve generalized by M(t) = Mo e−kt

(1)

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Fig. 6 Variation of relative humidity (day 1)

Fig. 7 Variation of relative humidity

Fig. 8 Drying curves

where ‘k’ represents drying constant as shown in Table 2. The coefficient of determination (R2 ) for given experimental data is close to unity, and hence, time dependence of drying curve for four modes resembles reported trend of agricultural products. The comparison of drying constants in OSD and SD reveals that SD has ‘drying constant-k’ to be lower by 49.35% (untreated sample—UT) and 9.86% (chemically treated sample—CT).

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Table 2 Drying equation for grape in different modes S. No.

Drying mode

Drying equations

Constant M o

k, day−1

R2

1

OSD-UT

M(t) = 1378.7 Exp (−0.233 t)

1378.7

0.233

0.9862

2

OSD-CT

M(t) = 1339.2 Exp (−0.152 t)

1339.2

0.152

0.9685

3

SD-CT

M(t) = 1338.2 Exp (−0.137 t)

1338.2

0.137

0.9653

4

SD-UT

M(t) = 1312.2 Exp (−0.118 t)

1312.2

0.118

0.9532

The data also signifies role of chemical treatment to have a positive influence in case of shed drying with improved quality of raisins. The drying rate in OSD was higher than SD owing to the presence of greater beam radiation; however, it is associated with dust and other contamination.

4.2 Mechanism of Grape Drying and Grape Quality This section deals with experimental observations on preparing raisins from harvested raw grapes through four different approaches identified as OSD-UT, OSD-CT, SDUT and SD-CT. The details of weight reduction in the grapes observed for the four methods were compared to arrive at a suitable methodology for harvested grapes. Figures 8 and 9 represent drying in four different samples investigated in terms of weight reduction of sample and percentage moisture removal rate. The drying rate in OSD-CT was fastest with moisture removal rate also higher, particularly during the early phase of drying. The moisture removal decreased during last two days, and grapes dried to a final weight of 243 g within 60 h (spread over about six days). The direct radiation on berry in OSD mode leads to improper coloration and poor taste of grapes. The chemically treated grape was better compared to OSD-UT grape samples in terms of attributes in color and flavor. The moisture removal rate for different modes of drying revealed that moisture removal rate was faster in OSD-CT as compared to OSD-UT samples. Among all investigated four processes, OSD-UT Fig. 9 Moisture removal rate curve

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fared too poor in raisin quality, while SD-CT mode fared to produce best raisin quality. The OSD was faster in SD mode with CT exhibited accelerated drying on account of ‘cuticle layer opening’ or ‘blooming’ phenomenon in the grape berry. The OSDUT clocked 95 h compared to 60 h for OSD-CT, while SD-CT took 100 h to attain final dry weight of 0.251 kg that was 16.66% lower than 120 h observed in case of SD-UT mode. The external structure of dried products also referred to as morphological features plays a vital role in the final product value. The study recorded the drying cycle exhibited in the four modes through visual inspection of the samples at regular intervals during the drying cycle. The dynamic changes occur on account of physicochemical processes that occur on account of heat and convective flowdominated drying process. The summary of changes noted during study has been recorded as Table 3 to get comparative merits and demerits of the alternate drying strategies. Table 3 Morphological changes in the harvested grapes Test Day

1

2

3

4

Grape Drying Method OSD-UT

OSD-CT

SD-UT

SD-CT

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4.3 Raisin Sensory Evaluation on Color, Texture, Weight and Taste The qualitative sensory evaluation on four different dried raisins through experiments indicated that field dried grape and ‘market available fine-quality raisin’ were comparable in terms of color, texture, taste and grape weight per unit of initial grape berry. The qualitative study was performed through the defined 0–5 judgment scale: with poor quality indicated by digit zero and superior quality in coherence assigned as a five-point score. The sensory evaluation was conducted through trained observers who evaluated independent dried raisin samples with respect to grape quality parameters at defined intervals of the drying cycle. Figures 10, 11, 12 and 13 indicate 5

Fig. 10 Variation of color of grapes

OSD-UT

OSD-CT

SD-UT

SD-CT

Color scale

4

3

2

1

0

3

5

7

9

11

Days

Fig. 11 Variation of texture of grapes

5

Standard Raisin Texture Scale

OSD-UT

OSD-CT

SD-UT

SD-CT

4

3

2

1

0

3

5

7

Days

9

11

Technological Interventions in Sun Drying of Grapes … Fig. 12 Variation of grape weight reduction

OSD-UT

5

413 OSD-CT

SD-CT

SD- UT

Weight scale

4 3 2 1 0

1

3

5

7

9

11

Days

Fig. 13 Variation of grape taste

OSD-UT

5

OSD-CT

SD-UT

SD-CT

Taste standard

4 3 2 1 0

3

5

7

9

11

Days

sensory responses collected for raisins produced by four different drying methods with respect to raisin attributes of color, texture, weight and taste. The sensory evaluation for grape color performed on a 0–5 scale assigned 5-point value to best-quality attractive golden brown color, and 0-point value reflected poorquality black-colored raisins. Figure 10 indicates that OSD-UT gave poor-quality black color raisin and SD-CT had good-quality attractive golden-colored raisin. The second sensory evaluation parameter investigated was texture, defined by roundness and surface wrinkles resembling standard raisins. Figure 11 depicts the periodic variation of grape texture on 0–5-point scale with 0 score indicative for distorted raisin, while 5 on the scale stands for better-quality raisin texture. The third sensory parameter for evaluation was ‘dry weight’—an indicator of proper drying revealing either over-drying or under-drying of raisin. As revealed in Fig. 12, CT yielded better quality for both OSD and SD. The sensory evaluation based on taste as indicated in Fig. 13 identified SD-CT to be best among all the four methods investigated.

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5 Conclusions • Experimental location had adequate solar insolation, wind speed and ambient temperature making it suitable for Thompson seedless grape drying. The overall observations of macro-climatic and micro-climatic conditions indicated that location was economically viable to produce marketable quality of raisin. • OSD-CT provided faster drying rate, but some important physical qualities of dried product were not favorable on account of poor color, texture, taste and improper shrinkage owing to excessive exposure to beam solar radiation, dust and insects. • OSD-CT drying mode produced better-quality raisins with 58% faster drying than OSD-UT. Similarly, SD-CT was 40% slower than OSD-CT as potassium carbonate provided attractive golden brown color to raisins while dipping oil accelerated drying rate on account of grape cuticle blooming. The overall effect leads to better quality of dried product through adoption of shed drying over the OSD method. • The OSD-UT sample required a 95 h drying period to yield 0.234 kg of dry raisin, as against OSD-CT grape sample that took 60 h drying time. The simultaneously performed tests took 100 h and 120 h, respectively, for SD-CT and SD-UT samples to yield 0.251 and 0.310 kg of dry raisin. The raisin quality was better for shade drying on account of transparent plastic covering isolation that prevented dust contamination. • The SD-CT samples with attractive golden coloration strongly suggested its implementation for large-scale grape drying by an additional drying technique augmentation through use of solar air heater to improve drying rate.

References 1. http://www.ibef.org/industry/agriculture-india.aspx. Last accessed on 2019/08/20 2. Pangavhane, D.R., Sawhney, R.L., Sarsavadia, P.N.: Effect of various dipping pretreatment on drying kinetics of Thompson seedless grapes. J. Food Eng. 39(1), 211–216 (1999) 3. Rathnayake, R.M.S.P., Ariyartane, A.R., Prematilake, S.P.: Design and fabrication of engineering model of a crop dryer. Trop. Agric. Res. 18(1) (2008) 4. Fadhel, A., Kooli, S., Farhat, A., Bellghith, A.: Study of the solar drying of grapes by three different processes. Desalination 185(1), 535–541 (2005) 5. Doymaz, I.: Drying kinetics of black grapes treated with different solutions. J. Food Eng. 76(1), 212–217 (2006) 6. Akoy, E.-A.O.M., Ismail, M.A., Ahmed, E.-F.A., Luecke, W.: Design and construction of a solar dryer for mango slices. In: The Annual Conference on Tropical and Subtropical Agricultural and Natural Resource Management (TROPENTAG), 11–13 October 2006. University of Bonn, Institute of Crop Science and Resource Conservation (2006) 7. Telis, V.R.N., Lourençon, V.A., Gabas, L., Telis-Romero, J.: Drying rates of Rubi grapes submitted to chemical pretreatments for raisin production. Pesq. Agropuc. Bras. 41, 503–509 (2006). Brasilia

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8. Forson, F.K., Nazha, M.A.A., Akuffo, F.O., Rajakaruna, H.: Design of mixed-mode natural convection solar crop dryers: application of principles and rules of thumb. Renew. Energ. 32(1), 2306–2319 (2007) 9. Ehiem, J.C., Irtwange, S.V., Obetta, S.E.: Design and development of an industrial fruit and vegetable dryer. Res. J. Appl. Sci. Eng. Technol. 1(2), 44–53 (2009) 10. Stephen, A.K., Emmanuel, S.: Improvement on the design of a cabinet grain dryer. Am. J. Eng. Appl. Sci. 2(1), 217–228 (2009) 11. Babagana, G., Silas, K., Mustafa, B.: Design and construction of forced/natural convection solar vegetable dryer with heat storage. ARPN J. Eng. Appl. Sci. 7(1), 1213–1217 (2012) 12. Bhuiyan, M.H.R., Alam, M.M., Islam, M.N.: The construction and testing of a combined solar and mechanical cabinet dryer. J. Environ. Sci. Nat. Res. 4(2), 35–40 (2011) 13. Askari, G.A., Kahouadji, A., Khedid, K., Charof, R., Mennane, Z.: Physicochemical and microbiological study of raisin, local and imported (Morocco). Middle East J. Sci. Res. 11(1), 1–6 (2012) 14. Basumatary, B., Roy, M., Basumatary, D., Narzary, S., Deuri, U., Nayak, P.K., Kumar, N.: Design, construction and calibration of low cost solar cabinet dryer. Int. J. Environ. Eng. Manag. 4, 351–358 (2013) 15. Adiletta, G., Senadeera, W., Di Matteo, M., Paola, R.: Drying kinetics of two grape varieties of Italy. In: Proceedings of the 6th Nordic Drying Conference (2013) 16. Ubale, A., Pangavhane, D.R., Warke, A.: Performance improvisation of conventional grape drying method by introducing forced air exhaust. Am. Int. J. Res. Sci. Technol. Eng. Math. 1–5 (2014) 17. Singh, S.P., Jairaj, K.S., Srikanth, K.: Influence of variation in temperature of dipping solution on drying time and color parameters of Thompson seedless grapes. Int. J. Agric. Food Sci. 4(2), 36–42 (2015) 18. Tesfamariam, D.A., Bayray, M., Tesfay, M., Hagos, F.Y.: Modeling and experiment of solar crop dryer for rural application. J. Chem. Pharm. Sci. 109–118 (2015) 19. Ubale, A.B., Pangavhane, D., Auti, A., Warke, : Experimental and theoretical study of Thompson seedless grapes drying using solar evacuated tube collector with force convection method. Int. J. Eng. 28(12), 1796–1801 (2015) 20. Sekyere, C.K.K., Forson, F.K., Adam, F.W.: Experimental investigation of the drying characteristics of a mixed mode natural convection solar crop dryer with back up heater. Renew. Energ. 92(1), 532–542 (2016)

Effect of Temperature on the Hydrodynamics of Steam Reactor in a Chemical Looping Reforming System Agnideep Baidya, Saptashwa Biswas, Avinash Singh, Debodipta Moitra, Pooja Chaubdar, and Atal Bihari Harichandan

1 Introduction Immense increase in challenges for developing new technologies which would produce clean and an alternating form of energy due to exhausting petroleum resources and unwarranted utilization of fossil fuels that results in global warming and air pollution has been a crucial concern for researchers [1, 2]. However, the environmental concerns due to greenhouse gases can be very well addressed by considering hydrogen as fuel which would produce only water as a result of combustion with no other detrimental effect to the environment. But, every ton of hydrogen generation from natural gas, oil, coal and electrolysis process can lead to production of 9–10 tons of CO2 [3, 4]. However, chemical looping reforming (CLR) is a process that produces hydrogen in an efficient way with 100% CO2 capture being extensively analyzed [5–7]. A CLR system constitutes (i) Air Reactor (AR), (ii) Fuel Reactor (FR) and (iii) Steam Reactor (SR). First the oxidation of metal oxide takes place in the AR, and then, it passes to the FR where metal oxide reduction takes place with water vapor and CO2 is being produced as by-products. At the outlet, 100% CO2 is apprehended with condensation of water vapor. The reduced metal oxide then reacts with the oxygen from water vapor in the steam reactor and produces metal oxide along with hydrogen as products. Figure 1 depicts the working principle of CLR system. A fluidized bed reactor (FBR) is analyzed to simulate steam reactor considering highly complex fluid dynamics and reaction kinetics among solidus and gaseous particles which takes place inside the steam reactor. The real-time flow physics explicated with conservation of mass, momentum, energy and species transport A. Baidya · S. Biswas · A. Singh · D. Moitra · P. Chaubdar (B) · A. B. Harichandan KIIT Deemed to be University, Bhubaneswar, Odisha 751024, India e-mail: [email protected] A. B. Harichandan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_40

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Fig. 1 Chemical looping reforming system

is discussed explicitly using commercial CFD Software [3, 8–15]. However, the temporal features of bubble generation, up-surging, expanding and bursting in the steam reactor have not been reported by many researchers and can be broadly attempted with due consideration of various operating constraints. Present study reports the successive progress of bubbles in the SR of a chemical looping reforming system that is important specifically at start of the process and subsequently for further time-period before accomplishing quasi-steady state along with fuel conversion rate for varied fuel reactor temperatures in a CLR process. Two oxygen carriers (iron oxide and manganese oxide) with water vapor as fuel are used for the current study of CLR process in a stream reactor. The bubble hydrodynamics and the effect of temperature on the conversion of fuel into hydrogen are investigated. Following are the chemical reactions in the CLR process in which iron oxide and manganese oxide are considered to be an oxygen carrier independently: (i)

low temperature (700–1100 K) exothermic reaction in AR 4Fe3 O4 + O2 → 6Fe2 O3 8 2 Mn3 O4 + O2 → 4Mn2 O3 3 3

(ii) high temperature (800–1600 K) endothermic reaction in FR 4Fe2 O3 + CH4 → 8FeO + CO2 + 2H2 O

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4Mn2 O3 + CH4 → 8MnO + CO2 + 2H2 O (iii) low temperature (700–1200 K) exothermic reaction in SR 3FeO + H2 O(g) → Fe3 O4 + H2 3MnO + H2 O(g) → Mn3 O4 + H2 .

2 Numerical Considerations The unsteady multi-phase flow physics has been addressed by using Ansys FLUENT with phase coupled (PC-SIMPLE) finite volume method and second-order upwind scheme to solve the coupled equations. To solve the convective part of the equations, second-order QUICK scheme has been considered. Figure 2 shows the 2-D computational domain generated for the steam reactor having reactor height as 1 m and width as 0.25 m. Boundary conditions and numerical parameters for unsteady numerical simulations are shown in Tables 1 and 2, respectively. In order to study bubble hydrodynamics in circulating fluidized bed reactor precisely, authors have kept the grid size 10 times more than oxygen carrier particles’ size [4] and did not carry out any grid independence test explicitly. The steam reactor is parted into two regions: (i) static bed region, which is up to 0.4 m in the reactor and (ii) free bed Fig. 2 Computational domain for steam reactor

420 Table 1 Boundary conditions

Table 2 Numerical Parameters

A. Baidya et al. Computational face

Boundary condition

Reactor inlet

Velocity inlet

Reactor outlet

Pressure outlet

Reactor surface

Wall (no-slip)

Numerical parameters

Details

Convergence criteria

10−6

Time step

10−4 s

Number of rectangular cells

2500

region which is rest part of the steam reactor, i.e., 0.6 m. Initially, the static bed region is patched with metal oxide granules with volume fraction of 0.48 having no trace of metal oxide particles in free bed height. The heat transfer coefficient among the gaseous and solidus phase was described by Gunn [16]. For solid particles, collision coefficient was taken as a constant value of 0.88. The solid particles in the static bed height regime are provided with certain fluidization velocity for better mixing of gaseous and solidus phase. The model parameters are alike to that considered by Deng et al. [8] and are used for the base case in the current investigation.

3 Results and Discussions The hydrodynamics of chemical reaction in the SR has been modeled using a multiphase CFD model according to kinetic theory of granular flow. The kinetic reaction that occurs between fuel (steam) and oxygen carrier (iron oxide or manganese oxide) has been customized by properly incorporating a user-defined function (UDF) in Ansys FLUENT. The shape of the granules is considered spherical with smooth, regular, inelastic and mono-dispersed spheres. Temperature range for the reaction, which is happening between gaseous and solidus phase, was assumed to be 873– 1273 K. 100% w.t. of the water vapor (fuel) is supplied to the SR with constant inlet velocity from the distributor plate placed at the bottom of the reactor. The oxygen carrier was stored in static bed region of the reactor with some initial fluidization velocity. When the fuel supply is initiated from the bottom of the reactor, transfer of momentum occurs to the oxygen carrier, and at reaction temperature, nucleation reaction or activation sites start in the reactor [17]. Hydrogen (H2 ) is produced as the product of reaction occurring between oxygen present in the steam and reduced oxygen carrier. The irregular formation of activation sites, due to reactor temperature variation, strengthens the study of transient bubble dynamics in the reactor. Figure 3 depicts results of numerical study performed with similar parameters that was considered by Deng et al. [8] with alike geometry and numerical domain

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Fig. 3 Mole fraction in gaseous phase along centerline of the steam reactor (x = 0 cm) with iron oxide as oxygen carrier

by using H2 as fuel and CaSO4 as oxygen carrier. It displays the variance of gaseous phase in mole fraction at height of y = 30 cm, along the reactor center line, from the inlet in static bed height (Fig. 3a) and at exit of the reactor (Fig. 3b). The discrepancy between current findings and the results reported by Deng et al. [8] are expected to be because of second-order discretization scheme used in present case in contrast to first-order scheme used by Deng et al. [8] and the pressure outlet boundary condition at reactor outlet in present case against the outflow condition used by Deng et al. [8]. An abrupt drop in mole fraction of reactant from unity has been noticed, and it oscillates about 0.68 initially for time up to 1.0 s. But, the characteristic is noticed to be reversed for gaseous product mole fraction that increases from 0 to oscillate about 0.32. A quasi-steady state has been attained after 1.0 s, and mole fraction of H2 and H2 O is obtained to be 0.7 and 0.32, respectively. After 3.8 s, the quasi-steady state is attained at the outlet with mole fraction of H2 and H2 O being 0.65 and 0.35, respectively. The good concurrence between the results obtained from current study with Deng et al. [8] necessitates further simulations by assuming steam and iron oxide or manganese oxide as fuel and oxygen carrier, respectively, for analyzing transient phenomena and the aspect of different operating temperature in a chemical looping reforming technology.

3.1 Transient and Quasi-Steady Bubble Dynamics Figure 4 depicts the volume fraction contour for solidus phase inside the steam reactor over time period from 0 to 2 s. The shrinking core model has been used to estimate chemical kinetics for this reactor [18]. Steam is supplied to distributor plate with uniform velocity. Iron oxide granules were initially patched with fluidization

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Fig. 4 Unsteady phenomena of bubble development in steam reactor with iron oxide as oxygen carrier

velocity which is less than that of steam velocity in static bed region. Contours in Fig. 4 show generation, upsurge, expand and burst phenomena of bubbles. Reaction starts from static bed region where small bubbles generate near distributor plate in the bed region for initial 5 s. Then, these smaller bubbles rise up creating larger bubbles. These larger bubbles create low pressure zone in wake region and thus followed by newly generated smaller bubbles. Smaller bubbles get accelerated toward the large bubbles due to pressure difference and coalesce with large bubbles, which results in the acceleration of large bubble also. Consequently, size of the bubble enlarges in the limited flow passage with slug formation in steam reactor that forms two vertically off-set columns. The solid granules are continuously forced in upward direction by surging slug, but due to gravitational effect and difference in densities of gas–solid particles, solid particles descend on the centerline and on the wall between time period of 0.5–1.5 s. This phenomenon is also observed and addressed by Clift and Grace [19] experimentally. Figure 5 depicts the physical process of solid volume fraction profile from t = 1.1 to 10 s. This provides a fair explanation of transient and quasi-steady bubble dynamics in the SR. It displays that rates reaction are different for different regimes inside the reactor which is mainly because the fluidization velocity of solid granules. One more reason for different reaction rate is that there is variation in gas velocity in slug and the bubble. The overall inter-mixing of gaseous and solidus phase is accomplished with constant delivery of the fed-stream through distributor. The transient phenomena of reaction are examined in the time between 0 and 1.5 s, and the beyond 1.5 s reaction advances close to the quasi-steady state. Consequently, the center annulus region

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Fig. 5 Solid volume fraction contours of transient and quasi-steady process with iron oxide as oxygen carrier

starts diminishing, and overall inter-mixing between gaseous and solidus granules achieves quasi-steady state.

3.2 Effect of Temperature on the Fuel Conversion Rate Contour lines for the solid volume fraction inside the SR are depicted by Fig. 6 for the temperature range 873–1273 K with oxygen carrier for MnO (Fig. 6a) and FeO (Fig. 6b) at the quasi-steady state up to time period of 40 s. The sizes 0.4, 0.6 and 0.25 m are kept constant for dense bed height, free bed height and width of steam reactor, respectively, for the present case. The temperature values for the current simulations are 873, 973, 1073, 1173 and 1273 K. Contours show that the conversion rate is high at higher temperatures which is because of the reason that mole fraction of steam decreases at reactor outlet in proportion to temperature increase in the reactor. The reaction temperature plays a key role for the successful completion of the any fluidized bed reactors as described by Hossain [18]. Induction period inside steam reactor decreases as the reactor temperature increases. As the temperature increases in the reactor, it results in the decrement in the induction time which ultimately causes more activation sites to form in the same duration. Because of this, it is concluded that increase in operating temperature will result in increased conversion rate. It is also noticed that fuel conversion rate for CLR process with manganese oxide as oxygen carrier is higher than the process with iron oxide as oxygen carrier.

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Fig. 6 Temperature effect on transient and quasi-steady process in CLR system with a manganese oxide b iron oxide as oxygen carrier

4 Conclusion An Eulerian multi-phase model is implemented to interpret the continuum principle of dual fluid model for gaseous and solidus phase model. The bubble hydrodynamics in terms of developing of bubbles, rising, growing and bursting in the steam reactor has been studied for transient and quasi-steady behavior. The conversion rate and mole fraction of steam have also been considered for various oxygen carrier particles with varying range of operating temperatures. In the current study, it is remarkable that transient bubble dynamics lasts for 0–2 s. Beyond this time period, bubble columns diminish because of differential density between gaseous and solidus phase. The conversion rate in the SR is noticed to increase with the increase in temperature that causes proper inter-mixing of gaseous and solidus phases with the temperature variation between 873 and 1273 K. The fuel conversion rate for CLR process with manganese oxide as oxygen carrier was found to be higher than the process with iron oxide as oxygen carrier.

References 1. Heidary, H., Abbassi, A., Kermani, M.J.: Enhanced heat transfer with corrugated flow channel in anode side of direct methanol fuel cells. Energ. Convers. Manag. 75, 748–760 (2013) 2. Thanaa, F., Eskander, M.N., El-Hagry, M.T.: Energy flow and management of a hybrid wind/PV/fuel cell generation system. Energ. Convers. Manag. 47, 164–180 (2006) 3. Momirlan, M., Veziroglu, T.: Recent directions in world hydrogen production. Renew. Sustain. Rev. 3, 219–231 (1999) 4. Gelderbloom, S.J., Gidaspow, D., Lyczkowski, R.W.: CFD simulations of bubbling/collapsing fluidized beds for three geldart groups. AlChE J. 49, 844–858 (2003)

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5. Richter, H.J., Knoche, K.F.: Reversibility of combustion processes. ACS Symp. Ser. 23, 571– 585 (1983) 6. Harichandan, A.B., Shamim, T.: CFD analysis of bubble hydrodynamics in a fuel reactor for a hydrogen-fuled chemical looping combustion system. Energ. Conserv. Manag. 86, 1010–1022 (2014) 7. Rydén, M., Lyngfelt, A., Mattisson, T.: Production of H2 and synthesis gas by chemical-looping reforming. In: The Eight International Conference on Greenhouse Gas Control Technologies, Trondheim, Norway (2006) 8. Deng, Z., Xiao, R., Jin, B., Song, Q.: Numerical simulation of chemical looping combustion process with CaSO4 oxygen carrier. Int. J. Greenhouse Gas Contr. 3, 368–375 (2009) 9. Khan, M.N., Shamim, T.: Investigation of hydrogen in a three reactor chemical looping reforming process. Appl. Energ. (2015) 10. Fan, L.S., Zeng, L., Wang, W., Luo, S.: Chemical looping processes for CO2 capture and carbonaceous fuel conversion—prospect and opportunity. Energ. Environ. Sci. 5, 7254–7280 (2012) 11. Tang, M., Xu, L., Fan, M.: Progress in oxygen carrier development of methane-based chemicallooping reforming: a review. Appl. Energ. 151, 143–56 (2015) 12. Protasova, L., Snijkers, F.: Recent developments in oxygen carrier materials for hydrogen production via chemical looping processes. Fuel 181, 75–93 (2016) 13. Mattisson, T., Lyngfelt, A., Cho, P.: The use of iron oxide as an oxygen carrier in chemicallooping combustion of methane with inherent separation of CO2 . Fuel 80, 1953–1962 (2003) 14. Ryu, H.J., Gin, G.T.: Chemical-looping hydrogen generation system: performance estimation and process selection. Korean J. Chem. Eng. 24(3), 527–531 (2007) 15. Mahalatkar, K., Kuhlman, J., Huckaby, E.D., O’Brien, T.: Computational fluid dynamic simulation of chemical looping fuel reactors utilizing gaseous fuels. Chem. Eng. Sci. 66, 469–479 (2011) 16. Gunn, D.J.: Transfer of heat or mass to particles in fixed and fluidized beds. Int. J. Heat Mass Transf. 21, 467–476 (1978) 17. Cho, W.C., SeO, M.W., Kim, S.D., Kang, K.S., Bae, K.K., Kim, S.H.: Reactivity of iron oxide as an oxygen carrier for chemical-looping hydrogen production. Int. J. Hydrogen Energ. 37, 16852–16863 (2012) 18. Hossain, M.M., Lasa, H.I.: Chemical-looping combustion (CLC) for inherent CO2 separation— a review. Chem. Eng. Sci. 63, 4433–4451 (2008) 19. Clift, R., Grace, J.R.: Continuous Bubbling and Slugging. London: Academic Press (1985)

Enhancement in Product Value of Potato Through Chemical Pre-treatment and Drying Process M. B. Gorawar , S. V. Desai , V. G. Balikai , and P. P. Revankar

1 Introduction The agricultural product preservation plays a key role as a post-harvesting strategy to enhance shelf life without loss of nutrients. The agricultural sector is reeling under major losses due to poor post-harvest techniques that have led to every fifth portion of harvested product lost either to rodents or microbial decay due to high moisture in food products. The moisture content, air flow rate, drying air temperature, relative humidity and pre-treatment process are important parameters governing drying process. Agricultural products are made preservation-ready using methods like drying, chemical processing and heat treatment. The open sun drying is most common and widely used drying method across the world. It involves spreading crop on open ground for sun drying through exposure to natural convective air until moisture reduces to a level that inhibits growth of microorganisms like microbial infestation. This mode of drying has unregulated solar insolation, intermittency of solar energy and contaminations due to direct exposure. The well-designed preservation techniques based on electric drying and indirect type solar drying help store food products for longer time, minimize the wastages and transport for longer distance without spoilage. The characterization of drying process is important to obtain parameters that yield hygienic products in lowest possible drying time and drying cost. The microbiological studies indicate hygiene level of dried product that is essential to ascertain suitability and safety aspects of dried product.

M. B. Gorawar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, KLE Technological University, Hubballi, India e-mail: [email protected] S. V. Desai Department of Biotechnology, KLE Technological University, Hubballi, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_41

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2 Literature Review The pre-treatment process reduced enzymatic activities in potato slice that resulted in surface discoloration of potato slices due to melanin formation. The 2% ethanol produced darker color, while texture was lost on account of acetic acid and ethanol that rehydrated potato slice [1]. Tripathy and Kumar analyzed dehydration rate in potato slice of cylindrical geometry with thickness 0.01 m and diameter 0.05 m under unsteady state that led to enhanced convective heat transfer coefficient due to larger surface area exposed to convective fluid. The specific energy consumption was higher for initial hours of drying and gradually decreased as it progressed owing to resistance for initial moisture removal on account of unbroken surface that limited moisture migration [2]. Bacelos and Almeida developed finite control volume model to analyze shrinkage and internal resistance to mass transfer in spherical potato of 10 mm diameter. The drying model that neglected shrinkage factor exhibited a good agreement with experimental data during early drying period, this trend however overestimated drying parameters for the later stage of drying process against observed experimental data [3]. Chayjan investigated drying of potato slice using different modes that included fixed thin layer, semi-fluidized and fluidized bed under laboratory conditions in the range of 40–70 °C. The moisture diffusivity directly related to temperature with activation energy between 15.88 and 24.95 kJ/mol [4]. Shekofteh et al. experimented on operating parameters for shrinkage of potato slices through indoor tests at 60, 70 and 80 °C of drying air temperature obtained using variable heat source (electric lamp) for air velocity in range of 0.5–1 m/s. The drying air temperature significantly influenced drying rate and shrinkage of potato slice as compared to changes in airflow velocity [5]. Tesfamichael et al. designed and developed natural convective solar crop dryer to analyze drying characteristics of potato slices. The clear sky days witnessed heated air at 14–29 °C temperature above ambient air temperature. The drying yielded better quality on account of avoidance of insects and contamination apart from 30% lower drying time than open sun drying system at similar climatic conditions [6]. Darvishi et al. discussed effect of heat supplied by microwave dryer in potato drying with respect to shape, energy consumption and energy efficiency. It was observed that with increase in input heat, the drying rate in cylindrical and rectangular geometry potato slices increased by up to 56% and 42%, respectively. However, cylindrical slices required 25% higher specific energy compared to rectangular slices [7]. Dagde and Nmegbu analyzed effect of temperature on drying of potato slices in batch tray dryer on basis of energy balance equation and appropriate boundary conditions. The slice surface temperature increased, while moisture content decreased as drying progressed in good agreement with published literature [8]. Naderinezhad et al. analyzed potato slice drying in hot convective air dryer at varied air temperatures between 45 and 70 °C and air velocities of 1.60 and 1.81 m/s. The air temperature significantly influenced drying rate of potato slices to the tune of about 25% lower drying time for increase by 10 °C. The drying rate for square slices was more than that for other shapes of potato slices

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[9]. Bundalevski et al. investigated kinematics of potato slice drying in vacuum farinfrared air temperature of 120–200 °C in the pressure range between 20 and 80 kPa. The results compared well with reported drying models that indicated direct relation of drying time with operating temperature and inversely to air pressure. The performance index, coefficient correlation, low chi-squared and RMSE values were reported in the study [10]. Amjad et al. assessed energy utilization, energy utilization ratio and exergy of single layer and split layer potato slice (thickness 5 and 8 mm) dried in temperature range of 55–65 °C using diagonal batch type electric dryer [11]. Adeniyi et al. investigated microbiological factors of processed potato fermented in 2% brine solution. The nutritive qualities of brine fermented samples were analyzed, and organoleptic parameter was accessed through trained panelist [12]. Amany et al. have investigated dried samples in the form of slices of common potato varieties like sponta, glactica, valor and leady through sensory evaluation for assessment of their food value. The dried potato samples were processed into edible food product after frying in sun flower oil at 180 ± 5 °C. The test samples were obtained every day for five consecutive days for conduct of organoleptic tests on fried chips. The samples of sunflower oil used in frying of dried samples of potato chips were also subjected to physicochemical assessment. Duran et al. tested microbiological quality of retail products of frozen hash brown, dried hash brown with onions, frozen French fried, dried instant mashed and potato salad. The colony counts per gram of dried product were, respectively, 270, 580 and 78 for hash brown potato, frozen hash brown potato with onion and frozen French fried potato. The instant mashed potatoes and potato salad had geometric mean values for aerobic plate counts per gram in cfu/ml as 3 log10 and 3.6 log10 , respectively [13].

3 Material and Pre-treatment Process The potato slices were washed in water and peeled with clean steel knife before immersion into solution containing NaCl (2%) for 5 min. The peeled potatoes sliced to 4–5 mm thickness are subjected to blanching process (immersed in boiled water for 5–10 min) and cooled before being treated in solution of NaCl (2%), Citric acid (0.2%) and Potassium meta-bisulphate (0.2%). The flowchart in Fig. 1 reflects the steps in potato slice pre-treatment. The chemical pre-treatment of potato induces desirable properties that make moisture removal better without any deterioration in product hygiene. Two identical control samples of sliced potato were prepared for study on effect of chemical pre-treatment on drying parameters in electric cabinet dryer. The study investigates choice of appropriate air temperature, air flow rate and chemical pretreatment in order to gain suitable product properties at accelerated drying of potato.

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Fig. 1 Flowchart of chemical pre-treatment for potato drying

4 Details of Experimental Setup for Electrical Drying The experimental setup used for study on crop drying as indicated in Figs. 2 and 3 consists of an electric blower, orifice, and electric air heater along with a drying chamber (cabinet) for removal of moisture from the product to be dried.

Fig. 2 Line diagram of experimental setup of cabinet drying unit

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Fig. 3 Experimental setup installed for investigation

The experimentation investigation involved two stages of sample preparation and drying. The primary stage had water washing of potato that removed impurities before peeling operation using clean steel knife. The peeled potatoes were soaked in cold water containing 2% NaCl solution (by volume) for about 5 min followed by their sizing to circular shape. The subsequent blanching in hot water (80 °C) for about 5–10 min was followed by its cooling to ambient temperature. The blanched potato slices were immersed in water containing metered quantity of NaCl (2%), Citric acid (0.2%) and Potassium meta-bisulphate (0.2%) (KMS) under controlled condition. The pretreated test samples were washed in clean water and spread in single layer on stainless steel tray with nylon mesh for drying as shown in Fig. 4a–d. The experimental investigations used electric crop dryer to predict drying characteristics of potato slice in terms of moisture ratio, drying rate and drying time for regulated drying air temperatures of 60, 80 and 100 °C, and three airflow rates of

a. Fresh potato

b. Peeling and NaCl treatment

c. Slicing and Blanching

d. Spreading after treatment

e. Final dried sample

f. Packed sample

Fig. 4 Experimental stages of drying potato

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0.015, 0.02 and 0.025 kg/s delivered through the variable speed air blower. The initial cabinet moisture was eliminated before each set of observation through the no-load test conducted at specified air temperature, and airflow rate for 30 min duration with an empty stain steel tray (known weight) was placed in drying cabinet.

5 Results and Discussion This section discusses important experimental observations made with respect to drying characteristics of potato slice and microbiological analysis of dried potato slice. The study includes variation of relative humidity, drying cabinet outlet temperature, drying time and weight reduction. The tests were conducted at constant drying chamber inlet temperature for various air mass flow rate (0.015, 0.02 and 0.025 kg/s) and vice versa for constant air mass flow rate at varying drying chamber inlet temperature (60, 80 and 100 °C).

5.1 Drying Characteristics of Potato Slice Figure 5 depicts variation in % moisture reduction with drying time for the air flow rate of 0.015 kg/s. It was observed that the % moisture reduction was higher during initial drying period for all air temperatures, due to the presence of higher loose moisture content on surface skin of potato slice. After removal, this surface moisture drying rate momentarily decreased and later increased during later phase of drying cycle. The drying rate strongly depends on molecular diffusivity of potato slice. The higher drying air temperature increased moisture reduction due to higher dispersion Fig. 5 Temperature dependence of % moisture reduction in potato slice drying

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in air molecules; however, relative change was more for rise in air temperature from 60 to 80 °C as compared air temperature rise from 80 to 100 °C. The potato slices attained desired moisture content within drying time durations of 7 h, 8 h and 10 h, respectively, for air temperatures 100, 80 and 60 °C that indicated strong influence drying air temperature on drying rate. The general trend indicated that weight reduction of potato slice was accelerated with increase in the mass flow rate of heated air on account of faster heat removal and better movement of the moisture particles after release from the potato surface. Results suggested that inlet temperature of 100 °C with a mass flow rate of 0.025 kg/s yielded fastest drying rate of 4.5 h for the 0.4 kg sample of potato. The drying time in case of 60 and 80 °C inlet air temperature was, respectively, higher by 22 and 11% for the identical mass flow rate of 0.025 kg/s. Similarly, in case of lower mass flow rate of air (0.015 kg/s), the drying time was higher by 50% and 28%, respectively, for 60 and 80 °C inlet air temperature as compared to 100 °C inlet air temperature that had a drying time of 6 h. The experiments were conducted on chemically treated potato slice and untreated slice of same geometry under similar operating conditions, 0.02 kg/s and at the air temperature of 60 °C to investigate the effect of chemical treatment on drying time, and results are presented in Fig. 6. However, quality of chemical treated slice in terms of color and texture was remarkably better that untreated slices as shown in Fig. 6 due to higher enzymatic activities and larger shrinkages. The nature of drying any agricultural product is explained by the generalized exponential drying curve with time-dependent moisture removal M(t) to be a function of drying constant ‘k’ as represented by Eq. 1. M(t) = Mo exp[−kt]

(1)

The drying constant is specific to nature of dried product and the drying conditions of temperature, humidity and air flow rate. Fig. 6 Weight reduction in CT/UT potato slice

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Table 1 Temperature dependence on drying characteristics of potato slice S. No. 1

Temperature (°C) 60

2

80

3

100

Mo

k day−1

R2

M(t) = 28.451

[exp−0.274 t ]

28.451

0.274

0.7454

M(t) = 40.932

[exp−0.371 t ]

40.932

0.371

0.6513

M(t) = 25.723 [exp−0.172 t ]

25.723

0.172

0.7909

Drying equation

Table 2 Influence of chemical treatment on drying characteristics of potato slice S.No.

Drying mode

Drying equation

Mo

k, h−1

R2

1

CT

M(t) = 404.58 [exp−0.203 t ]

404.58

0.203

0.9838

UT

[exp−0.201 t ]

415.78

0.201

0.9797

2

M(t) = 415.78

Fig. 7 Comparison of dried potato slices

a. UT sample

b. CT sample

Table 1 indicates the influence of temperature on the drying characteristics of potato as depicted through the drying constant that suggest accelerated drying at higher temperature. Similarly, the influence of chemical treatment on moisture removal as depicted in Table 2 strongly favors chemical treatment that has marginally higher rate along with hygienically acceptable end product as shown in Fig. 7a, b.

5.2 Microbiological Analysis of Dried Products The microbiological analysis of potato drying for bacterial and fungal (yeast and mold) count was performed and compared with different inlet temperatures and mass flow rate. The nutrient agar (NA) and potato dextrose agar (PDA) were used to culture bacteria and fungi, respectively.

5.2.1

Total Plate Count Method for Bacterial Infestation

The plate count technique used routinely measured bacteria containing samples that were cultured on nutrient agar medium visible in the form of clustered colony making

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Table 3 Bacterial count for potato slice Dried product Potato slice

Temperature (°C)

Colony count, (log10 colony forming units/g) 0.015 kg/s

0.02 kg/s

0.025 kg/s

60

3.9

3.2

2.9

80

2.7

2.3

2.1

100

1.7

1.5

1.2

it possible to obtain their quantitative measure. The physical count on the number of colonies developed directly indicated the number of organisms in the sample. The serial dilution was executed cumulatively by transferring the known volume of first dilution to second dilution blank. The process was successively proceeded on to third, fourth, fifth and sixth dilution blanks. Once diluted, the specified volume of dilution sample (1 or 0.1 ml) from various dilutions was added to sterile petri plates (in duplicate for each dilution) to which molten and cooled (45–50 °C) agar medium was added. The colonies were counted on a colony counter.

5.2.2

Procedure for Microbiological Analysis

Microbiological analysis of cabinet dried potato slice for total viable count was performed with all experiments were carried out in duplicates. Analysis of bacteria and yeasts and molds was performed by pour plate and spread plate method on nutrient agar and potato dextrose agar, respectively. The nutrient agar plates were incubated at 37 °C for 24–48 h, and potato dextrose agar plates were incubated at ambient room temperature for 3–4 days (Table 3).

5.2.3

Sensory Evaluation of Fried Potato Chips for Organoleptic Properties

The final stage to ensure the acceptance of dried product termed as sensory evaluation was carried out on chemically treated dried potato slice with the help of expert panelist to analyze effect of drying parameters on product quality. For sensory evaluation, the potato slice was fried at 180 ± 5 °C with sunflower oil. Each sample was randomly numbered and presented to the panel which consists of twelve inexperienced members to gauge its utility as a food item. The response of the participants on sensory quality of product was collected through the questionnaire designed on a ‘one to four scale’ to assess the attributes of taste, texture, color odor and overall appearance. The designed scale was rated as (1) Dislike, (2) Acceptable, (3) Like and (4) Like very much.

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Table 4 Results of sensory evaluation (on a scale of 4) CT

Taste

Texture

Color

Odor

Overall appearance

Sample 1

3

3.5

3.8

4

3.9

Sample 2

3

3.5

3.7

4

3.7

Sample 3

3

3.5

3.6

4

3.6

Sample 1

Sample 2

Sample 3

Table 4 summarizes sensory evaluation results for three CT samples (indicated 1, 2 and 3) of potato dried at a temperature of 80 °C with flow rates of 0.015, 0.020 and 0.025 kg/s that showed positive sign in terms of all the sensory parameters considered. Hence, it was concluded that drying process devoid of chemical pre-treatment was not suitable owing to discoloration and poor hygiene value of final product.

6 Conclusions • The air inlet temperature and mass flow rate strongly influenced drying time as per the experimental observations. The drying air temperature change from 60 to 100 °C had drying time reduced from 9 to 6 h for 0.015 kg/s airflow rate as against reduction in drying time from 7 to 4.5 h for 0.02 kg/s air flow. • As mass flow rate of air was increased from 0.015 to 0.025 kg/s, the drying time is reduced from 9 h, 8 h, and 6 h to 7 h, 6 h and 4.5 h, respectively, for 0.02 kg/s. • The microbiological analysis of dried product favored adoption of appropriate preservation strategy on the basis of drying characteristics like air inlet temperature and mass flow rate. The increase in drying temperature decreased the microbial activity, but however, the upper limit was fixed on the basis of safe drying temperature of the product to ensure retention of its nutritional value and equilibrium moisture content. • The lowest temperature studied with respect to drying (60 °C) indicated a high bacterial count of 2.9–3.1 log10 cfu/ml which was not considered as good microbiological quality. However, since these are intermediate products, which need to be processed by frying before their consumption, it is of less concern in regard to health. The counts tend to further decrease upon subjecting to frying.

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• The test on food quality of finished product with respect to CT samples of potato slice revealed the absence of yeast and mold in the final product; however, the dried versions of UT potato samples showed growth of mold leading to discoloration as against the CT samples that exhibited bright color. • The intangible results in terms of improvement in the farming sector have not been quantitatively evaluated in this study, but however, the impact is significant owing to the crisis like situation faced due to successive failure of good monsoon in India.

References 1. Ezekiel, R., Rani, M.: Effect of pre-dehydration chemical treatment on enzymatic discoloration and rehydration of solar dehydrated potato slices and cubes. Potato J. 33(3–4), 104–109 (2006) 2. Tripathy, P.P., Kumar, S.: Modeling of heat transfer and energy analysis of potato slices and cylinders during solar drying. Appl. Therm. Eng. 29, 884–891 (2009) 3. Bacelos, M.S., Almeida, P.I.F.: Modelling of drying kinetic of potatoes taking into account shrinkage. Procedia Food Sci. 1, 713–721 (2011) 4. Chayjan, R.A.: Modeling some drying characteristics of high moisture potato slices in fixed, semi fluidized and fluidized bed conditions. J. Agric. Sci. Technol. 14, 1229–1241 (2012) 5. Mohammad, S., Eskandari, C.F., Soheila, K., Yasin, H.: Study of shrinkage of potato sheets during drying in thin-layer dryer. Res. J. Appl. Sci. Eng. Technol. 4(16), 2677–2681 (2012) 6. Aklilu, T., Abebayehu, A.: Experimental analysis of potato slices drying characteristics using solar dryer. J. Appl. Sci. 13(6), 939–943 (2013) 7. Hosain, D., Hamid, K., Ahmad, B., Mehdi, L.: Effect of shape potato chips on drying characteristics. Int. J. Agric. Crop Sci. (2013). ISSN 2227-670X 8. Dagde, K.K., Nmegbu, C.G.J.: Mathematical modeling of a tray dryer for the drying of potato chips using hot air medium. Int. J. Adv. Res. Technol. 3(7) (2014). ISSN 2278-7763 9. Naderinezhad, S., Etesami, N., Najafabady, A.P., Falavarjani, M.G.: Mathematical modeling of drying of potato slices in a forced convective dryer based on important parameters. Food Sci. Nutr. 4(1), 110–118 (2016) 10. Bundalevski, S., Mitrevski, V., Lutovska, M., Geramitcioski, T., Mijakovski, V.: Experimental investigation of vacuum far-infrared drying of potato slices. J. Process. Energ. Agric. 19(2), 71–75 (2015) 11. Amjad, W., Hensel, O., Munir, A., Esper, A., Sturm, B.: Thermodynamic analysis of drying process in a diagonal-batch dryer developed for batch uniformity using potato slices. J. Food Eng. 169, 238–249 (2016) 12. Basuny, A.M.M., Mostafa, D.M.M., Shaker, A.M.: Relationship between chemical composition and sensory evaluation of potato chips made from six potato varieties with emphasis on the quality of fried sunflower oil. World J. Dairy Food Sci. 4(2), 193–200 (2009) 13. Oliveira J.V., Alves M.M., Costa J.C.: Optimization of biogas production from Sargassum sp. using a design of experiments to assess the co-digestion with glycerol and waste frying oil. Biores. Technol. (14), 01553–3 (2010). ISSN 0960-8524

Desalination Using Waste Heat Recovery with Active Solar Still Sandeep Kumar Singh, S. K. Tyagi, and S. C. Kaushik

Nomenclature Gr hcw hew md Pr pg pw qew Tw Tg CPCB ICMR ISI Nu USEPA WHO NR

Grash of number Convective heat transfer coefficient (W/m2 K) Evaporative heat transfer coefficient (W/m2 K) Distillate output (kg/s) Prandtl number Partial saturated vapor pressure at glass temperature (N/m2 ) Partial saturated vapor pressure at water temperature (N/m2 ) Rate of evaporative heat transfer (W/m2 ) Water temperature (K) Glass temperature (K) Central Pollution Control Board Indian Council of Medical Research Indian Standard Institution Nusselt number United States Environmental Protection Agency World Health Organization No relaxation

1 Introduction Nearly 71% area of the total earth’s surface is covered with water, though it is hard to complete the demand of all human and habitats. Approximately, 2.5% of freshwater S. K. Singh · S. K. Tyagi (B) · S. C. Kaushik Indian Institute of Technology Delhi, New Delhi, Delhi 110016, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_42

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is available, mainly in the form of groundwater, glaciers, and ice caps, and in that, accessible freshwater surface is only 0.008% [1]. Water scarcity counts when the supply of water supply goes below 1000 m3 /person per year [2]. Currently, one-third of total world resident’s population experience drastic water problem and anticipated to an escalation in percentage [3]. Not only India but also in many areas of the USA, increase in municipal contamination and private well for drinking water, primarily due to extensive use of fertilizers, as well as waste from human and animal, has been documented. Many people suffered from cholera, jaundice, and water-borne diseases. One of the primary approaches to reduce the water shortage is a desalination process. Through this process, potable water from saline or brackish water can be produced [4]. Conventionally, seawater desalination requires lots of energy intensive, is more expensive, and discharges an enormous amount of high salinity brine [5]. Salinity depends upon the total dissolved solids (TDS); for brackish water and seawater, TDS is up to 10,000 ppm and 45,000 ppm, respectively [6]. Permissible limit of drinking water quality is expressed in Table 1 [7]. By desalination, 1000 m3 of water can be produced in a day, but it requires extensive energy, about 10,000 tons of fossil fuel per year [8]. To decrease the carbon footprint and the emission of greenhouse gases, the use of renewable and sustainable energy resources is crucial because these gases are the main reasons for climate change and global warming. Solar energy is unable to provide continuous Table 1 Potable water quality range [7] Parameters pH

(kg/m3 )

WHO ×

10−3

Turbidity NTU Fluoride

(kg/m3 )

×

10−3

CPCB

ISI

USEPA

ICMR

(6.5–8.5)

(6.5–8.5)

(6.5–8.5)

(6.5–8.5)

(6.5–9.2)



10

10



25 1.5

1.5

1.5

0.6–1.2

4.0

Alkalinity (kg/m3 )



0.6







Total hardness (kg/m3 )

0.5

0.6

0.3



0.6

Calcium (kg/m3 ) × 10−3

75

0.2

75



0.2

Chlorides (kg/m3 )

0.2

1

0.250

0.250

1

Lead (kg/m3 ) × 10−3

0. 05

NR

0.10



0.05





0.2





Chromium (kg/m3 )



NR

0.05

0.1



Magnesium (kg/m3 ) × 10−3

50

0.1

30





Residual

(kg/m3 )

free



NR







Sulfate (kg/m3 )



0.4

0.15



0.4

Iron (kg/m3 ) × 10−3

1.0

E. coli (MPN/ 0.1

m3 )

0.1

1.0

0. 3



(kg/m3 )



0.1

45



0.1

Copper (kg/m3 ) × 10−3

1.0

1.5

0.05

1.3

1.5

Conductivity (kg/m3 )



2000







5.0

15.0

5.0



0.10

Nitrate

Zinc

(kg/m3 )

×

10−3

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operation of systems due to its intermittent nature. Agricultural wastes such as rice husk and bagasse are a potential source of energy which if unutilized is liable to cause severe environmental pollution crisis. Biomass being a CO2 neutral fuel helps in reducing the carbon footprints if combustion is clean and hence is an emerging field in the area of solarbiomass hybrid desalination systems. With the combination of solar field, biomass fired boiler at a temperature less than 90 °C can operate desalination plant after the sunshine hour.

2 Desalination Technologies Desalination is a procedure to take away the important mineral form the brackish or saline water. Nearly, 1% of the world’s total population depends upon the water obtained from desalination, but it is expected from the United Nation that almost 14% of the world population will face scarcity of water by 2025 [9]. The process of desalination consumes lots of energy and also has some adverse effect on the atmosphere. Conventional desalination units run on fossil fuel which contributes to greenhouse gas (GHG) emissions. Use of renewable energy can prevent depletion of fossil fuel that is non-renewable in nature, which has motivated the researcher to search for an option to operate the desalination plant by using the energy from a renewable source [10], and different solar distillation systems shown in Fig. 1. It is estimated that approximately, 80% of the total world’s desalination capacity is delivered by two technologies: Reverse osmosis (RO) and multi-stage flash (MSF). Almost 40% of the total world’s desalination capacity is covered by the Middle East, and they widely use MSF (particularly in Kuwait, UAE, and Saudi Arabia) [11]. MSF and multi-effect desalination (MED) procedures comprise a set of stages at continuously decreasing pressure and temperature. In MSF, reduction of pressure takes place suddenly when saline water enters into the evacuated chamber followed by vapor generation, and with the decreasing pressure, this process occurs repetitively. The steam nearly at a temperature of 100 °C is supplied externally, which is primarily required for the process to occur. In MED, the generation of vapor takes place by thermal energy

Fig. 1 Classification of solar distillation systems

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absorption from seawater. This generated steam in the first stage will heat the salt solution in the succeeding stage because the following stage is at lower pressure and temperature. Process performance depends upon the number of stages and effects. In mechanical vapor compression (MVC) and thermal vapor compression (TVC), the production of vapor can be increased by compressing the vapor which is generated from the initial saline solution either mechanically or thermally. Reverse osmosis has no limitation; it can desalt seawaters or brackish water, whereas electrodialysis has some limitation; usually, it is only used for brackish feed water [12]. Different desalination technologies are as follows: For selecting the particular desalination process, several factors [13] have to be considered, such as: a. b. c. d. e. f. g. h. i.

Saline water treatment requirements. The simplicity of operation and robust criteria. Compact size and low maintenance. The capital cost of the equipment and material. The effectiveness versus energy consumption of the selected process. Required land area for the equipment installation. Interest, approval, and local support with the least changes to the societal sphere. The relevance of the process with the solar energy application. The required quantity of potable water with the application of several desalination processes.

3 Experimental Setup The proposed experimental setup shown in Fig. 2 consists of a solar still, pelletbased cookstove, and a tank situated at an altitude. Tank filled with brackish water

Fig. 2 Solar desalination system with waste heat recovery from cookstove

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Fig. 3 Solar still mechanism

has a certain level of salinity, total dissolved solids, and impurities of that specific area. Water is supplied by gravitation to the water jacket surrounding biomass pelletbased cookstove, and after gaining sensible heat, it is supplied to the solar still. Solar still during daytime works as a desalinated unit, and at nighttime, it works as a condensing unit. The biomass cookstove has an outer and inner diameter of 0.180 m and 0.125 m, respectively, having height 0.350 m and made of mild steel. The feed water is naturally circulated to the water jacket under a controlled condition which is situated above the height of cookstove. In Fig. 3 a solar still with darkened basin filled with saline water or brackish water at a confined depth. It is roofed by an inclined transparent glass for solar radiation transmission and condensation which is due to the temperature difference between glass and basin temperature. The blackened liner is heated up by the solar energy which is entering the basin, and evaporation of water takes place. Due to partial difference in pressure and temperature, condensation of the water vapor takes place on inclined glass cover and collected at the bottom by providing the appropriate provision. In case of conventional still basin, the condensate of high quality obtained which is 2–3 m3 per unit area (m2 ) per day [14], this is the daily minimum requirement of an adult person as mentioned by the WHO [15, 16]. Solar desalination technology is one of the most suitable technologies for remote area dwellers because its construction is economical and has very low maintenance. It is used for evaporating brackish water to obtain potable water. Evaporation takes place by using solar heat from the sun, leaving behind the residue of salt and other contamination. The vapor from the evaporated water condenses on the surface of the cover and is collected as a fresh distilled water. Solar still basin type’s yield is given [12] by: md =

qew × Aw h fg

qew = h ew × (Tw − Tg )

(1) (2)

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h ew = 16.273 × 10−3 × h cw ×

( pw − p g ) (Tw − Tg )

(3)

The convective heat transfer coefficient is given by Duncle’s relation as 

h cw

( pw − pg ) × (Tw − Tg ) = 0.884 × (268.9 × 103 − pg )

 13 (4)

Among the 1.21 billion Indians, 0.833 billion live in rural areas, while 0.377 billion stay in urban areas, and approximately 75.1% household still use solid fuel for biomass cookstove [17], made from sawdust, coconut husk, agro waste, and palm waste, etc. The thermal imaging test has been carried out as shown in Fig. 4, using infrared camera (FLIR A325sc, spectral range 7.5–13.0 µm, standard temperature range 0–350 °C) for real-time assessment of outer surface temperature [18]. It has been found that a significant amount of heat is released from cookstove wall, which can be utilized in the water jacket to fulfill the heating requirement. During off-sunshine hours, the solar still acts as a condenser unit with the use of the biomass cookstove unit. A water jacket is to be fabricated around the cookstove as per the simulation results obtained from SOLIDWORKS 2017 premium [19]. Simulation is carried out at a variable mass flow rate and the diameter of the water jacket (Table 2). Total heat available/waste heat at the wall of the cookstove is given as: For all value of Gr and Pr at a constant heat flux ⎤2

⎡ 0.167

0.387(Gr Pr) ⎥ ⎢ Nu = ⎣0.82 +   0.492 0.5625 0.296 ⎦ 1 + Pr

Fig. 4 Thermal imaging test of cookstove during cooking

(5)

Desalination Using Waste Heat Recovery with Active Solar Still Table 2 Biomass cookstove wall temperature range

S. No.

Height range (m)

445 Temperature (°C)

1.

Above 0.30

350

2.

Between 0.25 and 0.30

340

3.

Between 0.15 and 0.25

220–315

4.

Between 0.00 and 0.15

105–115

Gr =

g β(Tmax − Tmin )L 3 υ3

(6)

Total heat available can be calculated as: Q = U AT

(7)

4 Results and Discussion The results have been retrieved from the simulation for various mass flow rates such as 0.0400, 0.0500, and 0.0600 kg/s, and the thickness of the water jacket considered is 0.006 and 0.008 m from the outer diameter of the cookstove for the same mass flow rate, so the total diameter with different thickness is 0.192 m and 0.196 m, respectively. The mean average temperature (T mean ) is 187.5 °C. At this mean average temperature and atmospheric pressure, the properties of the gases have been taken and the total heat available at the wall of the cookstove is 344 W. The following results obtained are shown in Fig. 5. 0.006 m

400

0.008 m

Temperature (°C)

350 300 250 200 150 100 50 0 0.004 kg/s

0.005 kg/s

Mass flow rate of water

Fig. 5 Gain in temperature at a different mass flow rate and thickness

0.006 kg/s

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The thickness of water jacket should be 0.008 m with the mass flow rate of 0.05– 0.06 kg/s, to fulfill the temperature requirement of the solar still which is around 50–70 °C. With this hybrid system, the productivity of solar still will be enhanced.

5 Conclusions In the proposed system, the effect of varying mass flow rate and inlet feed water temperature which is supply to the still was analyzed, and the following conclusion can be drawn from the present study: a. Waste energy utilized by the flowing water in the hybrid system for evaporation. b. When the mass flow rate is minimum, the heat transfer linearly increases and vice versa, so the average value of the mass flow rate is 0.055 kg/s. c. Increase in water jacket thickness will decrease the temperature of water which supplies to the still, so it should not go beyond 0.008 m. d. A system is able to run during off-sunshine hours. e. Able to fulfill the water requirement of a small family. For the people living below the poverty line, if the government helps by some initial investment, so this section of the population may get access to drinkable water at almost zero cost.

References 1. Wong, K.V., Pecora, C.: Recommendations for energy–water–food nexus problems. J. Energ. Res. Technol. 137(7), 32002 (2015) 2. Rijsberman, F.R.: Water scarcity: fact or fiction? Agric. Water Manag. 80(1–3), 5–22 (2006) 3. Jimenez-Cisneros, B.: Responding to the challenges of water security: the eighth phase of the international hydrological programme, 2014–2021. In: Proceedings of the 11th Kovacs Colloquium, Paris, France, vol. 366, pp. 10–19. International Association of Hydrological Science (2015) 4. Elimelech, M., Phillip, W.A.: The future of seawater desalination: energy, technology, and the environment. Science 333, 712–717 (2011) 5. Miller, S., Shemer, H., Semiat, R.: Energy and environmental issues in desalination. Desalination 336, 2–8 (2015) 6. Micale, G., Cipollina, A., Rizzuti, L.: Seawater Desalination: Conventional and Renewable Energy Processes, 1st edn. Springer, Berlin, Heidelberg (2009) 7. Kumar, M., Puri, A.: A review of permissible limits of drinking water, Indian. J. Occup. Environ. Med. 16(1), 40–44 (2012) 8. Methnani, M.: Influence of fuel costs on seawater desalination options. Desalination 205(1–3), 332–339 (2007) 9. Water Security. https://www.globalwaterintel.com/. Last accessed on 15/02/2019 10. Subramani, A., Badruzzaman, M., Oppenheimer, J., Jacangelo, J.G.: Energy minimization strategies and renewable energy utilization for desalination: a review. Water Res. 45, 1907–1920 (2011)

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11. Rao, S.M., Mamatha, P.: Water quality in sustainable water management. Curr. Sci. 87(7), 942–947 (2004) 12. Ettouney, H., El-Dessouky, H., Alatiqi, I.: Process control in water desalination industry: an overview. Desalination 126(1–3), 15–32 (1999) 13. Oldach, R.: Matching Renewable Energy with Desalination Plants. Project Report MEDRC Project: 97-AS-006a (2001) 14. Qiblawey, H.M., Banat, F.: Solar thermal desalination technologies. Desalination 220(1–3), 633–644 (2008) 15. World Health Organization: Guidelines for Drinking-water Quality, vol. 1, 3 edn. (2006) 16. Bhardwaj, R., Tenkortenaar, M.V., Mudde, R.F.: Inflatable plastic solar still with the passive condenser for single family use. Desalination 398, 151–156 (2016) 17. Census of India 2011. http://censusindia.gov.in/Census_And_You/area_and_population.aspx. Last accessed 2019/02/09 18. FLIR A325sc. https://www.flir.in/products/a325sc/. Last accessed 2018/12/03 19. SOLIDWORKS 2017 Premium, Flow Simulation 2017 SP3.0. Build: 3794

Incorporating Battery Degradation in Stand-alone PV Microgrid with Hybrid Energy Storage Ammu Susanna Jacob, Rangan Banerjee, and Prakash C. Ghosh

1 Introduction In a stand-alone renewable microgrid, the supply and demand variability is found in different time scales, i.e. instantaneous, diurnal, and seasonal. A single energy storage device cannot cater to these varied fluctuations. Therefore, we combine two or more energy storage to provide a reliable supply to the load forming a hybrid energy storage. An optimal method to combine different energy storage units is based on its nominal discharge duration as it can be easily correlated with the supply demand variability. It is important to analyse the performance of these complex energy storage systems in a microgrid context. The analysis of hybrid energy storage system in a microgrid context with varying lifetimes for battery storage is not found in literature. The motive of this paper is to model and simulate a microgrid with hybrid energy storage system (battery, supercapacitor, and hydrogen storage) by taking into account the battery degradation and analyse the system reliability and economics. Two off-grid microgrid case studies where the use of hybrid storage can be justified are examined—telecom tower and a welding shop.

A. S. Jacob · R. Banerjee · P. C. Ghosh (B) Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India e-mail: [email protected] A. S. Jacob Center for Study of Science, Technology and Policy, Bengaluru 560094, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_43

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2 System Design and Evaluation The block diagram for the generic system with hybrid storage is shown in Fig. 1. The system consists of PV, DC-DC converter with maximum power point tracking, supercapacitor as short-term storage, battery as mid-term storage, and hydrogen storage with fuel cell and electrolyser as long-term storage. The hybrid storage system is connected to the DC bus through controllers (depicted as C 1 , C 2 , and C 3 ). The AC load is coupled to the DC bus through an inverter. The microgrid with hybrid energy storage system is sized using pinch analysis and design space approach. Here cumulative supply should always be greater than the cumulative demand. For the given supply, there is a pinch point, where supply meets the demand and storage is minimum. The minimum storage points are plotted for different generator ratings to obtain the sizing curves. The region above the sizing curve is the feasible region (where demand is met by the supply) or called as, the design space. The sizing curves for short-term, mid-term, and long-term storage are obtained, by repeating the analysis for different time scales (minutes to hour, hour to days, and week to year), thereby correlating the supply demand variability with the nominal discharge duration of various storage devices. Hence, for a PV rating we get one set of hybrid storage (short-term, mid-term, and long-term) for the given supply and demand. The resulting design curves are approximated to quadratic equations,

Fig. 1 Block diagram of the generic microgrid system with hybrid storage

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and the correlations are used as constraints to determine the optimal mix of supply and storage that minimise the life cycle cost. This sizing methodology for hybrid energy storage in PV microgrids is discussed in detail in our previous work by the authors [1].

2.1 Component Modelling The different components in the block diagram of Fig. 1 are modelled using its electrical equivalent circuit or its characteristic curves. The component modelling equations and its validation are shown in Table 1. The DC/DC converters are modelled as a lumped parameter model with a constant efficiency. Three controllers—C 1 , C 2 , and C 3 —supervise the system based on the availability of solar irradiance and load. The main controller C 1 controls the subcontroller C 2 and C 3 . C 2 controls the power flow between PV, storage, and load. C 3 selects the different storage based on rulebased energy management strategy and system requirements. The PV, battery, and battery degradation model are discussed in detail in our previous work by authors [2]. The different component models described above in Table 1 are compiled together in MATLAB/Simulink to simulate the entire system behaviour. The information flow diagram of the generalised system is shown in Fig. 2. The solar insolation, temperature, and the loads are given as input to the system model. The component blocks show the input, output, and the parameters governing each blocks.

2.2 Energy Management Strategy We follow a rule-based energy management strategy for the simulation. Solar PV is the primary source of power to the loads. When PV generation is insufficient, stored energy from battery supplies the load. If this energy does not satisfy the load, fuel cell operates and converts the hydrogen to electricity. Similarly, when excess energy from PV is available battery is charged. Once battery is fully charged and still excess energy is available; then, the electrolyser converts the excess electrical energy to hydrogen.

3 Case Study Two practical microgrid contexts with respect to Indian conditions are considered— off-grid telecom tower and a welding shop. Three metrics are used to evaluate the microgrid contexts—loss of load probability (LOLP), annualised life cycle costing (ALCC), and battery degradation rate. LOLP measures the probability that in a given

PV model validation of ECO 250 W using specification sheet [4]

Output current of PV1 ,     s −1 − Io_PV = IPV − I0 exp VPVV+IR ta VPV +IRs Rp

(continued)

Validation

PV [3]

Single-diode equivalent circuit of PV model

Component and references Modelling equations

Table 1 Modelling of different microgrid components and its validation

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Experimental validation of a 2 V, 500 Ah VRLA, gel type battery for a 50 A constant current discharge

K c C 0∗ K t 1+(K c −1)(I /I ∗ )δ Qe C ( Iavg ,θ )

0

θb (t) = ∫

t



Ct

t

a) Ps − (θ−θ R



Battery electrolyte temperature,

State of Charge, SOC = 1 −

C(I, θ ) =

Capacity dependence on current and temperature,

Terminal voltage of battery2 ,   1 + Em Vb = Ib R0 + Im R2 + τ1Rs+1

(continued)

Validation

Battery [5–7]

Lead acid battery equivalent circuit

Component and references Modelling equations

Table 1 (continued)

Incorporating Battery Degradation in Stand-alone PV Microgrid … 453

Supercapacitor [10]

Id (k)∅DOD (k)t Cλ,max

k=ti

⎣⎝ Ae

⎡⎛ −E a R



λfloat

1 − 1 θb (k) θref



⎠φDOD (k)⎦t



SOC =

VSC VSCfull

Voltage of the supercapacitor,  VSC = ISC RSC + C1SC i dt State of Charge of the supercapacitor,

RC model of a supercapacitor cell

Total degradation, δ(Id , DOD, θb k) = δ(Id , DOD, k) + δ(θb , k)

δ(θb , k) =

t f

Degradation of battery w.r.t. temperature,

k=ti

k=t f

(continued)

Supercapacitor model validation of 2.7 V, 350 F using specification sheet [11]

Battery degradation model is validated with respect to End of Life (EoL) given in technical specification [9] for the given conditions. The battery is cycled for 1800 cycles at C/10 charge and discharge at a temperature of 25 °C. The EoL in technical specification = 80% of initial capacity (i.e. 20% battery degradation). The Simulink model gives a battery degradation of 21.78%

Battery degradation model Weighted Ampere hour approach degradation of battery w.r.t. [8] discharge current and DOD3 ,

δ(Id , DOD, k) =

Validation

Component and references Modelling equations

Table 1 (continued)

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Since the characteristic curves of fuel cell and electrolyser are taken as look-up table, the fuel cell and electrolyser system do not require validation

U H2 = NFC ∗

IFC 2F

=

PFCstack 2F∗VFC

Utilisation rate of hydrogen (in mol/s),

(continued)

Validation

Fuel cell [12]

Typical characteristic curves of PEM fuel cell

Component and references Modelling equations

Table 1 (continued)

Incorporating Battery Degradation in Stand-alone PV Microgrid … 455

Hydrogen produced from the electrolyser is stored in a compressed tank. It is assumed that the electrolyser produces hydrogen at this pressure

PH2 = NEC ∗

IEC 2F

=

PECstack 2F∗VEC

Production rate of hydrogen (in mol/s),

P V —PV current due to solar radiation, I 0 —reverse saturation current of the diode, V PV —PV output voltage V t —thermal voltage of PV, a—ideality factor, Rp —PV shunt resistance, Rs —PV series resistance 2 V —battery voltage, I —battery current, R , R , R —equivalent circuit resistances, E —main branch voltage, K —parameter relating to battery equivalent 0 1 2 m c b b circuit, Qe —extracted charge, Ps —losses in battery, θ a —ambient temperature, Rt —thermal resistance, C t —thermal capacitance 3 δ(I , DoD, θ )—battery degradation fraction, I —discharge current,  DOD —depth of discharge stress factor, k—simulation step, A—Arrhenius constant, d b d E a —activation energy, C λ,max —maximal lifetime capacity, λfloat —float life of battery

1I

Validation

Electrolyser [13]

Typical characteristic curves of an electrolyser

Component and references Modelling equations

Table 1 (continued)

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Fig. 2 Information flow diagram of a generic PV stand-alone microgrid with hybrid storage

time period, the supply is less than the demand. ALCC accounts for all expenses incurred during the system lifetime and battery degradation rate is calculated based on degradation modelling given in Table 1. The hourly variations of solar insolation and temperature variations for a year of New Delhi climate are used as inputs for the simulation.

3.1 Off-Grid Telecom Tower For any telecom tower, a dependable and continuous power supply is a crucial requirement. Conventional off-grid towers powered by PV have a diesel generator with battery backup [14]. The advantage of replacing diesel generator with hydrogen technologies (electrolyser, fuel cell, and H2 tank) includes elimination of transportation cost of diesel fuel to the remote location, CO2 emission, and uncertainty in diesel price. Telecom tower is more or less characterised by constant load throughout the year. An average load of 72 kWh/day is assumed for the given context. The input solar radiation has daily and seasonal fluctuations that are met by the hybrid storage. The optimal PV, battery, and hydrogen storage that will meet the daily and seasonal fluctuations are 37 kWp , 96 kWh, and 5.2 m3 (at 200 bar), respectively. The DC distribution side voltage where PV and hybrid storage are connected is selected as 48 V. Hence, DC-DC converters are required to boost the PV, fuel cell, and electrolyser voltage to the bus voltage. 2 V 500 Ah batteries (with 50% DoD) are connected directly to the DC bus as 48 V strings. The battery and hydrogen SOC variation for the entire year is shown in Fig. 3. The fuel cell and electrolyser sizes are iteratively

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Fig. 3 Battery and hydrogen SOC variation for telecom tower

selected from the simulation. The p.u. cost of the system is 27 |/kWh (with DOE target cost for hydrogen storage and its subsystems). During the summer months, battery and hydrogen SOC are near its upper limit (0.8 and 1, respectively) and during winter months, their SOC is near its lower limit (0.3 and 0, respectively). The battery degrades due to time and temperature variation to 3.78% of its initial capacity in the first year. From the simulation, it is observed that the fuel cell and electrolyser are operated in their nominal regime and hence have rated lifespan. The above system is compared by incorporating battery degradation for two sets of design points—one with hybrid storage and the other conventional battery only design as shown in Table 2. It compares the yearly LOLP, battery degradation, and the COE of the system. With battery degradation, the life of battery is halved due to ambient and operating conditions. As a result, there is two rupees increase in the p.u. cost of the system. Overall, the reliability of any system improves with the addition of a long-term storage with mid-term storage (battery) at a low COE of the system.

3.2 Off-Grid Welding Shop Small industries like welding shop are characterised by spiky changes in load, which is 4–8 times the base demand. An off-grid welding shop powered by PV requires battery and a short-term storage like supercapacitor to account for minutely load fluctuations. The welding shop logged load is shown in Fig. 4a. The only electric load other than welding machine is two incandescent light bulbs. The welding shop operates for around 5–6 h on an average when solar irradiance is available. For the simulation, it is assumed that the one hour load profile is repeated for 7 h, i.e. from 8:30 am to 3:30 pm. The daily energy demand is around 3.0 kWh. The seasonal variations in load, irradiance, and temperature are neglected during simulation. As a result, the hybrid storage combination becomes only battery and supercapacitor storage. For this context, the load current rises rapidly from the base current. Both PV and supercapacitor are employed to supply this power. As the load is continuous, the supercapacitor SOC depletes. In order to avoid this, battery supplies a continuous

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Table 2 Comparison of two design points with manufacturer data and with battery degradation model for telecom tower Component

PV

With manufacturer life of battery

With battery degradation model

HESS

HESS

Battery-only system

Battery-only system

Size (kWp )

37

65

37

65

lifetime (years)

25

25

25

25

Size (kWh)

96

96

96

96

lifetime (years)

10

10

5.3

5.1

H2 storage

Size (m3 )

5.2



5.2



lifetime (years)

20



20



Fuel cell

Size (kW)

4.75



4.75



lifetime (years)

7



7



Size (kW)

1.55



1.55



lifetime (years)

10



10



0

0.81

0

2.37





3.78

3.87

34.21

27.2

36.41

VRLA battery

Electrolyser Yearly LOLP (%)

Battery degradation (%/year) COE (|/kWh)

DOE target cost of 25.2 H2 storage Actual cost of H2 storage

59.7

61.7

current that is ‘k’ times its nominal current. The ‘k’ is determined according to the battery and supercapacitor SOC, and it never exceeds twice the nominal current (to ensure minimum battery degradation). During the process, if supercapacitor is completely depleted, battery supplies the remaining load. In addition, PV charges both supercapacitor and battery when excess energy is available. The ideal storage size from sizing curve for PV, battery, and supercapacitor to cater to the second-level fluctuations are 1.2 kW, 2.23 kWh, and 5.16 Wh. The DC distribution side voltage is selected to be 24 V. A bi-directional DC-DC converter needs to be employed at the terminals of supercapacitor to keep the voltage fluctuations minimal. The simulation is done for a day with 7 h of welding load. The SOC distribution of battery and supercapacitor is shown in Fig. 4b. From the figure, it is clear that the load is met at all times in the required voltage range. The sharp fluctuations in the second level are met by supercapacitors. The base load and charging of supercapacitor at a constant rate (depending on the SOC of supercapacitor and battery) are taken care by batteries. PV also charges the supercapacitor as and when it is available. The SOC of the battery remains more or less constant when PV is available as seen in Fig. 4b. As a result, the battery degradation is nominal (0.008%/day), and hence, the p.u. cost (COE) of this system is about 13 |/kWh.

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Fig. 4 a Welding shop load and b SOC variation for battery and supercapacitor during welding Table 3 Comparison of two design points for welding context Component

With manufacturer life of battery

With battery degradation

HESS

Battery only system

HESS

Battery only system

Size (kWp )

1.2

4.2

1.2

4.2

Lifetime (years)

25

25

25

25

Size (kWh)

2.4

1.01

2.4

1.01

Lifetime (years)

6

6

6.68

less than half a year

Size (Wh)

5.4



5.4



Lifetime (years)

15



15



Daily LOLP (%)

0

0.488

0

0.488

Battery daily degradation (%/day)





0.0082

7.18

COE (|/kWh)

13.2

35.2

13

>85

PV

VRLA battery

Supercapacitor

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Table 3 compares two systems on the boundary curves: (i) hybrid storage and (ii) battery-alone system with manufacturer given life and by incorporating battery degradation. It can be clearly seen that supercapacitor reduces battery degradation and increases the battery life. Life cycle costing becomes inaccurate if battery degradation model is not considered in contexts like welding as presented in the case of battery-alone systems. Battery-alone systems will not be able to handle these type of fluctuations (7–8 times the base load) resulting in its deterioration in less than a year. Otherwise, the battery should be heavily oversized. This leads to high life cycle cost of the system. Thus, the best solution is to have a hybrid energy storage of battery and supercapacitor.

4 Conclusion This paper models and simulates PV-based microgrid with hybrid storage by incorporating the battery degradation. This helps in understanding the performance of hybrid storage in a microgrid system. The reliability and cost of energy is also evaluated and compared with conventional battery-alone system. Reliability of the system improves with the addition of a long-term storage to battery-alone systems. If the load is repetitively spiky, an addition of short-term storage improves the battery degradation. In addition, the battery degradation has direct correlation with life cycle cost of the system. Typically, while calculating the life cycle cost, the life cycle given by manufacturer is considered. The life of battery under actual operating conditions is different. By incorporating the capacity fade of battery into the system, a more realistic life cycle assessment is achieved. Two case studies—off-grid telecom tower and a welding shop—are simulated. For these contexts, we need to have hybrid storage to improve reliability and reduce battery degradation. As an example, the isolated welding shop with annual energy demand of 1408.5 kWh, the addition of supercapacitor improves the life of battery from 2 to 8 years, thereby improving life cycle cost of the system from 18 to 13 |/kWh. In addition, the daily LOLP is reduced from 7.2 to 0%.

References 1. Jacob, A.S., Banerjee, R., Ghosh, P.C.: Sizing of hybrid energy storage system for a PV based microgrid through design space approach. Appl. Energ. 212, 640–653 (2018) 2. Jacob, A.S., Banerjee, R., Ghosh, P.C.: Trade-off between end of life of battery and reliability in a photovoltaic system. J. Energy Storag. 30, 101565 (2020) 3. Villalva, M., Gazoli, J., Filho, E.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009) 4. PV Power Tech: ECO 225–250 W specification sheet. PV Power Tech (2018). [Online]. Available https://www.pvpowertech.com/60-cells-poly. Accessed 26 Apr 2019

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5. Ceraolo, M.: New dynamical models of lead-acid batteries. IEEE Trans. Power Syst. 15(4), 1184–1190 (2000) 6. Barsali, S., Ceraolo, M.: Dynamical models of lead-acid batteries: implementation issues. IEEE Trans. Energ. Convers. 17(1), 16–23 (2002) 7. Jackey, R.A.: A simple, effective lead-acid battery modeling process for electrical system component selection. SAE Paper, 01-0778 (2007) 8. Martel, F., Kelouwani, S., Dube, Y., Agbossou, K.: Optimal economy-based battery degradation management dynamics for fuel-cell plug-in hybrid electric vehicles. J. Power Sour. 274, 367– 381 (2015) 9. Exide Industries: Exide Gel Battery Technical Literature, pp. 1–53 (2014) 10. Maxwell Technologies Inc: Test procedures for capacitance, ESR, leakage current and selfdischarge characterizations of ultracapacitors (2015). [Online]. Available http://www.maxwell. com/images/documents/1007239-EN_test_procedures_technote.pdf. Accessed 26 Mar 2018 11. Maxwell Technologies Inc: Data sheet bc series ultracapacitor (2013). [Online]. Available http://www.maxwell.com/images/documents/bcseries_ds_1017105-4.pdf. Accessed 27 Mar 2018 12. O’Hare, R.P., Cha, S.-W., Colella, W., Prinz, F.B.: Fuel Cell Fundamentals, 3rd edn. Wiley, New Jersey, USA (2006) 13. Saeed, W., Warkozek, G.: Modeling and analysis of renewable PEM fuel cell system. Energ. Procedia 74, 87–101 (2015) 14. Kaldellis, J.K.: Optimum hybrid photovoltaic-based solution for remote telecommunication stations. Renew. Energ. 35(10), 2307–2315 (2010)

Simulation Studies on Design and Performance Evaluation of SAPV System for Domestic Application M. R. Dhivyashree, M. B. Gorawar , V. G. Balikai , and P. P. Revankar

1 Introduction The generation of the solar photovoltaic generation is intermittent and subject of constraints such as availability of sun, time of the day, season and the sky conditions. The study of behavior of such a system becomes an important aspect before commissioning the power plant to analyze the techno-economic parameters. The system performance can be predicted and analyzed by the computational tool such as PVsyst which facilitates user to determine the system performance at the given location for specific configuration. The said tool can be used to simulate stand-alone, grid-connected solar photovoltaic system, water pumping system as well as DC gridconnected system. The tools come with an intuitive user interface for selecting the system to be simulated and selection of balance of system components for photovoltaic system from the large database of commercial photovoltaic, battery, inverter and PCU manufactures. The reported research presents design and performance analysis of the off-grid stand-alone solar photovoltaic system to cater the energy demands of rural domestic application. Stand-alone photovoltaic system (SAPV) is independently operated energy generation system using solar energy. The system neither imports nor exports any energy from the grid as it is not connected to grid hence called stand-alone system. Major components of the SAPV system are charge controller (MPPT or PWM), inverter or PCU and battery which are commonly called as balance of the system connected to solar photovoltaic module. The solar modules are arranged in specific series and parallel combination to form array and string in the large-scale power generation plant. The commercial installation of solar photovoltaic power generation demands thorough economic and technical feasibility studies, prior to its on-site installation by PV system designer. The present study evaluates the solar power availability at location, and further, the system is designed through the PVsyst M. R. Dhivyashree · M. B. Gorawar · V. G. Balikai · P. P. Revankar (B) School of Mechanical Engineering, K.L.E. Technological University, Hubballi, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_44

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Fig. 1 Stages of simulation implementation

software on specifying the domestic power consumption as shown in Fig. 1. The power generation scheme designed through software is analyzed in context of the metrological data at the location of the installation.

2 Literature Review The design analysis of SPV system as reported by various researchers, academicians and system designers using simulation tools has proved to be reliable approach. Kandasamy et al. reported on grid-connected SPV system analyzed with PVsyst for its performance ratio and various power losses (temperature, internal network and power electronic) for 1 MW SPV system along with life cycle cost [1]. Suresh and Thomas reported PV system characterization for solar irradiance, temperature and wind speed. The system adopted non-uniform operating field losses that assessed 7468 Wh of energy demand on basis factors such as temperature, soiling, seasonality, partial shading, system voltage, losses and aging [2]. Yadav et al. reported on Hamirpur with annual solar radiation of 4.4 kWh/m2 -day having an installed 1 kWp SAPV system simulated in PVsyst that evaluated an annual performance ratio of SPV of 0.724 on an average basis [3]. Irwan et al. reported on exhaustion of conventional energy and its climatic impact. The design aspects and assessment of SPV system based on actual field trials on 1 kW off-grid PV system at, New Delhi, India were investigated in the study [4]. Srivastava and Giri highlight the importance of simulation software for SPV systems to predict the output power. Research elaborates the study carried on grid-connected SPV system comprising of 2000 PV modules of 250 Wp rating connected with the 50 kW grid-tied inverter. The simulation shows that 901.44 MWh can be generated in a year that can be fed to grid with 83.1% performance ratio [5]. Barua et al. evaluated grid-connected SPV system on basis of NASA metrological data. The simulation predicted SPV generation of 590 MWh equal to 11% of annual consumption of Pondicherry University [6]. Tapaskar et al. have explored various renewables to meet rural energy needs. The study preferred distributed generation to grid for load size lower than a breakeven point that justifies capital investment for grid line extension to remote location [7].

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3 Methodology The design of the stand-alone system for the domestic application was accomplished through three design steps.

DATA COLLECTION

DATA ANALYSIS

RECOMMENDATION

3.1 Data Collection This step involves collection of critical data required to determine energy requirements of domestic consumer. The geographical location is also assessed for its metrological data for predicting the power generation and performance analysis of designed system. The critical step has system designer interacting with consumer apart from site survey for suitable installation space at the consumer premises for shadow-free zone.

3.2 Data Analysis This was accomplished on software that makes assessment of metrological data at given location and system sizing computed through PVsyst. The user specifies power needs on hourly basis with system autonomy integrated to metrological data.

3.3 Recommendation Computational tool designs the SAPV system component sizing based on userspecified demands and metrological data at the site. The simulation gives detailed performance and system behavior with recommendations suggested to make suitable modifications in system parameters or user end to accomplish better performance.

4 Design of Stand-alone Solar Photovoltaic System The typical energy flow in SAPV system is shown in Fig. 2. The power generated from SPV modules is fed into charge controller for power regulation in order to optimally

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CHARGE CONTROLLER

BATTERY

INVERTER

LOAD

Fig. 2 Generalized block diagram of a typical SAPV system with AC loads

Fig. 3 Site details to be specified in the PVsyst

charge the battery for storage of power to be utilized in the non-sunny hours of the day. The light energy (photo) is converted into electric energy by SPV module and is stored in the battery as chemical energy. The electric energy stored in the battery is DC in nature which is used to cater the load demands. The DC electric power extracted from the battery is connected to power converter known as inverter which converts DC electric power into AC power to cater the AC loads; however, DC loads can be directly connected to the charge controller with state of charge (SOC)-based load cut of system for prevention of over-discharge of the storage battery system. The design of the SAPV system at site is dependent on the weather condition at the site specified by the user. The metrological data from the trusted and reliable source get imported into the software on specifying the geographical coordinates (latitude and longitude) of the location. The monthly metrological data at the site with horizontal global radiation, diffused radiation, ambient temperature and wind velocity can be seen represented in the tabulated format in the software interface. Figure 3 represents input window of interface to specify the geographical details.

4.1 Geographical Data and Solar Potential Assessment The metrological data imported into PVsyst from authenticated databases (such as NREL) are in two distinct time-based formats—one being the hourly data and other is monthly data format. Imported weather data include horizontal global radiation, diffuses radiation, ambient temperature and wind velocity at specified geographical coordinates. The present studies were based on test location situated in Vidynagar, Hubballi, India, as specified in the software as shown in Fig. 3. The sun path for the specified location is graphically represented in Fig. 4 at 15.37° N and 75.12° E.

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Fig. 4 Sun path for the location

4.2 Module Orientation and Hourly Generated Solar Radiation Data The orientation of SPV modules plays an important role in the optimal power generation that in turn depends on the geographical location index of the installation and day-of-the-year varying with season. The general practice of aligning the solar module is to tilt the panel equal to latitude angle of the given site—however, this is considered to be quick and approximate method. The precise angle of tilt can have a relation to declination angle (δ) determined by the Eq. (1) 

360 δ = 23.45 sin (284 + n)) 365

 (1)

that has n to represent the number for day-of-the-year (“n” is 25 for January 25th and 41 for February 10th). Seasonal variation and change in sun path at the given location result in different optimal tilt angles throughout the year. The tilt angle can be varied manually as per the season or a tracking device and can be implemented for continuous sun path tracking and automatic aligning of solar module to the sun direction. The simulation studies in the present research assume the fixed type of solar module tilted at an angle of 16° as shown in Fig. 5. The constraints imposed by economic feasibility and maintenance considerations compelled to choose a fixed type of solar module installation for simulation purpose; later case of tracking would incur additional energy and cost factor for tracking mechanism.

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Fig. 5 Simulationgenerated data

Monthly solar irradiation data consisting of horizontal global irradiation, horizontal diffuse irradiation, temperature, etc., are shown in Fig. 6; these data are the site-specific and are imported from the Internet databases.

Fig. 6 Orientation of SPV modules

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4.3 Load Data of the Simulated Installation Site The user interface of the PVsyst allows the user to specify the load, its power consumption and usage hours per day for determining total daily energy needs of the user. The daily energy needs are further used by the software to size the storage battery bank and calculate the solar module required to cater the user needs and charge the battery bank. The solar module and battery bank sizing are critically dependent on the load distribution during the day—for example, if major loads are run at the sunshine hours, the size of battery bank would be reduced and vice versa. Figures 7 and 8 show the loads with their power ratings along with the hours of usage during the day. Domestic energy demand peaking at evening time and detailed hourly distribution of the load respectively can be noticed in the graphical display.

Fig. 7 Load data used for the simulation study

Fig. 8 Graphical display of defined hourly load usage at rural household

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Table 1 Module and battery specifications for simulated SAPV system PV module specifications

Battery specifications

Module/Manufacturer

Generic

Nominal power

75 Wp

Voltage

12 V

Technology

Polycrystalline silicon

Battery capacity

103 Ah

Short-circuit current

4.4 A

Permissible DOD

80%

Open-circuit voltage

16.9 V

Technology

Lead–acid/tubular

PWM charge controller 12 V, 10 A

Manufacturer

Exide

Inverter 12 V, 800 VA Pure Sine Wave

4.4 Configuration of the Proposed SAPV System The use of system components is decided by cost and availability of the specific components. The large database of the PVsyst allows user to select the desired component from the specific manufacturer supplying at the site of installation. The components chosen for the present simulation are presented in Table 1. It is very important to select the best components in the system to achieve costefficient and reliable system

4.5 PV Array Sizing The array is a stack of SPV modules arranged to generate required voltage and current to cater to loads and battery charging. The DC power from PV array must account to losses in later power storage stage and conversions like losses in charge controller, battery and inverter. These losses add up to power demands and determine rating and PV module capacity in array. The component losses are dependent on type and variant of component used—for MPPT charge controller, efficiency is in range of 90–95% and PWM charge controller has value from 70 to 85%. The battery efficiency ranges from 80 to 85%, while inverter efficiency is 70–80% for square wave and 85–95% for pure sine wave. Size of PV Array =

Daily consumption of the Energy (Sunshine hours at the location) × (Operating hours of PV modules)

(2)

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4.6 Battery Sizing Battery is the major component of the SAPV system with type and capacity decided by capital investment and consumer load demand, respectively. The batteries such as lithium-ion are the best for power storage as they are lightweight and efficient but the cost of these batteries would pose the budgetary inflations on total installation cost of the system. Sealed Maintenance-Free (SMF) batteries which are spill proof can also be used which are cheaper than lithium-ion batteries but costlier than lead– acid batteries. Commonly, tubular lead–acid batteries of c10 rating typically called as “tubular solar lead–acid battery” are used in the domestic solar installations as they have higher depth of discharge (DOD) and high current charge and discharge capability. These batteries if properly maintained can have life of 8–12 years of life with 1500 cycles @ 80% DOD, 3000 cycles @ 50% DOD and 5000 cycles @ 20% DOD. Battery capacity of the SAPV system is determined by the equation below Battery Bank Capacity =

(Average Daily Consumption) × (Autonomy in days) (System Voltage) × (Depth of Discharge)

where autonomy can be defined as the reserve power stored in the battery in terms of number of days to cater the load for non-sunny days.

4.7 Charge Controllers These are used to regulate the power from the PV array and charge the storage batteries. Two variants of charge controller are used in the stand-alone solar photovoltaic system—pulse width modulation (PWM)-based charge controller and MPPTbased charge controller. Low-powered SPV systems are designed with PWM-based charge controller to keep the system cost minimal. The high-power SPV systems typically above 1 kW are designed with maximum power point transfer (MPPT)-based charge controllers to extract maximum power out of PV module. These MPPT-based charge controllers are costlier compared to the PWM-based controllers but also have higher conversion efficiencies up to 95%. System designer has to make suitable trade-off between the choice of charge controller based on the system ratings and installation costs.

4.8 Inverter Home appliances use AC power for their operation—Indian appliances are manufactured to operate at AC electric power at 230 V and 50 Hz. Inverter converts DC

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power stored in the battery bank into AC power to cater the requirement of AC loads. Inverters can also be classified into square wave and sine wave inverters depending on the output waveform of the AC electric power. The square wave inverters are cheaper compared to sine wave inverters, but they produce square wave which is considered harmful for smooth operation and life of the appliance used. AC appliances running on the square inverter produce humming sound and exhibit inefficient operating behavior. However, sine wave inverters are costlier, but the output of the inverter is approximately similar to the gird power which is considered safe, smooth and efficient for the operating AC appliances. Modern-day solar inverters have inbuilt charge controllers featuring both PWM and MPPT variants which can be directly connected to solar modules.

5 Results and Discussions This section of the research article gives insight into the technical interpretations drawn on basis of the post-processed results furnished by the simulation tool. Figures 9, 10, 11, and 12 give the information of solar radiation data at specified location, signifying the solar potential available at the site of investigation. Similarly, Figs. 13, 14, 15, 16, 17, and 18 emphasize on the operational aspects of a particular design of solar photovoltaic system installed at the location. The simulation results of SAPV model designed in PVsyst at the selected geographical location have been discussed in the following section. The average power available at the given location is 5.37 kW, and the system is generating an average of 128.4 kWh per year. The system is designed from two-day autonomy and five percent allowable

Fig. 9 Array power generation

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Fig. 10 Daily array output energy

Fig. 11 Incident irradiation distribution

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Fig. 12 Incident irradiation tall distribution

Fig. 13 Normalized production of 75 Wp

loss of load which can be significantly seen in the months of June, July and August of the year.

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Fig. 14 Normalized production and loss factor

Fig. 15 Performance ration and solar fraction

5.1 Financial Aspects of SAPV System The feasibility of any engineering project is dictated by the economic parameters. The implementation of SAPV for power generation through roof-top installations has been assessed through inbuilt programming tools in PVsyst. Figure 19 presents

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Fig. 16 State of charge daily distribution

Fig. 17 Average SOC of the battery throughout year

the user interface for communicating the financial aspects of the project in terms of the cash inflows and outflows.

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Fig. 18 Available solar energy

Fig. 19 Financing aspects of the designed SAPV system in PVsyst

5.2 Sustainability Issues of the SAPV System-CO2 Mitigation Assessment PVsyst has carbon balance tool that allows the estimated savings of CO2 emissions out of designed SAPV system. Life cycle emissions (LCE) represent the emissions of CO2 connected to the respective system component or energy amount including total life cycle of a component, operation factors, production and disposal, etc. Total carbon balance of SAPV system is the difference between produced and saved CO2 emissions as shown in Figs. 20 (Fig. 21). Where E grid: energy yield of the SAPV system throughout the year System lifetime: system life in years (30 years) Grid LCE: average amount of CO2 emissions per unit of energy produced by the grid SAPV LCE: Amount of CO2 emissions released during commissioning and operation of the SAPV system.

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Fig. 20 Overview of the project carbon emission saving

Fig. 21 Variation of tCO2 with year

6 Conclusions The present article elaborates simulation and design of SAPV system for a Hubballi location in India. The computational tool has facilitated the optimal design of the system considering the user demands along with the metrological data of the site under consideration. The output of the SAPV was predicted to study the long-term behavior of the designed system. The SAPV system was designed to cater the load of rural domestic needs accounting to a daily energy need of 355 Wh. The optimal system was proposed on the design with SPV module of 75 Wp, charge controller of 12 V 10 A and lead–acid battery of 103 Ah. The average SOC of the battery throughout the year was 84.1% with 5% loss of load. The critical operation period during the month of June, July and August of the year was marked out for taking preventive measures through allied conventional power or grid imports. The CO2 mitigation of the system was also calculated and found to be 1.83 tonne during the 30 year lifetime for the SAPV system. The total capital investment on the system was assessed to be around Rs. 0.22 lakh. The system as per the design and prediction will serve the purpose of catering the energy demands of rural domestic application. The rate of return on investment for stand-alone SPV system ranges from 16 to 18% with a payback period for investment between 6.25 and 5.5 years. The societal impact of this research was to enhance living standards in rural India by means of a sustainable energy solution.

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References 1. Kandasamy, C.P., Prabu, P., Niruba, K.: Solar potential assessment using PVsyst software. 667– 672 (2013). https://doi.org/10.1109/icgce.2013.6823519 2. Suresh, P., Thomas, J.: Performance analysis of stand-alone PV systems under non-uniform operating conditions using PVsyst. Adv. Res. Electr. Electron. Eng. 1(4), 19–25 (2014). Print ISSN 2349-5804; Online ISSN 2349-5812 3. Yadav, P., Kumar, N., Chandel, S.S.: Simulation and performance analysis of a 1 kWp photovoltaic system using PVsyst. Comput. Power Energ. Inform. Commun. (ICCPEIC) 0358–0363 (2015) 4. Irwana, Y.M., Ameliaa, A.R., Irwantoa, M., Ma, F., Leowa, W.Z., Gomesha, N., Safwati, I.: Stand-alone photovoltaic (SAPV) system assessment using PVsyst software. In: International Conference on Alternative Energy in Developing Countries and Emerging Economies (2015) 5. Srivastava, R., Giri, V.K.: Design of grid connected PV system using PVsyst. Afr. J. Basic Appl. Sci. 9(2), 92–96 (2017). ISSN 2079-2034 6. Barua, S., Hossain, M.S., Mahmud, M.S., Rahman, M.W.: A feasibility study of low voltage DC distribution system for LED lighting in building. In: Innovations in Power and Advanced Computing Technologies (i-PACT) (2017) 7. Tapaskar, R.P., Revankar, P.P., Ganachari, S.V., Yaradoddi, J.S.: Biomass energy and bio-solar hybrid energy systems. In: Martínez, L., Kharissova, O., Kharisov, B. (eds.) Handbook of Ecomaterials. Springer Publication (2018)

Development of a Dynamic Battery Model and Estimation of Equivalent Electrical Circuit Parameters Sourish Ganguly, Subhrasish Pal, and Ankur Bhattacharjee

1 Introduction Battery modeling is the key to various battery storage system designs, especially in areas of renewable energy storage. Renewable technologies, such as solar or wind, do not produce a prolonged power output; and hence, electrical energy storage from a non-conventional energy source becomes a mandatory requirement. Proper design of an efficient battery model is a primary factor in the effective utilization of the power source, such as a solar panel. The model, thus obtained, is vital in the testing of various charge controller algorithms, required for the design of systems, such as electric-vehicles (EV). The fundamental challenge with the analysis of an electrical equivalent battery model is that the parameters change significantly throughout charge or discharge. Lack of constancy in the model parameters becomes an engineering challenge for the proper sizing of a battery for its applications. Since the dynamic characteristics of a battery [1, 2] vary with its state of charge (SOC) [3], proper estimation of the model parameters becomes an absolute necessity. Working with an equivalent electrical model simplifies the analysis of electrical characteristics of the battery with high precision. From literature, various concepts on battery modeling are observed [4–10]. Study has also shown that the values of the electrical parameters tend to change over time, cycle use and temperature. The main objective of this paper is to develop a robust model which considers all the above behavioral changes of the electrical parameters of different types of batteries by estimating their parameters with the help of measured S. Ganguly International Institute of Information Technology, Hyderabad, Telangana, India S. Pal Institute of Engineering and Management, Kolkata, India A. Bhattacharjee (B) Birla Institute of Technology and Science, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_45

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charging and discharging data, at dynamic conditions of the batteries. This is critical for high efficiency in algorithms involving charge controllers. The novelty of the procedure is that the model is not affected by the chemistry of the battery, which is demonstrated in this paper. The parameter estimation has been carried out in MATLAB through an iterative process to ultimately minimize the error between the experimental plot of battery terminal voltage and the simulated plot of the same. The final values of the estimated battery parameters can then be utilized as polynomial functions of the state of charge. The rest of the paper consists of the following sections: Sect. 2 consists of the overall description of the equivalent electrical circuit used in the battery model, Sect. 2 contains the description of the model used for the parameter estimation process, Sect. 4 includes the estimation process and its results, and Sect. 5 consists of the conclusion of the paper.

2 Overall System Description Literature shows various models of a battery, based on battery chemistry, such as the Rint model, Thevenin’s model, RC model, PNGV model, etc. [1]. In the following estimation process, the one time constant (OTC) model has been employed. As seen from Fig. 1a, the equivalent electrical model is able to describe the transient battery characteristics involving time constants, which was not possible in the Rint model. The model’s internal impedance parameters are represented by Randles’ equivalent circuit model [2] which consists of an open-circuit voltage source E oc , active electrolyte resistance, or the solution resistance R0 and a parallel combination of a double-layered capacitance C 1 and a charge transfer resistance, or the polarization resistance R1 . R1 describes the transfer of charge at interface of the electrode and the electrolyte, during charging and discharging [4]. Since all the parameters of the battery, namely E oc , R0 , R1 and C 1 , are dynamic in nature and are functions of the

Fig. 1 a Battery equivalent circuit based on OTC model. b Thevenin’s model of battery equivalent circuit

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SOC, they are described by dynamic equations. The parameters keep changing with the electrochemical processes in the battery. The equations pertaining to the model are given as follow: E t = E oc − Z in Ibat

(1)

where E t and E oc are the terminal voltage and open-circuit voltages of the battery, respectively; while I bat is the current through the battery which is assumed positive during discharge and negative during charging. Z in is the equivalent internal impedance comprising of R0 , R1 and C 1 . The internal impedance of the battery constituted by R0 , R1 and C 1 can be estimated by the loop equation given in (2): Z in =

E t − E oc ±I

(2)

where E t is the terminal voltage of the battery and E oc is the open-circuit voltage of the battery. The + and − sign of current ‘I’ denotes charging and discharging, respectively. The open-circuit voltage which is a dynamic quantity depending on the SOC of the battery can be estimated by using the well-known Nernst equation as a function of SOC (3) at a temperature T and self-discharge potential drop.  E oc = n ×

E (at 50% SOC) +

  SOC 2RT ln F 1 − SOC

(3)

where n is given by the number of cells connected in series, from the work of Bhattacharjee et al. [4]. E oc = open-circuit voltage of the battery, R = 8.314 J mol−1 K−1 , T is the working temperature (in K), F = 96,485 C mol−1 , E (at 50% SOC) is the open-circuit voltage at 50% SOC. Figure 2 shows the variation of the open-circuit voltage of a 30 V (20 cells in series) vanadium redox flow (VRF) battery with SOC while charging. The parameter estimation involves accurately modeling the variation of the internal impedance of the battery with changing battery capacity. From Fig. 1, the circuit is reduced to an equivalent Thevenin’s circuit, as shown in Fig. 1b, after applying Laplace transformation. Thevenin’s impedance of the electrical equivalent circuit Z in (s) is evaluated, as shown in (4). The internal impedance obtained is a function of the SOC. Each of the estimated dynamic parameters can be obtained as polynomial functions of SOC, and hence, analysis of the internal impedance of the battery can be carried out.

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Fig. 2 Variation of E oc with SOC for 20 cell VRFB

 Z in (s) = R0 +  = R0 +

R1 × R1 +

1 sC1 1 sC1



R1 R1 C 1 s + 1

 (4)

R0 , R1 and C 1 are functions of SOC.

3 Model Description 3.1 Proposed Battery Estimation Model and Its Components The simulation model designed in MATLAB, as given in Fig. 3a, shows a battery block with a controlled current source, which acts as the source (or sink, depending on the direction of current). A voltage sensor measures the simulated output terminal voltage of the battery. The input (the current source) and the output (V out ) in the figure are loaded with the experimental data, which is then utilized in the parameter estimation process to optimize the simulated response of the system. Randles’ model of a battery is modeled as shown in Fig. 3b and consists of an opencircuit voltage source (E oc ) in series with a resistor (R0 ) and parallel combination of a resistor (R1 ) and a capacitor (C 1 ). Each of these parameters mentioned above is tabulated in look-up tables (LUTs), with the horizontal axis consisting of the SOC (ranged from 0 to 1) as shown in Fig. 4b, c. Except E oc , which is tabulated from the Nernst Eq. (3), the rest of the parameters which are required for the estimation are observed to be dissimilar for charging and discharging. Hence, for each of the parameters (except E oc ), two separate LUTs are employed, one for charging and the

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Fig. 3 a Simulation model for estimating dynamic parameters, b the battery equivalent circuit in MATLAB with E oc as the open-circuit voltage and resistor R0 and parallel RC branch consisting of R1 and C 1

Fig. 4 The parameter estimation model with a open-circuit voltage (E oc ), b resistance (R0 ), and the parallel RC branch (R1 and C 1 ) referred to their respective LUTs. c SOC calculation block of the simulation model. d Subsystem of the SOC estimator block

other for discharging. The following figures show the various parts of the proposed model developed for carrying out the estimation process. Figure 3b shows the overall model of the equivalent circuit employed for the battery. Figure 4 shows the various components (subsystems) of the overall model as shown in Fig. 3b. Figure 4a shows the open-circuit voltage block (E oc ), along with the SOC calculating block. Figure 4b shows the subsystem of the RC branch block, and Figure 4c shows the subsystem of the series resistance (R0 ) block.

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3.2 Calculation of SOC The SOC calculation block is designed on the principle of the Coulomb counting (CC) method. Current flowing through the battery during charging is integrated with time and is added to initial capacity. Depending on charging or discharging, the SOC would increase or decrease, respectively (6). In the simulation model’s state of charge calculation block, shown in Fig. 4d, the block takes the instantaneous value of current from the current sensor and evaluates the SOC as described by Eqs. (5) and (6). The state of charge is found out by dividing the present capacity of the battery by the total capacity of the battery. Mathematically, the SOC of the battery is given as SOC =

q(t) qmax

(5)

where q(t) = capacity of the battery as a function of time t and qmax = maximum capacity of the battery. q(t) is calculated as: t q(t) = q0 +

i(t)dt

(6)

o

4 Estimation and Results 4.1 Estimation Process The estimation has been carried out through Simulink parameter estimation tool. The optimization has been carried out using trust-region-reflective algorithm, which minimizes the sum-squared error of the simulated response by iteratively tuning each parameter of the equivalent circuit model. As mentioned above, the parameters were initialized as constant values for all states of charge and were tabulated in LUTs before the beginning of the estimation process. The iteration optimizes the response by updating the model parameter values for C 1 , R1 and R0 after each iteration in their respective LUTs, resulting in dynamic values of parameters at each SOC.

4.2 Results Obtained Figure 5a shows the measured output of a 30 V, 7.2 kWh VRF battery at 40 A constant current charge at 27 °C, and the initial simulated response of the battery model with

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Fig. 5 a Measured and simulated response of the battery terminal voltage (V out ) before estimation is started (with constant parameters for every SOC) (with a constant charging current of 40 A at 27 °C), b simulated and measured terminal voltage (V out ) during 40 A constant current charging of 30 V, 7.2 kWh VRF battery, c simulated and measured terminal voltage (V out ) during 40 A constant current discharging of 30 V, 7.2 kWh VRF battery with tuned dynamic parameters

40 A constant current input and temperature 27 °C. As evident from the figure, a significant error between simulated and measured output voltage is observed. This is due to an erroneous assumption where all battery parameters are employed fixed values. A similar comparison has been carried out after completion of the estimation process, as shown in Fig. 5b. Simulation Response Figure 5 shows the simulated response compared with the experimentally obtained output voltage of 30 V, 7.2 kWh VRF battery after completion of parameter estimation. Similarly, a process to match the output voltage of the battery model with 40 A constant current discharge with that of the experimental battery (30 V, 7.2 kWh VRFB) is carried out, as shown in Fig. 5c. The estimation process is also carried out in an identical manner for a 12 V, 3.6 kWh lithium-ion battery with charging/discharging at 50 A constant current. The performance of the model in terms of sum-squared error is shown in Table 1. Estimated Parameters The parameters of the estimated model are plotted versus SOC, from the updated LUTs of the tuned battery model. Since the variation of the parameters with SOC is usually different for cases of charging and discharging, separate plots are made for both the cases. The following figures, Fig. 6a–f, show the plots of the estimated dynamic electrical parameters (R0 , R1 and C 1 ) of the 30 V VRF battery during charging and discharging respectively.

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Table 1 Performance of the estimation process on the battery model S. No.

Battery type

Sum-squared error between of the simulated response of the battery model (in V) During charging

During discharging

Before estimation

After estimation

Before estimation

After estimation

1

30 V, 7.2 kWh VRF battery

1.28

6.19 × 10−4

3.66

6.32 × 10−4

2

12 V, 3.6 k Wh Li-ion battery

1.21

5.51 × 10−4

2.91

8.18 × 10−4

Since the estimation is also carried out with a 12 V, 3.6 kWh lithium-ion battery [11], the following figures, Fig. 7a–f, show the plots of the estimated dynamic electrical parameters (R0 , R1 and C 1 ) of the 12 V lithium-ion battery during charging and discharging, respectively. The parameters obtained after estimation are plugged into the battery model to obtain the optimized response of the system.

5 Conclusion The proposed model performance is validated by the experimental data as input to the model. The internal circuit parameters are found to be dynamic with SOC while charge and discharging. Even though the parameters have been estimated using constant current of certain values for charging and discharging, the model can be used with dynamic values of charging or discharging current in larger systems. It is observed from the plots of the estimated parameters (Figs. 6 and 7) that the components of the equivalent impedance of the battery follow a certain trend: During charging, the solution resistance (R0 ) has an overall decreasing nature with respect to increasing SOC; and an overall increasing nature is observed with respect to increasing SOC during discharging. This trend is observed to be similar with different values for both types of batteries employed in the experiment. The variation of doublelayered capacitance C 1 with SOC is observed to be identical for both charging and discharging processes with very similar curves for both the VRF battery and the Li-ion battery. The capacitance values are observed to be very high in magnitude, which is due to the double-layered phenomenon in the electrochemical cells. As the dimensions of the electrodes and the cell remains constant, the only variable, E (permittivity of the electrolyte), should be very high due to high energy density near the electrodes. From Table 1, the sum-squared errors of the responses of the model are shown, which are observed to be in the orders of 10−4 volts. The postestimation response of the simulation model is shown in Fig. 5b, c, which accurately predicts the behavior of the battery at different SOC. Future work would include curve fitting analysis to obtain polynomial functions for all parameters obtained

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Fig. 6 Dynamic nature of parameters: a active electrolyte resistance R0 , b charge transfer resistance R1 and c double-layered capacitance C 1 —during charging for a 30 V, 7.2 kWh VRF battery (at 40 A constant current charge at 27 °C); d active electrolyte resistance R0 , e charge transfer resistance R1 and f double-layered capacitance C 1 —during discharging for 30 V, 7.2 kWh VRF battery (at 40 A constant discharge, 27 °C)

through estimation, which can be used in charge controllers, such as systems having solar PV application, to achieve accurate maximum power point tracking (MPPT) utilizing the dynamic values of impedance of the battery. Implementation of more accurate models with higher number of time constants can be implemented as a continuation of work on the current proposed system. The work has been carried,

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Fig. 7 Dynamic nature of parameters: a active electrolyte resistance R0 , b charge transfer resistance R1 , and c double-layered capacitance C 1 —during charging for 12 V, 3.6 kWh Li-ion battery (at 50 A constant charging, 27 °C); d active electrolyte resistance R0 , e charge transfer resistance R1 , and f double-layered capacitance C 1 —during discharging for 12 V, 3.6 kWh Li-ion battery (at 50 A constant current discharge, 27 °C)

assuming constancy in temperature throughout the charge/discharge cycles. Physical factors, such as variation of temperature, leakage losses and aging, play great roles in battery performance and can be taken into consideration for analysis to ensure better prediction of the battery storage performance in practical power systems.

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References 1. Zhang, X., Zhang, W., Lei, G.: A review of Li-ion battery equivalent circuit models. Trans. Electr. Electron. Mater. 17(6), 311–316 (2016) 2. Randles, J.E.B.: Kinetics of rapid electrode reactions. Discussions of the Faraday Society (1947) 3. Pop, V., Bergveld, H.J., Danilov, D., Regtien, P.P.L., Notten, P.H.L.: Battery Management Systems Accurate State-of-Charge Indication for Battery-Powered Applications, vol. 9. Springer, The Netherlands 4. Bhattacharjee, A., Roy, A., Banerjee, N., Patra, S., Saha, H.: Precision dynamic equivalent circuit model of a Vanadium Redox Flow battery and determination of circuit parameters for its optimal performance in renewable energy applications. J. Power Sour. 396, 506–518 (2018) 5. Berrueta, A., Martin, I.S., Sanchis, P., Ursúa, A.: Comparison of state-of-charge estimation methods for stationary Lithium-ion batteries. In: Presented at the Conference 42nd Annual of the IEEE Industrial Electronics Society (IECON), Florence, Italy, 24–27 October 2016 6. Ye, Y., Shia, Y., Cai, N., Lee, J., He, X.: Electro-thermal modeling and experimental validation for Lithium ion battery. J. Power Sour. 199, 227–238 (2012) 7. Saha, B., Goebel, K.: Modeling Li-ion battery capacity depletion in a particle filtering framework. In: Annual Conference of the Prognostics and Health Management Society (2009) 8. Gao, L., Liu, S., Dougal, R.A.: Dynamic Lithium-ion battery model for system simulation. IEEE Trans. Compon. Packag. Technol. 25(3) (2002) 9. Omar, N., Widanage, D., Monem, M.A., Firouz, Y., Hegazy, O., den Bossche, P.V., Coosemans, T., Mierlo, J.V.: Optimization of an advanced battery model parameter minimization tool and development of a novel electrical model for Lithium-ion batteries. Int. Trans. Electr. Energ. Syst. 24, 1747–1767 (2014) 10. Rahmoun, A., Biechl, H.: Modelling of Li-ion batteries using equivalent circuit diagrams. Przegl˛ad Elektrotechniczny (Electr. Rev.) 88, 152–156 (2012) 11. Yao, L.W., Aziz, J.A., Kong, P.Y., Idris, N.R.N.: Modelling of Lithium-ion battery using MATLAB/Simulink. In: 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013) (2013)

A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter P. Sriramalakshmi

and Sreedevi V. T.

1 Introduction The voltage source inverter finds wide range of applications in stand-alone and gridconnected renewable energy systems (RES) [1, 2]. The voltage obtained from the RES needs to be stepped up owing to the low output voltage. It is necessary to connect an additional DC-DC boost converter to obtain a high AC voltage. Thus, it results in two stages of power conversion which increases the price and affects the performance of the inverter [3]. In the conventional inverter, both the devices in a single phase cannot be turned on and shoot-through is not permitted. Moreover, the shoot-through can cause short circuit in the DC supply. The drawbacks of conventional inverters are overcome by the single stage Z source inverters (ZSI) proposed in the literature [3]. It consists of an impedance network which includes two inductors and two capacitors. In this topology, both upper and lower switches in a single phase can be fired at the same time. Also, the shoot-through can boost the input voltage with high reliability. But the traditional ZSI has limitations on providing boost voltage where a low-level voltage needs to be inverted into a higher AC voltage. In recent years, many researchers are involved in bringing out novel topologies on ZSI networks, new pulse width modulation (PWM) strategies, implementation of modeling and control techniques. To avoid the inrush current, quasi-ZSI (qZSI) is proposed in [4]. Various qZSI derived topologies are discussed in [4]. For high boost applications, switched inductor-based qZSI [5–8] is used. The trans-ZSI, transformer assisted ZSI are proposed to get a high boost voltage with reduced capacitor count [9–12]. An improved trans-ZSI [13] is proposed to offer continuous input current along with boost inversion. However, large size of P. Sriramalakshmi (B) · Sreedevi V. T. Vellore Institute of Technology, Chennai Campus, Chennai, India e-mail: [email protected] Sreedevi V. T. e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_46

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inductors and diodes used in ZSI and its variants increase the size and cost of the entire system. Whenever the transformer is used in the configurations, the effect of leakage inductance needs to be handled very carefully. It results in high voltage spike in the inverter input voltage which reduces the efficiency of the converter system. The switched-boost inverter and quasi-switched-boost inverters (SBIs/qSBIs) are introduced in [14–20] which can overcome the drawbacks associated with the ZSI and qZSIs. They use reduced count of passive elements but increased count of active devices than the ZS/qZSIs. The SBI derived topologies such as CFSI which has the very same characteristics like ZSI [21], trans-qSBI [22] are suggested in which the voltage stress on the boost network components such as capacitor, diode and switch is same as that of the DC-link voltage. The SBI with four switches is introduced in [23]. The half bridge SBI with low capacitor voltage stress is proposed in [24]. The T-type-qSBI is suggested in [25]. The half bridge and full bridge SBI with continuous input current are presented in the literature. The higher voltage gain can be obtained either by paralleling various topologies or by using switched inductor (SL) cell, switched capacitor (SC) cell. Usually SL-based qSBI is used with voltage fed inverter [26, 27] and SC-based qSBI is used with current fed qSBI [28, 29]. In this paper, a SL cell and SC cell are combined together to form the boost network and to boost the overall voltage gain of the inverter topology. A novel SL-SC-based qSBI topology is designed and analyzed in this paper. The inverter is designed with a low DC input voltage of 32 V to get the DC-link voltage of 205 V and an inverted AC voltage of 113 V (rms). It is implemented in MATLAB/Simulink environment for further analysis and the simulation results are discussed in detail.

2 Single-Phase SL-SC qSBI Topology Figure 1 depicts the single-phase SL-SC-based qSBI topology which is the improved configuration of a single-phase SC-based qSBI topology of type-1 presented in [28].

Fig. 1 Single-phase SL-SC-based qSBI topology

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Fig. 2 a Shoot-through state. b Non-shoot-through state

In type-1 of the SL-SC qSBI, the negative terminal of the inverter bridge is connected to the negative terminal of the DC source as shown in Fig. 1. It has a DC source (Vin ), a boost network cascaded with a VSI. The boost network includes a SL cell and SC cell. The SL cell consists of a pair of inductors (L 1 , L 2 ) and three diodes (D4 , D5 , D6 ) and the SC cell consists of two capacitors (C1 , C2 ), three diodes (D1 , D2 , D3 ) and one active switch (S0 ).

3 Operating Principle of the Proposed Topology The proposed topology has two states of operation as in classical SBI and qSBI topologies. One is shoot-through state and the other is non-shoot-through state.

3.1 Shoot-Through State During shoot-through mode as depicted in Fig. 2a, both the inductors L 1 , L 2 , capacitor C1 are charged together and C2 is discharged. Diodes D2 , D4 , D6 are conducting and D1 , D3 , D5 are non-conducting. During this state, both the switches (S1 , S4 ) or (S2 , S3 ) in the inverter bridge are on along with the boost network switch S0 . The DC-link voltage (VPN ) is zero; hence, the inverter bridge is shorted. There is no voltage across the load during this mode of operation.

3.2 Non-Shoot-Through State During non-shoot-through state as given in Fig. 2b, the inductors L 1 , L 2 , the capacitor C1 are discharged and the capacitor C2 is charged. Diodes D1 , D3 , D5 are conducting and D2 , D4 , D6 are non-conducting. The boost network active switch S0 is off. This state is as that of the active state of the classical inverter. In this state, inverted voltage appears across the load.

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4 Steady-State Analysis of SL-SC Topology During shoot-through state, the inductor voltages (VL 1 , VL 2 ) are given by, VL 1 = VL 2 = Vin = VC2

(1)

The capacitor voltage is related as, VC1 = VC2

(2)

The DC-link voltage across the inverter bridge is obtained as, VPN = 0

(3)

The capacitor currents are given by, IC 1 = IC 2 − I L 1 − I L 2

(4)

IC2 = IC1 + Iin

(5)

During non-shoot-through state, VL 1 = Vin − VC2 − VL 2(NST) VL 2(NST) = L 2

dI L 2 dt

(6) (7)

IC2 = Iin − IPN

(8)

IC1 = −IPN

(9)

VPN = VC1 + VC2

(10)

  Applying volt second balance to inductor voltage VL 2 , (1 − D)VL 2(NST) + D(Vin + VC2 ) = 0 VC2 =

1+ D Vin 1 − 3D

Average voltage across the capacitors,

(11) (12)

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VC1 = VC2 =

1+ D Vin 1 − 3D

497

(13)

Peak DC-link voltage is obtained as,  VPN = VC1 + VC2 =

 1+ D 1+ D + V in 1 − 3D 1 − 3D

(14)

Peak DC-link current is obtained as, IPN = Boost factor B is given by, B =

(1 − D) VPN RL

(15)

2(1 + D) . (1 − 3D)

(16)

VPN Vin

B=

5 Passive Components Design Like single-phase conventional topologies, the proposed inverter too produces the low-frequency ripple content at the DC side of the topology. The design equations for ripple inductor currents are given in Eqs. (17) and (18) and only the high frequency ripples are taken for consideration. The low-frequency inductor current ripples are eliminated by adopting a suitable feedback control. The ripple content of the inductor currents can be given by, I L 1 =

Vin + VC2 DT L1

(17)

I L 2 =

Vin + VC2 DT L2

(18)

where T is the operating frequency of the inductors. The voltage ripple across the capacitors are given by, VC1 =

(I L 1 + I L 2 ) − IPN (1 − D)T C1

(19)

IPN (1 − D)T C2

(20)

VC2 =

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As presented in [28], the proposed topology can be extended to n-cell topology. By adding more number of SC cells along with SL structure, the inverter topology can be extended to multi cell SL-SC-based qSBI topology.

6 Modulation Strategy for the Proposed SL-SC qSBI Topology The principle of operation of the single-phase SL-SC qSBI inverter topology is validated using the simple boost PWM technique [3]. The single-phase peak AC voltage (Vacpk ) can be obtained by, Vacpk = MVPN = MBVin

(21)

where M is the modulation index and B is the boost factor. Gain G = MB =

Vacpk Vin

(22)

With the following limitation given in (23), the shoot-through state is inserted into the traditional zero states itself. D+M ≤1

(23)

7 Simulation Studies The simulation model of 200 W single-phase SL-SC-based qSBI is designed to verify the operating modes and steady-state analysis. The MATLAB/Simulink software is used to validate the proposed inverter. The design values of the inverter topology are inductance L = 1 mH, capacitance C = 470 µF, filter inductance L f = 1 mH, filter capacitance C f = 4.7 µF, load resistance RL = 50 . The switching frequency f s of H-bridge switches and boost network switch (S0 − S5 ) is considered as 20 kHz. The input voltage (Vin ) is taken as 32 V and the output voltage is 113 V (rms) at 50 Hz. The modulation index M and duty ratio D are considered as 0.78 and 0.22, respectively. The simulated voltage waveforms of diodes in the switched capacitor structure (SC) and voltage stress across the switch S0 are shown in Fig. 3a. It is obvious that the voltage stress across the diodes D1 , D2 , D3 is 205 V, 100 V and 100 V, respectively. The voltage stress across the switch S0 in the SC-based network is obtained as 100 V. It is obvious that the voltage stress across the boost network is very low compared to that of other conventional qSBI topologies. Figure 3b depicts the simulated capacitor voltage waveforms VC1 , VC2 of 103 V each.

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Fig. 3 a Diode voltages waveforms and switch stress. b Capacitor voltages, DC-link network and current and DC-link voltage. c Simulated waveforms of inductor and source current waveform. d Simulated waveforms of filtered load voltage and load current

The DC-link current of about 10 A is also shown. The DC-link voltage (VPN ) is obtained as 206 V which is shown in Fig. 3b. The current through the inductors L 1 , L 2 in the inductor cell is shown in Fig. 3c as 5 A each. The source current is obtained as 10 A. The filtered peak output voltages and peak output current are obtained as 160 V and 3.2 A, respectively, as shown in Fig. 3d.

8 Performance Evaluation of the Proposed Topology 8.1 Duty Ratio (D) Versus Boost Factor (B) The comparison between duty cycle (D) and boost factor (B) of the proposed converter with the conventional topologies under SBC technique is shown in Fig. 4. Compared to other topologies, the proposed topology provides a higher voltage boost and inversion. With the same modulation index, the SL-SC-based qSBI gives higher

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Fig. 4 Shoot-through duty ratio versus boost factor

gain and it improves the output voltage quality. When the duty ratio reaches 0.3, the boost factor becomes significant for the proposed topology. With the simulation studies, it is obvious that the proposed topology is able to provide the boost factor of 6.4 with the input DC voltage of 32 V and duty ratio of 0.22. It is slightly less than the theoretical calculation due to the snubber resistance and capacitance present in the active and passive switches. With the same operating parameters, ZSI, CFSI and qSBI can provide the boost factor of 1.79. The SL-qSBI provides 2.29, and SC-qSBI provides (with single cell) the boost factor of 3.58.

8.2 Modulation Index (M) Versus Voltage Gain (G) The relation between the modulation index (M) and the voltage gain (G) of various single stage boost inverter topologies is depicted in Fig. 5. It is clear from Eq. (22) that the gain (G) depends on D and M. It may also be observed from Fig. 5 that the maximum operating value of D and M is governed by D + M ≤ 1. With the reduced value of modulation index, the proposed inverter is capable of providing higher voltage gain compared to that of the traditional inverter topologies. With the modulation index (M) of 0.78 and input voltage of 32 V, the proposed topology is able to provide the peak ac voltage of 160 V with a gain (G) of 5.

8.3 Harmonic Profile Figure 6 shows the harmonic profile of load voltage of the SL-SC qSBI topology. It is observed that the proposed topology has the THD of 2.44% which very well meets

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Fig. 5 (M) versus (G)

Fig. 6 Harmonic spectrum of load voltage

the IEEE standard. Due to the absence of dead time in the switching pulses, THD is minimized compared to conventional VSI topologies.

9 Conclusion A novel single-phase SL-SC-based quasi-SBI topology is discussed in detail. The proposed topology offers a high voltage gain by replacing the inductor in SC-qSBI topology by a switched inductor cell without changing any other elements. The proposed topology is suitable wherever a high voltage gain needs to be obtained from a low DC voltage source like RES-based applications. To summarize the features of the SL-SC-based qSBI topology, performance comparison is done between the

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proposed topology and the classical inverters in terms of voltage gain, voltage boost and THD. The performance comparison exhibits that the proposed topology has higher boost factor with a minimum duty ratio, as compared to the conventional topologies. Also, it is able to produce a boost factor of 6.4 and voltage gain of 5 with a minimum duty ratio of 0.22 which is not possible with conventional topologies. Finally, the operation of the proposed topology is verified with simulated waveforms.

References 1. Erickson, R.W., Maksimovic, D.: Fundamentals of Power Electronics. Kluwer, Norwell, MA (2001) 2. Lazzarin, T.B., Bauer, G.A.T., Barbi, I.: A control strategy for parallel operation of single-phase voltage source inverters: analysis, design and experimental results. IEEE Trans. Ind. Electron. 60(6), 2194–2204 (2013) 3. Peng, F.Z.: Z-source inverter. IEEE Trans. Ind. Appl. 39(2), 504–510 (2003) 4. Anderson, J., Peng, F.: Four quasi-Z-source inverters. In: 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, pp. 2743–2749 (2008) 5. Nguyen, M.K., Lim, Y.C., Cho, G.B.: Switched-inductor quasi-Z-source inverter. IEEE Trans. Power Electron. 26(11), 3183–3191 (2011) 6. Pan, L.: L-Z-source inverter. IEEE Trans. Power Electron. 29(12), 6534–6543 (2014) 7. Ho, A., Chun, T., Kim, H.T.: Extended boost active-switched-capacitor/switched-inductor quasi-Z-source inverters. IEEE Trans. Power Electron. 30(10), 568–5690 (Dec 2014) 8. Nguyen, M.K., Lim, Y.C., Choi, J.H.: Two switched-inductor quasi-Z-source inverters. IET Power Electron. 5(7), 1017–1025 (2012) 9. Nguyen, M.K., Lim, Y.C., Kim, Y.G.: TZ-source inverters. IEEE Trans. Ind. Electron. 60(12), 5686–5695 (2013) 10. Qian, W., Peng, F.Z., Cha, H.: Trans-Z-source inverters. IEEE Trans. Power Electron. 26(12), 3453–3463 (2011) 11. Loh, P.C., Li, D., Blaabjerg, F.: -Z-source inverters. IEEE Trans. Power Electron. 28(11), 4880–4884 (2013) 12. Mo, W., Loh, P.C., Blaabjerg, F.: Asymmetrical -source inverters. IEEE Trans. Ind. Electron. 61(2), 637–647 (2014) 13. Nguyen, M.K., Lim, Y.C., Park, S.J.: Improved trans-Z-source inverter with continuous input current and boost inversion capability. IEEE Trans. Power Electron. 28(10), 4500–4510 (2013) 14. Upadhyay, S., Ravindranath, A., Mishra, S., Joshi, A.: A switched-boost topology for renewable power application. In: 2010 Conference Proceedings IPEC, vol. 10, pp. 758–762 (2010) 15. Mishra, S., Adda, R., Joshi, A.: Inverse Watkins–Johnson topology—based inverter. IEEE Trans. Power Electron. 27(3), 1066–1070 (2012) 16. Ravindranath, A., Mishra, S., Joshi, S.: Analysis and PWM control of switched boost inverter. IEEE Trans. Ind. Electron. 60(12), 5593–5602 (2013) 17. Adda, R., Ray, O., Mishra, S.K., Joshi, A.: Synchronous-reference-frame-based control of switched boost inverter for standalone DC nanogrid applications. IEEE Trans. Power Electron. 28(3) (2013) 18. Nguyen, M.K., Lim, Y.C., Park, S.J.: A comparison between single phase quasi-Z-source and quasi-switched boost inverters. IEEE Trans. Ind. Electron. 62(10), 6336–6344 (Apr 2015) 19. Ravindranath, A., Avinash, J., Santanu, M.: Pulse width modulation of three-phase switched boost inverter. In: 2013 IEEE Energy Conversion Congress and Exposition (2013) 20. Nguyen, M.K., Le, T.V., Park, S.J., Lim, Y.C.: A class of quasi switched boost inverters. IEEE Trans. Ind. Electron. 62(3), 1526–1536 (2015) 21. Nag, S.S., Mishra, S.: Current-fed switched inverter. IEEE Trans. Ind. Electron. 61(9) (2014)

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22. Asl, E.S., Babaei, E., Sabahi, M.: High voltage gain half-bridge quasi-switched boost inverter with reduced voltage stress on capacitors. IET Power Electron. 10(9), 1095–1108 (2017) 23. Nguyen, M.-K., Tran, T.-T.: A single-phase single-stage switched-boost inverter with four switches. IEEE Trans. Power Electron. 33(8), 6769–6781 (2018) 24. Asl, E.S., Babayi, M.H.: Steady-state and small-signal analysis of high voltage gain half-bridge switched-boost inverter. IEEE Trans. Ind. Electron. 63(6), 3546–3553 (2016) 25. Do, D.-T., Nguyen, M.-K.: Three-level quasi-switched boost T-type inverter: analysis, PWM control, and verification. IEEE Trans. Ind. Electron. 65(10), 8320–8329 (2018) 26. Nguyen, M.-K., Le, T.-V., Park, S.-J., Lim, Y.-C., Yoo, J.-Y.: Class of high boost inverters based on switched-inductor structure. IET Power Electron. 8(5), 750–759 (2015) 27. Zhu, M., Yu, K., Luo, F.L.: Switched-inductor Z-source inverter. IEEE Trans. Power Electron. 25(8), 2150–2158 (2010) 28. Nguyen, M.-K., Duong, T-D., Lim, Y.-C., Kim, Y.-G.: Switched-capacitor quasi-switched boost inverters. IEEE Trans. Ind. Electron. 65(6), 5105-5113 (2018) 29. Sriramalakshmi, P., Sreedevi, V.T.: A single phase cascaded five level quasi switched boost inverter based on switched capacitor structure. In: IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)), India, pp. 1–6 (2018)

Investigation of Energy Performance of a High-Rise Residential Building in Kolkata Through Performance Levels of Energy Conservation Building Code, 2017 Gunjan Kumar, Biswajit Thakur, and Sudipta De

1 Introduction India is the world’s third largest producer and consumer of electricity with installed capacity of 344 GW as on May 31, 2018. All India per-capita electrical consumption has increased from 631.4 kWh (2005–2006) to 1075 kWh (2015–16) [1]. On volume basis, India is generating a total of 2344.2 million metric ton of CO2 emissions which is 7% of world total emissions [2] and third highest after China and USA. Under Paris agreement on climate change, India has submitted its intended National Determined Contribution (INDC) with commitments to reduce emission intensity of its GDP by 33–35% by 2030 from 2005 level [3] and having nationwide campaign for energy conservation target to save 10% [4] of energy consumption by 2018–2019. In order to achieve energy efficiency in India, Energy Conservation Act was enacted in 2001, under which Bureau of Energy Efficiency (BEE) was created in 2002. One core focus of BEE is to reduce energy intensity of commercial buildings and high-rise residential buildings through Energy Conservation Building Code (ECBC) 2007. Due to technological development in energy efficiency, execution flexibility and fast-track code implementation, second version of ECBC was launched in June 2017 [5] with provision of energy efficiency performance levels. This code prescribes the three levels of energy efficiency like ECBC baseline building, ECBC+ building and ECBC super building. Residential buildings consume about 75% of total electricity used by building sector and are second highest emitter of greenhouse gases (GHGs) after industrial sector. The generation of private building stock in urban zones is moving rapidly toward multi-storey private structures from the prior method of building singular G. Kumar · S. De (B) Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India e-mail: [email protected] B. Thakur Department of Civil Engineering, Meghnad Saha Institute of Technology, Kolkata 700150, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_47

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homes. Planned 12 million affordable houses by 2022 Government of Indian will keep the building sector rolling to new height in the years to come. The gross electricity consumption in residential buildings has been rising sharply over the years. Given the current and anticipated rapid growth in the residential building stock across India, the Energy Conservation Code for residential buildings (Part I: Building Envelope Design; Eco-Niwas Samhita 2018) was established by the Ministry of Power in December 2018 [6]. Residential buildings codes require energy efficiency performance levels, and hence, an effort is given in this paper to explore energy saving potential of high-rise residential building based on performance-level approach of Energy Conservation Building Code (ECBC) 2017. This code addresses the complex thermodynamics of a building for minimum energy performance, passive design strategies and incorporate advanced technologies to realize the code compliance. The building models were designed as per ECBC guideline with focus on envelop and HVAC system. Energy performance analysis was carried out in e-QUEST simulation software for actual design, ECBC baseline, ECBC+ and ECBC super. The obtained results compare the achievable energy performance improvements of a typical high-rise residential apartment building in the warm and humid climate of Kolkata over its actual conventional design by complying with the three ECBC specified levels. It may be useful to the policy makers for finding out the probable positive impacts following code compliance, possible scope of building energy code improvement for better code compliance, energy efficiency benchmarking and facilitate energy policy decision for this segment.

2 Methodology 2.1 Building Data Collection The building data and required drawings like floor plan , elevation and section of a high-rise residential building at Kolkata, West Bengal, India is collected along with other required construction and system details. Table 1 shows the details about the building selected for the present study.

2.2 Operation Schedule Occupancy considered is from 5 pm to 9 am on weekdays and Sundayis considered 24 h open. Day time is considered as unoccupied.

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Table 1 Input building data of Kolkata location Parameters

Description

Building type

High-rise multifamily residential

Location

Kolkata, West Bengal, India

Climatic zone

Warm and Humid

Floors

G + 19

Area,

ft2

155,103

Conditioned area, ft2

131,187

Un conditioned area, ft2

23,916

Building orientation

Longer axis is in E-W direction

2.3 Envelope Details Envelope details for built building are given in Table 2.

3 Energy Modeling and Simulation Building energy modeling and simulation closely mimic the actual building with real world at design stage itself and give performance understanding without carrying field test before going for the actual construction. It helps at early stage to optimize system Table 2 Opaque envelope specification Opaque assembly

Construction layers

Specification

Exterior wall assembly

Assembly layers: Cement plaster with sand-aggregate, 25.4 mm Brick, common, 304.8 mm Cement plaster with sand-aggregate, 25.4 mm

U-value = 1.84 W/m2 K

Roof assembly

Assembly layers: Concrete, LW, 40 Lb., 101.6 mm Concrete, HW, Dried, 140 Lb., 152.4 mm Cement plaster with sand-aggregate, 25.4 mm

U-value = 1.16 W/m2 K

Vertical fenestration

U-value = 4.9916 W/m2 K: SHGC = 0.50, VLT = 0.50

Window wall ratio

24.40%

HVAC

Split system, single zone DX, Air cooled

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design and guide for selection of most performing building materials, equipments, appliances and schedules. It serves the interest of occupant, building owner and environment. Simulation input parameters take care of the thermal comfort, visual comfort and indoor air quality as required for occupant’s productivity. It facilitates designer to select effective equipment, material and helps to opt climate responsive strategies. Owner interest is served with building energy optimal performance, scope of energy benchmarking and lower operational cost. Energy saving achieved at the end will reduce environmental impact and promote sustainable building design solution. Whole building performance method is used for energy modeling and simulation.

3.1 Description Energy Modeling Software A building following ECBC code shall need to show compliance through whole building energy simulation software that has been approved by BEE. e-QUEST is among the BEE recommended tools for building energy simulation based on DOE 2.2 platform. It is a widely used and well-accepted building energy simulation tool. Whole building energy simulation involves energy consumption prediction using software taking into consideration of integrated approach of design like building orientation, shape, climate zone, envelope, heat load, lighting load, comfort condition, equipment efficiencies, operational schedule, etc. on annual calculation of energy consumption. e-QUEST simulation tool, which runs on DOE 2.2 simulation engine, is used for the purpose of analysis. The software has the capability to model three-dimensional geometry, envelope, lighting, process and HVAC loads to accurately represent the energy consumption of a building. Design cases for different performance levels are modeled as per the ECBC guidelines to calculate the relative energy efficiency improvements over the actual as built building.

3.2 Weather File The project is located at Kolkata; thus, IND_Kolkata.428090_ISHRAE.bin weather file has been used.

3.3 Building Model Description A representative model of the considered building is devolved using the energy modeling software and elevation views of the same from two different directions are presented in Fig. 1. The details of the parameters are given in Table 3.

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Fig. 1 Views of the building as modeled in e-QUEST

Table 3 Building model input parameters Description

Unit

Actual design

ECBC baseline

ECBC +

ECBC super

Wall U-value

W/m2 °K

1.84

0.40

0.34

0.22

Roof U-value

W/m2 °K

1.16

0.33

0.20

0.20

Glazing U-value

W/m2 °K

5

3.0

2.2

2.2

0.50

0.50

0.50

0.50

50

27

27

27

Window shading

Yes

Yes

Yes

Yes

HVAC system type

Split system, single zone DX 3.4

3.5

Glazing SHGC Glazing VLT

%

Cooling EER

3.5

3.3

Lighting power density

W/ft2

0.70 (software default)

Equipment power density

W/ft2

1.90 (software default)

Occupancy

ft2 /person

200 (software default)

Zone cooling set point

°F

78

78

78

Zone heating set point

°F

68

68

68

3.4 Simulation Output Building simulation is performed in e-QUEST simulation software through whole building performance method by keeping total unmet hours less than 300. Presented simulation results highlight three levels of energy performance: ECBC, ECBC+ and

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ECBC super building as prescribed by the revised ECBC code 2017 and compare the same with actual as built case to assess the achievable performance complying with the mentioned three levels in a warm and humid climate of Kolkata.

4 Results and Discussions Based on simulation outcome, monthly load variation for space cooling, ventilation fans, Misc. equipment, area lighting and total annual energy consumption load is given in Figure 2 and overall saving is given in Table 4. Figure 2 shows variation of major monthly loads like space cooling, ventilation fans, Misc. equipment, area lighting across the year. For first two months (January, February) and last two month (November, December), the energy load seems to be low as compared to the same for remaining eight months where there is significant load as expected in Kolkata’s climate. In this study with reference to Fig. 3, it is to be noted that in annual energy consumption, Misc. equipment load and space cooling contribute maximum on load profile with further contribution from lighting load and ventilation fans load. Saving for space cooling load, ventilation load and total energy with ECBC-level approach of envelope design and HVAC system efficiency is in Fig. 4. Envelope parameter is selected as per the guideline given in ECBC 2017 for ECBC, ECBC+ and ECBC super design. Results show space cooling load and ventilation load decreases form actual design case to ECBC super design case with overall reduction by 16.17% and 20.88%, respectively. This is due to the combined effect of improved envelop and selected high star BEE rating HVAC system for given level of performance. It has been observed that the annual energy demand decreases form actual design to ECBC super design but with a value of 7.2%. A building complies with the ECBC Table 4 Annual energy consumption by end-use electricity (kWh × 1000) End uses

Actual design

ECBC baseline

Space cooling

1008.9

Ventilation fans Exterior lighting Misc. Equipment Area lighting Total

% Saving

ECBC+

% saving

ECBC super

% saving

992.2

1.65

916.9

9.11

845.7

16.17

95.3

77.7

18.46

77.0

19.20

75.4

20.88

5.1

5.1



5.1



5.1



1114.1

1114.1



1114.1



1114.1



316.6

316.6



316.6



316.6



2540.0

2505.6

1.35

2429.6

4.34

2356.9

7.2

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Fig. 2 Monthly energy consumption a ECBC b ECBC+ c ECBC super and d As built building

Fig. 3 Annual energy consumption pattern a ECBC b ECBC+ c ECBC super and d As built building

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Fig. 4 Percentage energy saving with ECBC-level approach of design

code using whole building performance method, when the estimated annual energy use of the proposed design is less than that of the standard design. The energy performance index (EPI) of a building is its annual energy consumption in kilowatt-hours per square meter (kWh/m2 ) of the building. The EPI ratio of a building is the ratio of the EPI of proposed building to the EPI of standard building. The EPI ratio of a building should be less than or equal to the EPI ratio of the applicable building type and climate zone of the code. Achieved result of present residential building study is in line with requirement as EPI ratio for ECBC, ECBC+ and ECBC super is 1, 0.96 and 0.94, respectively. Present study considers only the variation of envelope parameters, HVAC system rating and other significant parameter like lighting load variation, electro-mechanical equipment efficiency is considered as fixed values for all case in order to understand the impact of envelope design for annual energy consumption. However, cumulative effect of all above mentioned parameters is expected to give significant energy saving and establish the need of Energy Conservation Building Code for residential building with all components.

5 Conclusion Design of high-rise residential building in line with Energy Conservation Building Code with performance-level approach is a sustainable energy solution as it offers design flexibility and energy saving. ECBC 2017 performance-level approach for envelop and HVAC design was applied for energy performance study of residential building and to assess its saving potential. As per the provisions of the code, variation of thermal performance of the opaque and non-opaque part of the envelop and

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variation in the efficiency of the HVAC systems is considered only. Effect of the variation in other energy-consuming parameters such as exterior and interior lighting, electro-mechanical equipments is yet to be explored. Results show that the space cooling load and annual energy consumption decrease with the improved level of design, i.e., ECBC baseline, ECBC+ and ECBC super. Further exploration of the energy performance of the residential buildings in light of the ECBC Residential Code is deemed necessary for better understanding of the process.

References 1. Central Electricity Authority (CEA) (2017) Growth of electricity sector in India from 1947– 2017. CEA, Government of India, New Delhi. http://www.cea.nic.in/reports/others/planning/ pdm/growth_2017.pdf. Last accessed 14 Mar 2019 2. Chandel SS, Sharma A, Marwaha BM (2016) Review of energy efficiency initiatives and regulations for residential buildings in India. Renew Sustain Energy Rev 54:1443–1458 3. NITI Aayog. India Energy Security Scenario, 2047. NITI Aayog, Government of India, New Delhi. Available at http://indiaenergy.gov.in/iess/default.php. Last accessed 1 May 2018 4. Yu, S., Tan, Q., Evans, M., Kyle, P., Vu, L., Patel, P.L.: Improving building energy efficiency in India: state-level analysis of building energy efficiency policies. Energy Policy 110, 331–341 (2017) 5. Energy Conservation Building Code User Guide 2017 (2017). https://beeindia.gov.in/sites/def ault/files/BEE_ECBC%202017.pdf. Last accessed 14 Mar 2019 6. Bureau of Energy Efficiency (2017) Eco-Niwas Samhita 2018, Energy Conservation Building Code for Residential Buildings Part I: Building Envelope. ISBN 978-81-936846-3-4. https:// www.beeindia.gov.in/sites/default/files/ECBC_BOOK_Web.pdf. Last accessed 14 Mar 2019

Addressing Last Mile Electricity Distribution Problems: Study of Performance of SHGs in Odisha Sneha Swami and Subodh Wagle

1 Introduction Despite the push for electrification schemes specifically geared for grid extension and universal access, the current status on the ground of electricity access to poor consumers is dismal, especially in remote areas [1]. The last mile of electricity distribution remains one of the unconquered territories for distribution utilities (DU) across India. Un-metered connections, illegal connections (tapping on distribution line), poor efficacy, and efficiency in metering and bill collection are common in those areas. [2]. Un-metered and illegal connections lead to high level of losses on the system (sometimes even 40–50%), while poor billing and collection efficiency lead to high levels of revenue losses. Unscheduled power outages, low-quality of supply (frequent interruptions and low voltages), lacuna and mistakes in billing, and unresponsive consumer service lead to consumer dissatisfaction, non-payments of bills, theft, and other malpractices [2, 3]. The following are some of the problems faced by distribution utilities (DU) in the last mile of electricity distribution: • Limited Demand: Electricity consumption in rural areas is very less as compared to an urban area or on commercial feeder. As utilities earn less revenue, they give less priority for providing 24 h of supply on rural feeders [4]. • Remote Locations and Sparse Consumer Spread: Due to the remoteness of villages and thin spread of consumers, feeders are extended over long distances, however, without provision of adequate number of transformers and other equipment. This leads to the increased losses, frequent interruptions, and low quality of supply, all, in turn, leading to reducing the bill collection efficiency [5]. S. Swami · S. Wagle (B) Centre for Policy Studies (CPS), IIT Bombay, Mumbai 400076, India e-mail: [email protected] S. Wagle Centre for Technology Alternatives for Rural Areas (CTARA), IIT Bombay, Mumbai 400076, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_48

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• Accessibility Problems: Villages are often far from the substation or the DU Office located in a town where electricity bill amount is collected and complaints are registered (Field experience). For consumers, it is difficult and expensive to travel every time to reach the office and do a follow-up of their complaints. This prompts illegal access or meter tampering by consumers. • Lack of Consumer Awareness and Capability: Sometimes rural consumers are not even aware of the protocols for complaints or for getting new/metered connection or raising the complaints. This affects not just the service quality and revenue but also the accountability of DU towards the consumers. • Limited Human Resources: Addressing a large number of consumer complaints with limited human resource manpower (available with the DU) creates many predicaments for consumers. With limited manpower in hand, it is difficult to detect theft cases in all areas. • Employee Theft: In some cases, DU employees indulge in malpractices like recording lesser consumption than actual metered consumption or simply not providing meter to the consumer [6]. This affects the revenue generated by the utility. • Bill Complaints: Meter reading is the most crucial part of revenue generation process. If there are mistakes in meter reading then it takes long time and energy of consumers to rectify it. This hampers the overall services provided to consumers. To address such problems in the last mile and to improve the performance of electricity distribution sector, the provisions allowing Distribution Franchisees (DF) were introduced in Electricity Act 2003 [7]. Section 2 of the Act defines franchisee as: “franchisee means a person authorized by a distribution licensee to distribute electricity on its behalf in a particular area within his area of supply”. Section 5 has provisions for local distribution in rural areas by Panchayat institutions, co-operative societies, non-governmental organizations, and the user’s association to work as a franchise. DFs are supposed to improve bill collection efficiency and reduce losses in the specified area. DF can have a combination of functions like, • Maintenance of the Local Grid: Preventive maintenance, Repairs of the fault in the grid, repairs of the faults at consumer’s end • Handling the Metering Cycle: Meter reading, preparing bills, distributing bills, collecting bill amount • Addressing Consumer Grievances: giving new metered connections, solving consumer complaints regarding meter, bills, or connection problems, • Help DU in Controlling Theft. Over the past 10 years, various utilities have attempted to adopt different versions of the DF model. While a handful of DFs have been operating successfully, some of these DFs were aborted at the bidding stage itself and others were terminated due to various problems [8]. As most of the DFs are operating at the district-level, they require significant capital investment and incur high operational costs. In most cases, districts having very high losses were selected by DUs for the DF experiment. Torrent Power in Bhiwandi, Maharashtra is one of the most successful examples of

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DF which managed to bring down losses from 73 to 24% [9]. But, apart from this DF, DFs in different states could not accomplish the given targets and hence have been shut down. The main reasons for terminating DF contracts include: huge amounts of debt, failure to improve bill collection efficiency, increased consumer complaints, and failure to reduction in losses consistently [10, 11]. Many cases of DF show that transition of responsibility from DU to DF did not make much of positive difference in performance, rather, in some areas, it made the situation worse (i.e., increased losses and consumer complaints). DFs tried to change the style of management but they could not effectively address ground-level problems in the last mile of the distribution network. Though there is one case in Andhra Pradesh State where women SHGs were contracted for metering-billing and revenue collection activities. There were 12 rural feeders in tribal areas which were maintained by self-help group named IKP Mandal Mahila Samakhya. This area prominantly has power theft by direct hooking, and it is difficult to arrest theft since it is influenced by Maoist movement. Along with SHGs tribal youth is also engaged and the consumer complaints are being redressed with in a stipulated time. It is not only creating employment, but also it has yielded very fruitful results. The line losses were reduced from 47.33% to 20.26% over a span of 2 years [12]. In many other cases policy documents and DF contracts articulate targets and responsibilities of DFs, they fail even to mention the need for or measures for building capabilities of DFs to handle the multi-faceted, chronic, and complex problems in the last mile of the distribution network. On this background, few DFs from Odisha employed an innovative strategy of appointing SHGs from villages to handle tasks of distribution of bills and collection of payments. This paper presents the research understanding this institutional innovation and carries out preliminary assessment of its contribution to improvement in the performance of DF in the last mile.

2 Methodology This paper presents the research prompted by the previous discussion on difficulties faced by DFs and the possible remedy of involving community-level organizations such as SHGs in the last mile operations in the electricity distribution sector. The research question is articulated as: How local institutions like SHGs, NGOs and PRIs could address last mile functions in the electricity distribution sector? To answer the research question, the study focused on the initiative of one districtlevel distribution franchise (DF) called Feedback Distribution Company (FEDCO) from Odisha, which engaged SHGs for the last mile operations in its area of work. For this study, semi-structured interviews were carried out in Hindi and English languages with field and office staff of FEDCO and SHGs involved in the process of implementing this SHG model under distribution franchise. All interviews were transcribed in English for thematic analysis. Contract documents, collection, and

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billing efficiency data was collected from the franchise office and analyzed in a systematic manner.

3 SHGs in the Last Mile of Electricity Distribution 3.1 Organizational Structure and Working Feedback Distribution Company (FEDCO) is the DF working under the DU in Odisha called Central Electricity Supply Utility (CESU). FEDCO is operating in 16 divisions of one district and has engaged 142 SHGs for handling tasks of metering, billing, and collection of payments (MBC). Figure 1 shows the organizational structure of FEDCO. The distinctive and novel features of this organizational structure are as follows: • SHG Management Team: This team consists of one Manager and two Assistant Managers, who are socially active and educated persons from the local area. They help FEDCO in communicating with women of the SHGs. They help SHG members to solve community-level issues by personally visiting the field locations. They keep the track of performance of all SHGs and focus on the improvement of teams with poor performance. Before starting the work FEDCO’s officials and SHG management team conducted community meetings in villages to make consumers aware of paying bills and checked the status of metered connection in villages.

Fig. 1 Organizational structure of FEDCO operations and SHGs

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• Consumer Care Centre: This Centre is operational 24-by-7. Consumers can call on the toll-free number provided to them for registering their complaints regarding electricity supply or fault. • Analytics Team: This team works to identify theft cases from the analysis of available consumption data. It also monitors volume of work handled and overall performance of SHG members. • Vigilance Team: This team detects theft cases and takes action against them. SHG members and Analytics team provide them with information on suspected theft cases. Table 1 describes specific tasks delegated to SHGs under six broad areas of responsibilities. To ensure good performance from SHGs on these tasks, FEDCO conducted 3 day-long sessions for training and building capabilities before SHGs started their operations. Apart from training on procedures to follow, members of SHGs were also trained to operate machines that are used for taking meter reading and making calculations for bills. FEDCO also has stipulated operating procedures for members of SHGs to carry out the tasks assigned to them. For example, if a consumer is not available at home they have to visit again to take the meter reading. Most of the villagers decide the day on which they will pay the bill, SHG members have to collect the bill amount on that day. SHGs get paid in the form of incentives. Money is transferred to their registered bank accounts every month. Existing incentive structure is mentioned in Table 2. FEDCO staff members along with SHG teams conducted community meetings. During those meetings, they tried to solve past billing related problems as well as made them aware of paying future bills timely. Awareness was spread via SHG members, meter readers, banner, declaring in speakers, and printed leaflet. This helped them to fetch the arrears from rural consumers. Table 1 Areas of responsibilities and specific tasks of SHGs Broad areas of responsibilities

Specific tasks

Meter reading and billing

Metering and billing cycle starts after 5th of every month Infra-Red meter reading, mobile-based and manual entry Trained for using these machines and understand bill components

Bill collection

Collection starts after 15th of the month Receipt of payment is given to the consumers after payment

Arrears collection

Collection drive after every 3 months Section officers accompany for collection of arrears

Consumer complaints

Consumer complaint form is with members which they fill up For urgent complaints, they call section office or field staff

New connection

SHG members fill the form with them Guide consumers about the further procedure

Theft

Community members inform SHG members about suspected cases

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Table 2 Incentive structure for SHG’s payment Billing

Collection

Rs. 3/– per meter reading

6% on total collection

Rs. 5/– per extra bill generated

Additional 2% incentive on achieving collection efficiency greater than 100%

Additional incentive of Rs. 2/– per bill, for billing new service connections

Additional 1% incentive on net current collection from 90 to 100% of the current billed amount

Table 3 Present Practices to address last-mile electricity distribution problems Last-mile functions in the electricity distribution

Present practices in FEDCO’s area

Provision of adequate, quality, reliable, affordable supply of electricity

It is not outsourced to local institutions nor to DF. DU has all the responsibility and control over it

Management of Supply provided to franchise, Management of resources (financial and manpower), repair and maintenance of the system in distribution area

DU only informs DF of outages or DF informs DU on maintenance requirements. Odisha, having surplus power, does not have load shedding for many years

Revenue generation (meter reading, billing and All LV-side infrastructure and consumer collection), responsibilities of staff members complaints are handled by DF (FEDCO’s) field staff. HT side maintenance is under DU (CESU) Grievance redressal and conflict resolution

This is completely under FEDCO. They have SHGs and meter readers for MBC activities

Expansion of distribution system for adding new consumers and total increase in supply due to increased consumption

Consumer grievances are handled by FEDCO

While adopting this institutional innovation, DF has also invested in infrastructure and manpower to improve the supply and services in villages. For example metering technologies used are easy to handle and can be monitored by the sensors. Thus, DF is able to monitor their field employees and hold them accountable for their work. As SHGs are only doing the MBC, tasks they cannot be held accountable for is quality of supply provided, franchise is responsible for that. From all the interviews present practices followed in last-mile connectivity functions, in the Nayaghar district are listed in Table 3.

3.2 Lessons from FEDCO’s Innovation Some interesting insights were brought out through the interaction with staff members of DF and SHGs. Employing SHGs to take MBC tasks in the last mile

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of electricity distribution was not aimed at addressing all the problems in the last mile of distribution mentioned before. However, the innovation did help address some. • Improving Consumer Awareness and Capability: As SHG members are always present in the village, consumers report their complaints over a phone call and don’t have to go to the office every time. Easy access to a person representing DF has helped consumers to report their complaints immediately and take follow-up. Any doubts on the part of the consumer about a new metered connection, meter reading, or bill can be addressed immediately. The trained SHG members act as resource persons for villagers. • Reducing Electricity Theft: Women SHG members were found to be more effective in handling the theft-related tasks. As women working in SHG and consumers are from the same community, identifying theft is easier for these women, sometimes from neighbors of the consumer indulging in theft. Over the tea and casual conversations, they get to know if someone is stealing the electricity. Being women, they can easily go inside the homes and check if meters are tempered. • Reducing Employee Theft: FEDCO has started monitoring of meter reading and bill collection work of each employee, including SHG members. They analyze the data from GPS tracker, the submitted readings, bill collection, and talk to the SHG members personally if their performance is poor. Also apart from monitoring, other positive incentives are provided, such as prizes to best-performing SHGs. • Reducing Billing Complaints: Many meter reading complaints arise due to lack of mechanism for cross-checking of entered reading against actual reading. Taking and uploading a photograph of the meter reading is mandatory in the process of bill preparation. The photographs are used for cross-checking by the team in the head office of FEDCO. Any discrepancies found are reported on the same day and corrected. As bills are generated on the spot, consumers can also check its accuracy. This has resulted in significant improvement in efficiency of billing and collection. This also keeps SHG members accountable to enter correct meter reading and brings transparency in the system. • Provision of Adequate Human Resources: Engaging SHG members for these activities has created additional human resources in the last mile, at very low costs compared to hiring full-time employees. The rate of attrition is very low for these local women. This trained and experienced work force in the last mile has helped DF address last mile problems in fast and effective manner, immensely improving the quality of service provided to consumers. As another evidence of improvement in performance, Fig. 2 shows reduction in Aggregate Technical and Commercial (AT&C) losses in the Nayaghar district from 59 to 39% which is the highest percentage reduction in areas under FEDCO.

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Fig. 2 Performance of SHGs

4 Conclusions The paper underscores the need to find new and innovative solutions to address the well-known and chronic problems in the last mile connectivity of the electricity distribution sector, especially in rural areas. SHGs appear to be an attractive solution to the failure of Distribution Franchisee, especially in view of the credibility, grounding, rapport, and intimate knowledge possessed by SHGs of local consumers and community. With the required training and capabilities, SHGs can surely address the problems related to electricity theft, consumer complaints, and lack of human resources which will eventually improve consumer satisfaction. Strict monitoring by DF will bring accountability and transparency in the system. The observations and analysis of initiative in Odisha presented in this paper further the expectation that SHGs can be one possible solution for the vexed problems in the last mile of the electricity distribution sector. The research presented in this paper is still undergoing and is expected to bring in more insights and better understanding of this solution.

References 1. Ramji, A., Soni, A., Sehjpal, R., Das, S., Singh, R.: Rural energy access and inequalities: An analysis of NSS data from 1999-00 to 2009-10, TERI-NFA Working Paper No. 4, The Energy and Resources Institute (2012) 2. Chaurey, A., Ranganathan, M., Mohanty, P.: Electricity access for geographically disadvantaged rural communities-technology and policy insights. Energy Policy 32(15), 1693–1705 (2004)

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3. Bhattacharyya, S.C.: Energy access problem of the poor in India: Is rural electrification a remedy? Energy Policy 34, 3387–3397 (2010) 4. Prayas Energy Group, Electricity Supply Monitoring Initiative (ESMI) data, https://www.wat chyourpower.org/index.php. Accessed on 12th Feb 2019 5. Gibson, J., Olivia, S.: The effect of infrastructure access and quality on non-farm enterprises in rural Indonesia. World Dev. 38, 717–726 (2010) 6. Sharma, T., Pandey, K.K., Punia, D.K., Rao, J.: Of pilferers and poachers: combating electricity theft in India. Energy Res. Soc. Sci. 11, 42–52 (2016) 7. Electricity Act 2003, Central Electricity Regulatory Commission. http://www.cercind.gov.in/ act-with-amendment.pdf. Accessed 27 Jan 2019 8. Business line: Maharashtra Govt’s Discom wants to rope in private players in six more cities, Dated on 19 Oct 2017. http://www.thehindubusinessline.com/companies/maharashtra-govtsdiscom-wants-to-rope-in-private-players-in-six-more-cities/article9916037.ece 9. Tankha, S., Misal, A.B., Fuller, B.W.: Getting reforms done in inhospitable institutional environments: untying a Gordian Knot in India’s power distribution sector. Energy Policy 38, 7121–7129 (2010) 10. Thakur, T., Bag, B., Prakash, S.: A critical review of the franchisee model in the electricity distribution sector in India. Electricity J. 30, 15–21 (2017) 11. Banks, J.P., Bowman, C.D., Gross, T.P., Guy, J.: The private sector: cautiously interested in distribution in India. Electricity J. 11, 21–28 (1998) 12. Success Stories, REC Institute of Power Management and Training. http://www.recipmt.com/ success.php. Accessed 3 Feb 2019

Transient Stability Analysis of Wind Integrated Power Network Using STATCOM and BESS Using DIgSILENT PowerFactory Neha Manjul and Mahiraj Singh Rawat

1 Introduction Unlike conventional generators, the wind energy produces stochastic power output due to inherent wind characteristics. In recent years, the power production from wind energy resources has increased exponentially. Therefore, the stability of power system becomes an important issue in modern power systems. The transient stability is among one of the electrical power system stability. According to IEEE/CIGRE joint task report [1] “Transient stability is concerned with the ability of the power system to maintain synchronism when subjected to a severe disturbance, such as a short circuit on a transmission line.” In Denmark, the power output from wind energy supplies more than 20% of the local electricity consumption and the aim of the Danish government to increase this share to 50% by 2025 [2, 3]. The voltage and transient stability of wind farms integrated into the power grid was studied in [4]. The ability of fast response under large disturbances, the power converter of doubly-fed induction generators (DFIGs) enables wind farm to reach steady-state conditions much faster than conventional generators. In [5], the sensitivity-based method was investigated in order to determine the impact of DFIG based wind turbine generators (WTGs) on small-signal and transient stability of power systems. The intermittent generation characteristics of wind farms lead to fluctuating power output which may further push the stability margin to its limit. In [6], a method was proposed for fast assessment of the transient stability margin (TSM) considering the uncertainty of wind generators. In [7], a wide area control (WAC) was proposed to enhance the transient stability of the DFIG integrated power grid. A real-time method for the assessment of transient stability of a power system comprising wind N. Manjul (B) · M. S. Rawat National Institute of Technology Uttarakhand, Srinagar Garhwal 246174, India e-mail: [email protected] M. S. Rawat e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_49

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turbine was proposed in [8]. The proposed real-time method utilizes the corrected kinetic energy method for determining the critical clearing time. In [9], a coordinated control scheme for superconducting magnetic energy storage (SMES) devices in the wind power integrated system were investigated in order to enhance the overall transient stability of the system. To enhance the transient stability of large power system the researchers had applied the STATCOM and BESS [10]. It is observed that power system integrated with DFIG based wind farms was less sensitive to transient disturbances such as fault clearing time, voltage sags and wind penetration below a certain threshold. Above threshold the wind farm has adverse effect on transient stability of system [11]. Authors in [12] had utilized energy capacitor system (ECS) comprised of electric double layer capacitor (EDLC) and power electronics device to improve the system transient stability. In [13], the performance of the DFIG based wind farm on different transient disturbances had investigated. In this paper, the transient disturbances such as sudden load change, three-phase faults, sudden change in wind speed, and wind gust are investigated in a power system comprising wind farms. To enhance the system transient stability, the devices, i.e., BESS and STATCOM are also considered. The paper is structured as follows: Sect. 2 explains the model of DFIG based wind farm and its control. Section 3 discusses the BESS and its control. Section 4 describes the STATCOM and its control. Finally, Sect. 4 concludes the results. The standard IEEE 14 bus test system is used to obtain the results. All simulation studies have performed in DlgSILENT PowerFactory software.

2 Modeling of DFIG Based Wind Power In literature [14–16], different models of DFIG based wind power for the transient stability study have been proposed. The schematic diagram of the grid integrated DFIG is represented in Fig. 1. The DFIG based wind turbine has the ability to independently control the active and reactive power during transient disturbances. In this paper, the DFIG based wind turbine model available in PowerFactory library have utilized. The block diagram of DFIG with its controllers is shown in Fig. 2.

3 Modeling of BESS and Its Control The BESS technology provides fast active power compensation in power systems during transient disturbances. The BESS technology comprises of two parts: battery and rectifier/inverter. A voltage source converter (VSC) functions as a rectifier and inverter during charging and discharging, respectively. The schematic diagram of a typical battery storage is represented in Fig. 3. The state of charge (SOC) defines the current status of battery. If SOC is one, then the battery is fully charged while SOC is zero, then the battery is fully discharged.

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DFIG

Tranformer Grid

Gear Box DC

DC AC

AC

RSC

GSC

Fig. 1 Grid integrated DFIG based wind power SpeedRef speed

Pitch Control

Turbine

pw

Shaft

vw

pt

MPT

Slow freq Meas.

Pref-in

Fmeas

Over freq power reduction

Pref

PQ control

Ird_ref Irq_ref

Ir Control

Pctrl,qctrl

Compen sation

Cosphiu, sinphiu

PQ total

Ird Irq

Protection

Vac bus

usr, usi

DFIG

Psir_r,qsir_j Cosphiref, sinphiref Cosphim, sinphim

Theta meas.

Id, Iq

Speed Control

Vac gen

beta

Current Measurement

Irot

Fig. 2 Block diagram of DFIG with controllers (PowerFactory library model) Udc

0 0

PQ Measurement

p

1

DC Side Calculation

l

Battery Model

1 3 4

Fig. 3 Block diagram representation of battery

Ucell SOC ICell

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AC- Voltage Measurement

Cosref;sinref

vin

frq

Frequency Measurement Frequency Control

dpref

Id_ref_out; Iq_ref_out

PQ-Control

PQ Measurement

Id_ref_in; Iq_ref_in

Ucell

Battery Model

Converter

SOC

Charge Control

Icell deltai

Fig. 4 Block diagram representation of composite model of BESS

There are two main constraints on the system: first is the rated power/current of the converter and the second is the capacity of the battery that is the amount of stored energy. In this paper, the BESS of 30 MVA capacity has utilized. The composite model of battery with its controller is shown in Fig. 4. The voltage output from a typical BESS can formulate using Eq. (1). UDC = Umax × S OC + Umin × (1 − S OC) − I Z i

(1)

where SOC: State of charging; U max : Maximum voltage output; U min : Minimum voltage output; I: current; Z i : equivalent internal impedance.

4 Modeling of STATCOM and Its Control The static compensator (STATCOM) is a FACTS device connected in shunt position and used for reactive power compensation in the transmission network. The schematic diagram and V-I characteristics of STATCOM is shown in Fig. 5. The basic STATCOM device consists of VSC, coupling transformer, and capacitor bank. The STATCOM has the ability to supply/absorb reactive power independent with system voltage at the point of common coupling (PCC) during a transient disturbance. The control scheme for typical STATCOM is shown in Fig. 6.

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VAC

1.0

VCON

VSC VDC

Ic

Icmax

0

ILmax

IL

Fig. 5 STATCOM and V-I characteristics

Voltage DC Meas

Vdc

Voltage AC Meas

Vac

PLL Sinref, cosref

Id_ref

VDC /VAC Convertor Q Meas

PWM Convertor Iq_ref

qac

Fig. 6 Control scheme of STATCOM

5 Simulation and Results In this paper, the IEEE 14 bus test system is utilized to investigate the transient stability of the system comprising of the DFIG based wind turbine which is shown below in Fig. 7. The test system consists of five generators (Synchronous generator -02 Nos, Synchronous Compensator -03 Nos), 17 transmission lines, 11 constant load demand, 19 buses, and 8 transformers. The total system active and reactive load demand is 259 MW and 73.5 MVAr, respectively. The 30% load scaling has set for the test system. In order to compensate for the increased load demand, the 13 Nos. DFIG based wind turbine having a 6 MW capacity each has integrated into the test system. The transient stability of test system comprising DFIG based wind farms

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Bus_13 G1 Bus_14

Bus_12

Bus_10

Bus_11

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G8

G6

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WTG 1

Bus_2 Bus_3

G2

STATC OM

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+ -

G3

Fig. 7 IEEE 14 bus test system

in the presence of STATCOM and BESS has been investigated during following transient disturbances. 1. 2. 3. 4.

Sudden change in load demand Three-phase fault in transmission line Sudden change in wind speed Effect of Wind gust.

The following cases are taken into consideration. Case 1: Test system with DFIG based wind farm Case 2: Test system with DFIG based wind farm, STATCOM, and BESS.

5.1 Sudden Change in Load Demand The sudden change in load demand is considered as a transient event. The sudden increase in load demand at selected bus can introduce system transients. In this paper, the power system performance is investigated for 20% increase in load demand at bus 3, 4, and 9 at after 1.0 s. The temporary difference in the power balance between

Transient Stability Analysis of Wind Integrated Power Network … 1.02

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Fig. 8 Variation of various parameters under sudden load change

the mechanical and electrical power of each generator can lead to acceleration of rotor angle for the whole system. The variation in various parameters of different elements of the IEEE 14 bus test system is represented in Fig. 8. It is observed from Fig. 8, the transient response of various parameters in case 2 has better performance compared to case 1.

5.2 Three-Phase Fault in Transmission Line The most severe disturbance in the power network is three-phase fault on the transmission network. In this paper, a symmetrical three-phase fault is created at 1.0 s on transmission line connected between bus 2 and 3, which is cleared at 1.5 s. When three-phase fault occurs in the system, the dynamics during the post fault can

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become unstable because of inadequate damping supplied from the generator. The combination of BESS and STATCOM offers an additional degree to add damping in the system and assist with mitigating the instability problem. The results obtained through simulations are represented in Fig. 9. 1.3

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Fig. 9 Variation in different parameters under three-phase fault

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5.3 Sudden Change in Wind Speed The simulation is started normally and sudden increase in wind speed from 11.019 m/s to 14 m/s. When the wind speed is increased, the negative slip is also increased, therefore the power delivered from the stator side is decreased and power delivered from rotor side is increased. The system gets back in stable state after some seconds. When generator speed is increased then the pitch angle of the system settles to a new value. The power delivered by the generators also settles to a new value so that the maximum power can be achieved from the new speed. The simulation results are shown in Fig. 10. 15

50.03

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Active power (MW)

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26 25.8 25.6 25.4 25.2 25

0

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Fig. 10 Variation in different parameters under sudden change in wind speed

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frequency (Hz)

speed

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1.0206 1.0204

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0

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Fig. 11 Variation in different parameters under wind gust transient condition

5.4 Effect of Wind Gust A wind gust starting at 2 s and ending at 5 s is simulated. All parameters are settling back to its original position. Hence a wind gust can reach its original position of the event, but this event takes a longer time to settle down to the steady-state. The results obtained from the simulation are represented in Fig. 11.

6 Conclusion In this paper, the transient stability of a power system comprises of the DFIG based wind farm is investigated. To enhance the transient stability of the system, STATCOM

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and BESS are applied. The transient disturbances, i.e., sudden change in load demand, three-phase fault on the transmission line, sudden change in wind speed, and the effect of wind gust has investigated on wind energy integrated power system with/without STATCOM and BESS. It is observed that STATCOM and BESS are the most effective solution to improve the transient stability of a power system with wind farms. They can absorb or produce active and reactive power, according to the requirement under transient events.

References 1. Kundur, P., Paserba, J., Ajjarapu, V., Anderson, G., Bose, A., Canizares, C., Harziargyriou, N., Hill, D., Stankovic, A., Taylor, C., Cutsem, T.V., Vittal, V.: Definition and classification of power system stability. IEEE Trans. Power Syst. 19(2), 1387–1401 (2004) 2. Muyeen, S.M., Tamura, J., Toshiaki, M.: Stability augmentation of a grid-connected wind farm, 1st edn. Springer, London (2009) 3. Fox, B., Bryans, F., Flynn, D., Jenkins, N., Milborrow, D., O’Malley, M., Watson, R., AnayaLara, O.: Wind Power Integration: Connection and System Operational Aspects, 2nd edn. IET, United Kingdom (2014) 4. Muljadi, E., Nguyen T.B., Pai M.A.: Impact of wind power plants on voltage and transient stability of power systems. In: IEEE Energy 2030 Conference, pp. 1–7. IEEE, Atlanta, GA, USA 5. Gautam, D., Vittal, V., Harbou, T.: Impact of increased penetration of DFIG based wind Turbine Generators on transient and small signal stability of power systems. IEEE Trans. Power Syst. 24(3), 1426–1434 (2009) 6. Hua, K., Mishra, Y., Ledwich, G.: Fast unscented transformation based transient stability margin estimation incorporating uncertainty of wind generation. IEEE Trans. Sustain. Energy 6(4), 1254–1262 (2015) 7. Yousefian, R., Bhattarai, R., Kamalasadan, S.: Transient stability enhancement of power grid with integrated wide area control of wind farms and synchronous generators. IEEE Trans. Power Syst. 32(6), 4818–4831 (2017) 8. Tajdinian, M., Seifi, A.R., Allahbakhshi, M.: Transient stability of power grids comprising wind turbines: new formulation, implementation, and application in real time assessment. IEEE Syst. J. 13(1), 894–905 (2019) 9. Jiang, H., Zhang. C.: A method of boosting transient stability of wind farm connected power system using S magnetic energy storage unit. IEEE Trans. Appl. Superconduct. 29(2) (2019) 10. Kanchanaharuthai, A., Chankong, V., Loparo, K.A.: Transient stability and voltage regulation in multimachine power systems vis-à-vis STATCOM and battery energy storage. IEEE Trans. Power Syst. 30(5), 2404–2416 (2015) 11. Chowdhury, M.A., Shen, W., Hosseinzadeh, N., Pota, H.R.: Transient stability of power system integrated with doubly fed induction generator wind farms. IET Renew. Power Gener. 9(2), 184–194 (2015) 12. Muyeen, S.M., Takahashi, R., Ali, M.H., Murata, T., Tamura, J.: Transient stability augmentation of power system including wind farms by using ECS. IEEE Trans. Power Syst. 23(3), 1179–1187 (2008) 13. Baggu, M.M., Chowdhury, B.H.: Performance of doubly fed induction machine windgenerators during grid and wind disturbances. In: 38th North American Power Symposium, pp. 49–56. IEEE, Carbondale, IL, USA 14. Ledesma, P., Usaola, J.: Doubly fed induction generator model for transient stability analysis. IEEE Trans. Energy Convers. 20(2), 388–397 (2005)

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15. Kim, D.J., Moon, Y.H., Nam, H.K.: A new simplified doubly fed induction generator model for transient stability studies. IEEE Trans. Energy Convers. 30(3), 1030–1042 (2015) 16. Coughlan, Y., Smith, P., Mullane, A., O’Malley, M.: Wind turbine modeling for power system stability analysis—a system operator perspective. IEEE Trans. Power Syst. 22(3), 929–936 (2007)

Experimental Investigation of Solar Drying Characteristics of Grapes S. P. Komble, Govind N. Kulkarni, and C. M. Sewatkar

1 Introduction Drying helps in the preservation of agricultural food products. Open sun drying is the oldest drying method. This method has limitations of being unprotected from rain, wind-borne dirt and dust, insects, rodent, and other animals. Solar drying was explored to understand the drying characteristics of many agricultural products. Lewis [1] first presented a mathematical model to describe drying characteristics. An exponential model for drying of porous products was proposed. In this model drying coefficient varied with the rate of diffusion, surface evaporation, and thickness. Page [2] reported the effects of temperature and relative humidity of the drying air and proposed an expression for moisture content and drying time. A review of various drying models was done [3]. The models in the review were evaluated on the basis of coefficient of correlation (r), reduced chi-squared (χ 2 ), RMSE, and MBE values. The review observed that Page’s model provided highest value of the coefficient of correlation and least value of reduced chi-square. Experiments were carried out on grapes in Turkey [4]. Grape behavior is very well related by two-term model. As far as kinetic studies are concerned, experimental investigation recorded that Page’s model was more fitting to Thompson seedless grapes for determining drying constants [5]. An analytical model of drying chamber air and grapes was presented [6]. The authors concluded S. P. Komble · C. M. Sewatkar (B) Department of Mechanical Engineering, Govt. College of Engineering and Research, S. P. Pune University, Avasari, Pune, India e-mail: [email protected] S. P. Komble Department of Mechanical Engineering, Vishwakarma Institute of Technology, S. P. Pune University, Pune, India G. N. Kulkarni Anna Saheb Dange College of Engineering and Technology, Ashta, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_50

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that latent heat of vaporization of grapes was higher than that of free water. The latent heat was found to be the function of the moisture content of the grapes. Many researchers investigated drying air temperature, relative humidity, velocity, effects of initial and final moisture content [7–20]. Research work was also undertaken on solar drying kinetics of grapes by forced convection with various available thin-layer models. It is observed that not much attention is given to the natural convection solar drying process with thin-layer models. In the presented experimental work dry run tests identified the maximum mean temperature inside the dryer for loading the product. The study focused on the applicability of thin-layer mathematical models for solar drying of grapes placed in a cabinet type natural convection solar dryer. The study was carried out in the climatic conditions of Pune, India.

2 Experimental Setup Figure 1 shows the schematic diagram of natural convection cabinet type double glazing solar dryer with auxiliary electric heating arrangement and dimensions 2 m length, 1 m width and 0.8 m height. Figure 2 shows the actual photo of solar dryer system. A black-coated sheet is fitted below the glass cover to prevent direct heating of the products. This sheet acts as absorber plate. The sidewalls and base of the solar dryer were insulated. For air circulation inlet and outlet pipes are provided at the front and top of the dryer, respectively. Product to be dried is loaded on the trays. Electric heaters of 3 kW ratings are provided at the bottom portion inside the dryer. Though heaters are an integral part of the dryer, electric heaters are not used in the present experimentation. The ambient air enters through the inlet pipes of dryer. Through the glass glazing, solar radiation strikes the absorber plate. The absorber plate becomes hot and radiate

Fig. 1 Schematic diagram of natural convection cabinet type double glazing solar dryer

Experimental Investigation of Solar Drying Characteristics …

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(a) Front view of solar dryer

(b) Rear view of solar dryer Fig. 2 Actual photo of solar dryer system

heat in the dryer space. Incoming air becomes hot. Hot air passes over the products to be dried, heats up the product, removes moisture from the product, and rises towards the outlet. The product under experimentation in this work is grapes. Grapes are pretreated before loading into the dryer. Initially, grapes were dipped into ethyneoleate oil of 250 ml, solution of potassium carbonate of 300 gm, and water for 5 min to increase the permeability of waxed coat. In this experiment, 15.6 kg of grapes were used. To improve the quality of drying, the grapes were sprayed by the solution of oil and carbonate for three days. Grapes

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were dried for 8–9 days. During this period measurements were taken from 10 to 17 h with an interval of 1 h each day. Gradual growth of grapes is shown in Fig. 5.

3 Result and Discussion 3.1 Dry Run Tests on Solar Dryer To know the performance of dryer without load, the dry run tests involved running the dryer in the following conditions Case 1: Inlets and outlets—open, Case 2: Inlets and outlets—closed Case 3: Inlets and outlets—closed with heaters

Temperature, ºC

Case 4: Inlets and outlets—open with heaters. Temperature of the air in the dryer changes with time and space, therefore, maximum mean temperature of air in the dryer is recorded in all the four cases. This information is used as a guide to load the product. Figure 3 demonstrates variation of mean temperature of air in the dryer for the above cases from 1000 to 1700 h with an interval of 1 h. The global radiations are measured at these time instances using pyranometer. It is observed that the dryer temperature, ambient temperature, and global radiation are increased from morning to noon and after that decreases towards evening which is as expected. Difference between dryer temperature and ambient temperature represents 160

Case1

140

Case2 Case3

120

Case4 100 80 60 40 20 10

11

12

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Time, hrs Fig. 3 Variation of average instantaneous temperature

15

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541

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Case1

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Case4 600 400 200 0 10

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Time, hrs Fig. 4 Variation of average radiation intensity with time over the period

the heat gain in inlet air inside the dryer. The corresponding variation in the radiation intensity during the same time period is shown in Fig. 4, which confirms the results of temperature variations. These tests were conducted from 31st March to 3rd April 2017; each day corresponds to the respective case. In case 1, all the inlet and outlet pipes were kept open. The rate of air circulation in the dryer became the highest. The air temperature in the dryer, therefore, observed to be almost uniform at all the locations. Difference in the dryer and ambient air temperature was also minimal in this case. In case 2, all the inlet and outlet pipes were closed. Auxiliary electric heaters were not operated. This has increased the average temperature of the dryer as there was no airflow within the dryer. In case 3, all inlet and outlet pipes are closed. There was no convection current. The average temperature inside the dryer, therefore, shoots up substantially. In case 4, all the inlet and outlet pipes were opened. Auxiliary electric heaters were operated. The dryer air temperature, in this case, is higher than case 1 and 2.

3.2 Test Results with Loaded Trays With the help of photographs, the progressive stages of drying of grapes over 8 days are shown in Fig. 5. The change in the color of the grapes confirmed the conversion into raisins. This process required 8 days with the proposed dryer. The measurement of moisture content of the grapes and raisins was carried out using moisture analyzer. It was found that initial moisture present in the grape was

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Fig. 5 Growth of grapes to raisins from day 1 to day 8

77% which reduced to 13.6% after complete drying of the grapes into raisins. This is the safe moisture content limit for obtaining good quality raisins with better taste and odor.

3.3 Drying Characteristics of Grapes In the present work, drying was conducted in the solar dryer for eight days. The average velocity and temperature noted at inlet pipes and outlet pipes over the period of eight days were 0.68 m/s and 41.1 °C, respectively. Moisture contents were determined using correlations given in Table 1. Due to natural convection relative humidity varies continuously so MR (moisture ratio) is taken as M − Me /Mo − Me . The value of R (correlation coefficient) and reduced ψ 2 helps us to understand best fitting of equation. The expressions for ψ 2 and R2 are given below: 2 N  n=i MRexp i − MRpre i ψ = N −n 2

Table 1 Mathematical models applied to the drying curves [4]

(1)

Thin-layer drying model

Model name

MR = exp(−kt)

Newton

MR = exp(−kt n )

Page

MR =

exp(−kt)n

Modified page

MR = a exp(−kt)

Henderson and Pabis

MR = a exp(−kt) + c

Logarithmic

MR = a exp(−k0 t) + b exp(−k1 t)

Two-term

MR = 1 + at + bt 2

Wang and Singh

Experimental Investigation of Solar Drying Characteristics …

  N  n=i MRexp i − MRexp MRprei − MR pr e R = 2  N  2 N  n=i MRexp i − M R exp n=i MRprei − MR pr e 2

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(2)

In the above expressions MRexp , i is the ith experimental moisture ratio, MRpre i is the ith predicted moisture ratio, N is the number of observations, and n is the number of constants [4]. Dependence of drying kinetics variable on constants was observed in this work. During the drying experiments, variation in the values of parameters was in the following range Ambient air temperature, °C Relative humidity of ambient air, % Average dryer temperature, °C Relative humidity of air in the dryer, % Global solar radiation intensity, W/m2

29–40 24–38.5 34–52 34 to 93 198–976

The initial moisture content of the grapes on dry basis was 3.34 kg while the same reduced to 0.1580 kg. Newton model resulted in a higher R2 and lower ψ 2 . So this model is used to represent the drying characteristics of grapes in this work and constants and coefficients of model used for regression analysis were as follows. MR = exp (−kt)

(3)

where k = 0.042704. At any point of point moisture content in grapes can be found out by the above model, Drying variables very much depend on various constants and coefficients in model, with R2 = 0.9869 and ψ 2 = 9.09 × 10−3 . The experimental work illustrates modeling and validation of drying process of grapes. Constants of the empirical models can be expressed in terms of air temperature, humidity, and velocity. The computed values of moisture content for particular drying conditions were validated by experimental moisture content values. The predicted values indicated the suitability of the Newton model in describing drying behavior of grapes. Drying time and moisture ratio are introduced in Newton model to obtain the best fit. Low values of standard error and high values of correlation coefficient demonstrate the suitability of exponential model in describing the drying characteristics of seedless grapes. Among various drying models studied, Newton model accurately predicted the drying characteristics of grapes. Newton model effectively described the thin-layer drying kinetics. Logarithmic scale on Y-axis is useful to understand and it is showing a straight line as shown in Fig. 7.

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Moisture Ratio

1 0.8 0.6 0.4 0.2 0 0

10

20

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40

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Drying Time (hrs) Fig. 6 Variation of moisture ratio with drying time for all models

In Lewis or Newton model, the difference in moisture content between the material and the equilibrium moisture content in the drying air condition is proportional to rate of drying. This rate of drying can be determined by drying conditions such as temperature, humidity of air, velocity of air, and the heat supply to which grapes are exposed. Drying of grapes involves evaporation and diffusion. Newton model is based on assumptions that there are constant drying conditions and given material does not shrink greatly during drying. The drying constant K varies with rate of diffusion and surface evaporation and with thickness. In this case, due to pretreatment surface is made porous and therefore initially surface evaporation is rapid as compared to diffusion, this can be observed in Fig. 6 where experimental results and Newton model is matching very much till 14 h then later diffusion is rapid as compared with surface evaporation, but rate of diffusion is slow and grapes take longer time to come to safe moisture limit that is from 14 h to 56 h, so after the second day there is some variation in experimental results and Newton model. This is due to variation in drying conditions by natural convection. Therefore, value of K affects very much to the drying process as per the external drying conditions. Thus in this experimental work, controlling factors are the drying conditions govern the drying phenomenon rather than the controlling factors of material itself, which are not considered in Newton model. Therefore, Newton model effectively describes drying kinetics in this study (Fig. 7).

4 Conclusion With dry run tests, a guideline could be obtained about maximum mean temperature inside the dyer.

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1 0

7

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Moisture Ratio

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56

Moisture Ratio (Exp) Newton Model Page Model Modified Page Model Handerson and Pabis Logarithmic Model

0.25

0.125

Two Term Model 0.0625

Two Term Exponential Model Wang & Singh Model

0.03125

Drying Time (hrs) Fig. 7 Variation of moisture ratio with drying time for all models (logarithmic scale)

Moisture content of grape varied with tray location. Drying rate was higher at two locations first in the top region near the glass cover and second at the front portion of lower and upper trays. Product comes directly in contact with high-temperature air at theses points. Relative humidity of air entering the dryer is low while that of leaving the dryer is high due to gain in moisture from grapes. Drying process occurs in falling rate period and thin-layer solar drying of grapes was studied.

References 1. Lewis, W.K.: The rate of drying of solid materials. J. Ind. Eng. Chem. 13(5), 427–432 (1921) 2. Page, G.E.: Factors influencing the maximum rates of air drying shelled corn in thin layers. MS Thesis. Purdue University, US. pp. 1–46 (1949) 3. Mujumdar, A.S.: Handbook of Industrial Drying, 3rd edn. Taylor and Francis Group, LLC. (2006) 4. Yaldiz, O., Ertekin, C., Uzun Ibrahim H.: Mathematical modelling of thin layer solar drying of sultana grapes. Energy 26(5), 457–465 (2001) 5. Sawhney, R. L., Pangavhane, D.R., Sarsavadia, P.N.: Drying Studies of Single Layer Thompson Seedless Grapes. In: International Solar Food Processing Conference Proceedings, pp. 1–20, Indore, India (2009) 6. El-Ghetany, H.: Experimental investigation and empirical correlations of thin layer drying characteristics of seedless grapes. Energy Convers. Manag. 47(11–12), 1610–1620 (2006) 7. Lyes, B., Azeddine, B.: Numerical simulation of drying under variable external conditions: application to solar drying seedless grapes. J. Food Eng. 76(2), 179–187 (2006)

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8. Krokida, M.K., Karathanos, V.T., Maroulis, V., Marinos-Kouris, V.: Drying kinetics of some vegetables. J. Food Eng. 59, 391–403 (2003) 9. Ibrahim, D., Mehmet, P.: The effects of dipping pretreatments on air-drying rates of the seedless grapes. J. Food Eng. 52(4), 413–417 (2002) 10. Fadhel, A., Kooli, S., Farhat, A., Bellghith, A.: Study of the solar drying of grapes by three different processes. Desalination 185(1–3), 535–541 (2005) 11. Singh, S.P., Jairaj, K.S., Srikant, K.: Universal drying rate constant of seedless grapes: a review. Renew. Sustain. Energy Rev. 16(8), 6295–6302 (2012) 12. Nascimento, P., Silva, C., Gomes, J., Hamawand, I.: Description of seedless grape drying and determination drying rate. J. Agric. Stud. 2(2), 1–10 (2014) 13. Doymaz, ˙I., Akgün, N.A.: Study of thin-layer drying of grape wastes. Chem. Eng. Commun. 196(7), 890–900 (2009) 14. Cakmak, C., Yıldız, C.: The drying kinetics of seeded grape in solar dryer with PCM-based solar integrated collector. Food Bioprod. Process. 89(2), 103–108 (2011) 15. Belessiotis, V., Delyannis, E.: Solar drying. Sol. Energy 85(8), 1665–1691 (2011) 16. Bennamoun, L.: An overview n application of exergy and energy for determination of solar drying Efficiency. Int. J. Energy Eng. 2(5), 184–194 (2012) 17. VijayaVenkataRaman, S., Iniyan, S., Goic, R.: A review of solar drying technologies. Renew. Sustain. Energy Rev. 16(5), 2652–2670 (2012) 18. Ekechukwu, O.V., Norton, B.: Review of solar-energy drying systems II: an overview of solar drying technology. Energy Convers. Manag. 40(6), 615–655 (1999) 19. Singh, P., Shrivastava, V., Kumar, A.: Recent development in greenhouse solar drying. Renew. Sustain. Energy Rev. 82(3), 3250–3262 (2018) 20. Gallali, Y.M., Abujnah, Y.S., Bannani, F.K.: Preservation of fruits and vegetables using solar drier: a comparative study of natural and solar drying, III; chemical analysis and sensory evaluation data of the dried samples. Renew. Energy 19(1–2), 203–212 (2000)

Feedback and Feedforward Control of Dual Active Bridge DC-DC Converter Using Generalized Average Modelling Shipra Tiwari and Saumendra Sarangi

1 Introduction Growing focus towards the renewable sources to produce power which is clean and non-polluting has increased over the decades to sustain the conventional sources from getting depleted. In order to harness these powers, the various interfaces are needed after which it is ultimately supplied to the load demands. The power electronic converters are also an interface between the renewable sources or any other source to the load. Among these converters, the dual active bridge is one such converter which is an isolated bidirectional DC-DC converter used for high- and medium-power applications. The dual active bridge converter was first proposed by De Doncker et al. [1, 2], which can be used for high-power applications including electric vehicles, interfacing devices for renewable sources, smart grids and solid-state transformers applications. The dominance of this isolated bidirectional DC-DC converter is providing buckboost property as well can be seen in terms of high- power density [1, 3], highly efficient, low passive components, inherent soft switching, circuit symmetry, etc. Earlier, due to lack of availability in the advanced power electronic switches, the power density achieved in a DAB converter was restricted. However, with the developments in the advanced IGBT modules, the power level was increased and hence the efficiency was enhanced. The modelling of the DAB converter was also done using small-signal analysis [4–8] and also the usage of DAB in a very high-frequency applications where the size of the transformer is very compact such as for aerospace applications [9]. The new modelling is also proposed and compared with the existing S. Tiwari (B) National Institute of Technology, Srinagar, Uttarakhand, India e-mail: [email protected] S. Sarangi Motilal Nehru National Institute of Technology, Prayagraj, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_51

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models [10]. The various drawbacks like reactive power and conduction losses have also been minimized so as to improve the efficiency of the converter [8, 11]. The basic circuit analysis can be carried out in continuous time domain and discrete domain [7] where the latter analysis becomes more complex to understand. The continuous time domain makes the analysis based on the state-space model of the system where the constraint applied is the assumption “small ripple”. This assumption is however, not true in the cases where high ripples or harmonic contents are present [12]. Therefore, the overall analysis of using only the DC component (approximated) might not be able to provide the actual dynamics of the system parameters. Therefore, the generalized average modelling technique [12–14] can be used to provide the alternating behaviour of the inductor current which contains the zeroth-order, firstorder and increased order harmonics. In [13], the author has provided the complete analysis of the DAB converter considering the forward power flow and developed the full-time continuous sixth-order model considering the zero-order and first-order harmonics of the inductor current and output capacitor voltage. The author also used the assumption of DC component of inductor current to be zero and developed the decoupled circuit equations which was the reduced third-order model. The full order model was more accurate than the reduced order model. However, as the converter is bidirectional, the input side parameter was not considered as when this converter is interfaced with another system, the input parameter will definitely affect the system performance and dynamics will vary. In order to control the input side, the feedforward control can be used whereas for the output side, the feedback controlling technique has been employed [15]. In this paper, the author has modelled the DAB converter taking into account the input capacitor voltage with detailed analysis. It is observed that, the input parameter when used in the dynamics, the equations will modify and depend on this parameter as well. The system performance is also analysed through the equations of generalized average modelling as well as extensive simulations. The simulation results are shown and it is observed that the output as well as the input voltage is controlled using the two PI controllers which are easily tuned. The only limitation is the usage of two controllers. However, the input controlled using output relation also makes the system slower as it involves the inverse trigonometric functions which are slow loops and will ultimately make the system slower. The overall paper is divided into six sections where Sect. 2 describes the DAB converter basics. Section 3 briefs about the generalized average modelling technique which is used here. In Sect. 4, the control aspects is discussed and simulation results are presented in Sect. 5. Finally, Sect. 6 concludes the paper.

2 The Dual Active Bridge Converter The DAB converter consists of two H bridges on either side of the high-frequency (HF) transformer with an external inductor connected as a power transfer link. There is a phase shift φ between the primary and secondary voltage (referred to the same

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Fig. 1 The dual active bridge converter

side) which decides the direction of power flow. Positive phase shift will make the power flow from high voltage to low voltage side (forward direction) and similarly negative phase shift will do the opposite. The waveforms associated with forward power flow are shown in Fig. 2. The power output of the converter is given as [1] Po =

nVs Vo d(1 − d) 2 fs L s

(1)

where d = πφ ; f s = switching frequency (Fig. 1). The circuit equations and modelling are described in the next section.

3 Generalized Average Modelling The fixed switching frequency of 0.5 duty ratio is taken and a fixed frequency of 10 kHz. This is the simple single phase shift control technique where the secondary side voltage lags the primary voltage by a phase shift φ = dπ . The waveform of the technique is shown in Fig. 2 for better understanding. With the switching of the corresponding switches for positive and negative voltages, the two voltage levels at the transformer sides are converted into a switching function given by [13] ⎤ ⎡ s1 (τ ) = 1; 0 ≤ t < T2 ⎥ ⎢ −1; T ≤ t < T 2 ⎥ ⎢ ⎣ s2 (τ ) = 1; dT ≤ t < T + dT ⎦ 2 2 2 −1; 0 ≤ t < dT 2 Therefore, primary and secondary voltages are

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Vpri = s1 (τ )Vc1 (τ )

(2)

Vsec = s2 (τ )Vc2 (τ )

(3)

Taking the state variables i L (τ ), Vc1 (τ ), Vc 2(τ ) as inductor current, input and output side capacitor voltages being time-dependent are related through equations given as following (4) Vs (τ ) − i s (τ )rs = Vc1 (τ ) VL (τ ) = L

using (4) i c1 (τ ) =

di L (τ ) = s1 (τ )Vc1 (τ ) − s2 (τ )Vc2 (τ ) dτ

(5)

Vc2 (τ ) = Vo (τ )

(6)

i c1 (τ ) = i s (τ ) − s1 (τ )i L (τ )

(7)

Vs (τ ) − Vc1 (τ ) C1 d Vc1 (τ ) = − s1 (τ )i L (τ ) dτ rs

(8)

C2 d Vc2 (τ ) Vc2 (τ ) = s2 (τ )i L (τ ) − dτ RC2

(9)

To make these time-varying nonlinear systems of equations into linear time-invariant system, state-space averaging is applied by using Fourier series to represent the system variable. Thus, the zero-order and first-order harmonics terms are considered and the equations involved is clearly mentioned in [13]. Using those equations, the zero-order and first-order harmonic content variable is given where the fundamental harmonic component is bifurcated into real and imaginary parts. The system state variables are < Vc1 >o , < Vc1 >1R , < Vc1 >1I , < I L >o , < I L >1R , < I L >1I , < Vc2 >o , where R and I are the real and imaginary parts and subscript “o” is the DC component or the mean value. The switching function is like a square wave and its Fourier coefficients are given as < s1 >o =< s2 >o = 0 < s1 >1R = 0; < s1 >1I = < s2 >1R =

(10) −2 π

−2 sin(dπ ) −2 cos(dπ ) ; < s2 >1I = π π

(11) (12)

The state-space equations by applying averaging and Fourier series property are given in the following matrix

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dX = AX + BU dt

(13)

where X = [Vc1o , Vc11R , Vc11I , I L o , I L 1R , I L 1I , Vc2o ]T and U = Vs . The matrix A comes out to be ⎤ ⎡ −1 4 0 0 0 0 0 rs C 1 πC1 ⎢ 0 −1 ωs 0 0 0 0 ⎥ ⎥ ⎢ rs C 1 ⎢ 0 −ω −1 2 0 0 0 ⎥ s rs C1 πC1 ⎥ ⎢ ⎥ ⎢ 0 0 0 ⎥ ⎢ 0 0 π−4L 0 ⎢ 2 sin(dπ) ⎥ ⎥ ⎢ 0 0 0 0 0 ωs πL ⎢ −2 2 cos(dπ) ⎥ ⎦ ⎣ πL 0 0 0 −ωs 0 L sin(dπ) −4 cos(dπ) −1 0 0 0 0 −4 πC πC2 RC2 2 and B = [ rs1C1 0 0 0 0 0 0]T .

3.1 Steady state and small-signal analysis At steady state, the derivative terms (d X/dt) = 0 and the matrix are reduced to 4thorder, and small-signal analysis is carried out by taking small perturbations in the parameters, d = D + d (vc1 )0 = Vc10 + Vc10 and similarly other large signals are represented in terms of steady state and smallsignal states, and the small-signal averaged model is derived using trigonometric approximations as sin(π d) = π d and cos(π d) = 1. The reduced 4th-order matrix is similar to 3rd-order matrix by removing the Vc10 row. In the 3rd-order case [13], the relation between input and output voltage came out to be Vo 8R sin(Dπ ) (14) = Vs π 2 ωs L However, Eq. (14) will be true for 4th-order as shown Vo 8R sin(Dπ ) = Vc1 π 2 ωs L

(15)

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Thus, the relation between input–output voltage is now changed to 8R sin(Dπ ) ∗ π 2 ωs L Vo = 2 Vs (π ωs L) + (8 sin(Dπ ))2 rs R

(16)

Therefore, designing the system equations based on input state variable also changes the system dynamics. The derived reduced order model after applying small-signal perturbations is shown as Aˆ ⎤ ⎡ −1 4 0 0 rs C 1 πC1 2 sin(Dπ) ⎥ ⎢ 0 0 ωs πL ⎥ ⎢ −2 2 cos(Dπ) ⎦ ⎣ −ωs 0 πL L −4 cos(Dπ) −1 0 −4 sin(Dπ) πC2 πC2 RC2 c20 −2 sin(Dπ)Vc20 4 sin(Dπ)I L I LR T Bˆ = [0 2 cos(Dπ)V − 4 cos(Dπ)I ] πL πL C2 C2 T ˆ X = [Vc10 I L R I L I Vc20 ] Uˆ = d. The characteristic equation |s I − A| is calculated and given as

|s I − A| = s 4 + (a1 )s 3 + (a2 )s 2 + (a3 )s + (a4 )

(17)

1 + rs1C1 + π 20.5 where a1 = RC LC1 2 8 a2 = π 2 LC2 + ωs2 + Rrs C1 1 C2

a3 =

ω2 ωs2 8 + π 2 rs LC + rs CS 1 RC2 1 C2 2 ωs2 (π D) − 4sin . rs RC1 C2 π 4 L 2 C1 C2

+

0.5 π 2 R LC1 C2

a4 = The obtained transfer functions of both the voltages to be controlled are given as (ao )s 3 + ( rsaCo 1 )s 2 + (b1 )s + (b2 ) Vo (s) VC2 (s) = = d(s) d(s) |s I − A| 4(sin(π D)I L I −cos(π D)I L R ) C2 8ωs2 cos2 (π D)Vo 8 sin2 (π D)ωs Vo + + ao ωs2 π LC2 π LC2 a ω2 D) sin(π D) 8ωs Vo − 4Vo cos(π + rsoC1s . πrs LC1 C2 π 3 L 2 C1 C2

(18)

where ao = b1 =

b2 = Similarly,

+

2ao 4π 2 LC1

(c1 )s 2 + (c2 )s + (bo ) VC1(s) = d(s) |s I − A|

D) cos (π D) s cos(π D) o sin (π D) where bo = 0.5VπoRωLC − 4Vo sin(π − 4V − π 3 L 2 C1 C2 π 3 R L 2 C1 C2 1 C2 D)Vo c1 = −0.5 πsin(π LC1 D) o cos(π D) o sin(π D) c2 = −0.5ωsπVLC − 0.5V + 0.5aπo 2cos(π . π R LC1 C2 LC1 1 2

3

(19) 0.5ao ωs sin(π D) π 2 LC1

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The control to output transfer functions in Eqs. (18) and (19) is tuned using PI controller using the feedforward and feedback control of the output and input variables discussed in the next section.

4 Feedforward and Feedback Control The output and input side capacitor voltage is to be controlled for which the simple PI controller is tuned, respectively. Usually the input side control is accompanied by inverter buffer at the comparator input, while the output tuning is done according to the transfer matrix obtained from the above derivation. The open-loop system itself comes out to be stable, and therefore, the simple tuning of PI controller makes the control easy. For the primary side switches, each switch is maintained the duty cycle of 0.5 by comparing a constant with a repeating signal of frequency 10 kHz. The output is the pulse of 0.5 duty cycle. This is fed to the primary side switches. However, the secondary side switches are provided the delay of dTs as modulated by the PI controller. The basic control diagram is shown in Fig. 3. The parameter specifications are shown in Table 1.

5 Simulation Results The following results are validated using MATLAB/Simulink for the two control desired. These are shown in the following results shown in Figs. 4, 5 and 6. The

Fig. 3 Closed-loop control Table 1 Parameter specifications Parameter Vs VO Value

150 V

150 V

C1

C2

L

fs

100 µF

540 µF

500 µH

10 kHz

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Fig. 4 Input controlled voltage

Fig. 5 Output controlled voltage

Fig. 6 Inductor current

increased ripple at the input side is mainly due to the capacitor value taken to be 100 µF. It can be seen that the input as well as the output voltages are as required along with the inductor current, which is also within the range.

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6 Conclusion The generalized average modelling technique is applied to the DAB converter including the input parameter so as to employ the feedforward as well as feedback controlling method. The bidirectional converter property makes it greatly used in the applications where the input as well as output side voltages need to be controlled, as incase of solar arrays. The small-signal approximation used here includes the DC component as well as fundamental harmonic component to capture the alternating behaviour of the inductor current. The modelling is done and it is found that the input side parameter will affect the overall system dynamics and the voltages are controlled. Two PI controllers are used here for controlling both side voltages, and the simulated results are presented.

References 1. De Doncker, R.W.A.A., Divan, D.M., Kheraluwala, M.H.: A three-phase soft-switched highpower-density DC/DC converter for high-power applications. IEEE Trans. Ind. Appl. 27(1), 63–73 (1991) 2. Kheraluwala, M.H., Gasgoigne, R.W., Divan, D.M., Bauman, E.: Performance characterization of a high power dual active bridge DC/DC converter. In: Industry Applications Society Annual Meeting, 1990, Conference Record of the 1990 IEEE, pp. 1267–1273. IEEE (1990) 3. Demetriades, G.D., Nee, H.-P.: Characterization of the dual-active bridge topology for highpower applications employing a duty-cycle modulation. In: Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, pp. 2791–2798. IEEE (2008) 4. Mi, C., Bai, H., Wang, C., Gargies, S.: Operation, design and control of dual H-bridge-based isolated bidirectional DC-DC converter. IET Power Electron. 1(4), 507–517 (2008) 5. Bai, H., Mi, C., Wang, C., Gargies, S.: The dynamic model and hybrid phase-shift control of a dual-active-bridge converter. In: Industrial Electronics, 2008. IECON 2008. 34th Annual Conference of IEEE, pp. 2840–2845. IEEE (2008) 6. Demetriades, G.D., Nee, H.-P.: Dynamic modeling of the dual-active bridge topology for highpower applications. In: Power Electronics Specialists Conference, 2008. PESC 2008. IEEE, pp. 457–464. IEEE (2008) 7. Krismer, F., Kolar, J.W.: Accurate small-signal model for the digital control of an automotive bidirectional dual active bridge. IEEE Trans. Power Electron. 24(12), 2756–2768 (2009) 8. Krismer, F., Kolar, J.W.: Closed form solution for minimum conduction loss modulation of DAB converters. IEEE Trans. Power Electron. 27(1), 174–188 (2012) 9. Naayagi, R.T., Forsyth, A.J., Shuttleworth, R.: High-power bidirectional DC–DC converter for aerospace applications. IEEE Trans. Power Electron. 27(11), 4366–4379 (2012) 10. Bai, H., Nie, Z., Mi, C.C.: Experimental comparison of traditional phase-shift, dual-phaseshift, and model-based control of isolated bidirectional DC–DC converters. IEEE Trans. Power Electron. 25(6), 1444–1449 (2010) 11. Bai, H., Mi, C.: Eliminate reactive power and increase system efficiency of isolated bidirectional dual-active-bridge DC-DC converters using novel dual-phase-shift control. IEEE Trans. Power Electron. 23(6), 2905–2914 (2008) 12. Sanders, S.R., Mark Noworolski, J., Liu, X.Z., Verghese, G.C.: Generalized averaging method for power conversion circuits. IEEE Trans. Power Electron. 6(2), 251–259 (1991) 13. Qin, H., Kimball, J.W.: Generalized average modeling of dual active bridge DC-DC converter. IEEE Trans. Power Electron. 27(4), 2078–2084 (2012)

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14. Mueller, J.A., Kimball, J.: An improved generalized average model of DC-DC dual active bridge converters. IEEE Trans. Power Electron. (2018) 15. Kazimierczuk, M.K., Massarini, A.: Feedforward control of DC-DC PWM boost converter. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 44(2), 143–148 (1997)

Performance Assessment and Parametric Study of Multiple Effect Evaporator Pranaynil Saikia, Soundaram Ramanathan, and Dibakar Rakshit

1 Introduction Reverse Osmosis (RO) rejects are treated in multiple effect evaporators (MEE) in zero liquid discharge (ZLD) wastewater treatment systems. MEE is used to evaporate water from RO rejects and concentrate the rejects before sending it to a centrifuge in ZLD wastewater treatment systems for salt separation. MEE utilizes steam of temperature of 120–200 °C to evaporate wastewater [1]. Thermal energy input for the device can be sourced from solar concentrators. In the present study, a simulation tool has been proposed using an analysis based design to estimate the gross area required for the solar concentrator to operate MEE and also to study the daily performance analysis using energy conservation laws. Based on the model parametric analysis was conducted following which optimization dealing with parameters for operating MEE was performed with the objective of maximizing exergy. Unlike traditional boilers where heat is supplied to vaporize products, in single-effect evaporator systems steam provides energy for vaporization and the vapor product is condensed and removed from the system, while in multiple effect evaporator, the vapor product of the previous effect is used to provide energy for a next vaporization unit and the process continues for subsequent effects. MEE has been traditionally used for desalination. However, influence of different wastewater parameters on steam requirements and its effect on the operation of the system is comprehensively unavailable. This study has attempted to summarize it, also it has optimized energy solutions for solar-assisted MEE. In the present study, the evaporator is connected to a solar thermal concentrator (Fig. 1). P. Saikia · D. Rakshit (B) Centre for Energy Studies, Indian Institute of Technology Delhi, Delhi, New Delhi 110016, India e-mail: [email protected] S. Ramanathan Centre for Science and Environment, Delhi, New Delhi, India © Springer Nature Singapore Pte Ltd. 2021 M. Bose and A. Modi (eds.), Proceedings of the 7th International Conference on Advances in Energy Research, Springer Proceedings in Energy, https://doi.org/10.1007/978-981-15-5955-6_52

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Fig. 1 Solar assisted multiple effect evaporator

2 Simulation Tool and Algorithm A simulation tool was designed to excel visual basic application (VBA) which can estimate the area required for solar concentrator and the daily performance of the device with varying solar irradiance. The listed eleven parameters are taken as the model inputs—steam temperature at the inlet in the first effect (T s1 ), steam temperature at the outlet in the last effect (T sn ), number of effects (n), feed flow rate (mf ), concentration of the effluent (wastewater) at the inlet in the first effect (X 1 ) and at the outlet in the last effect (X 2 ), heat transfer coefficient of the first effect evaporator (U D ), optical efficiency of the solar concentrator, concentration ratio (CR), top loss coefficient of the solar concentrator’s receiver tube and the city (for solar irradiance). The basic principle behind the tool is energy balance. Multiple effect evaporators were designed first and corresponding to the useful heat requirement of the first effect of the MEE, area required for solar concentrator was determined. For the present mathematical modeling the flow is assumed to be in steady and uniform. The fluids are assumed to be isotropic and homogeneous. To design the interconnected series of evaporator, the heat transfer (Q) happening in each of the effects were assumed to be equal [2], Q1 = Q2 = Qn

(1)

Sub-indices 1, 2, n denote properties such as temperature, area, etc. of Effect (I), Effect (II), Effect (n) of MEE, s denotes steam, B denotes effluent to be concentrated.

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U D1 A1 (Ts1 − TB1 ) = U D2 A2 (Ts2 − TB2 ) = U Dn An (Tsn − TBn )

(2)

U D1 A1 T1 = U D2 A2 T2 = U Dn An Tn

(3)

Q of the n effects was determined following the below steps. Step 1: Determination of T n . Assuming area of the heat transfer in all of the evaporators to be identical the above equation gets reduced to Eq. (4). U D1 T1 = U D2 T2 = U Dn Tn

(4)

From Earle [3], in each effect the heat transfer coefficient degrades by approximately 5 percent consequently, i.e. U Dn = 0.95 × U Dn−1

(5)

Also by having a knowledge on the steam inlet temperature and its vapor temperature at the final effect/chamber/evaporator the T n can be estimated as T1 + T2 + · · · + Tn = Ts1 − Tsn

(6)

   Tn 1 + (Un /U1 ) + · · · + Un /U(n−1) = Ts1 − Tsn

(7)

Using Eqs. (5) and (7), T n of the ‘n’ effects were determined. Step 2: Steam temperature in each effect (Tsn ). Based on T 1 , T n , T s1 , T sn the temperature of steam inlet and outlet in the ‘n’ effects were determined. Step 3: Calculation of latent heat of vaporization of the effluent. Since the water has salt contaminants the latent heat of vaporization has been taken from the empirical relations given by Sharqawy et al. [4]. This equation considers other thermal properties of the salt contaminants which influence the latent heat rate. λs = a(1) + a(2) × Tsn + a(3) × Tsn2 + a(4) × Tsn3 + a(5) × Tsn4

(8)

h f = λs × (1 − 0.001 × S)

(9)

The equation is valid for the range 0 < T sn < 2