Smart Grids for Smart Cities, Volume 1: Real-Time Applications in Smart Cities 1119872073, 9781119872078

Table of contents :
Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Carbon-Free Fuel and the Social Gap: The Analysis
1.1 Introduction
1.2 Objectives
1.3 Study Areas
1.3.1 Community A
1.3.2 Community B
1.3.3 Community C
1.3.4 Community D
1.4 Data Collection
1.5 Data Analysis
1.6 Conclusion
References
Chapter 2 Opportunities of Translating Mobile Base Transceiver Station (BTS) for EV Charging Through Energy Management Systems in DC Microgrid
2.1 Introduction
2.1.1 Telecom Sector in India
2.1.2 Overview of Base Transceiver Station (BTS)
2.1.3 Electric Vehicle in India
2.1.4 Evolution of EV Charging Station
2.2 Translating Mobile Base Transceiver Station (BTS) for EV Charging
2.2.1 Mobile Base Transceiver Station (BTS) for EV Charging – A Substitute or Complementary Solution?
2.2.2 Proposed Methodology
2.2.3 System Description
2.2.3.1 Solar PV Array
2.2.3.2 DC-DC Boost Converter
2.2.3.3 Rectifier
2.2.3.4 Battery Backup System
2.2.3.5 Charge Controller
2.2.3.6 Bidirectional Converter
2.3 Implementation of Energy Management System in Base Transceiver Station (BTS)
2.3.1 Introduction
2.3.2 Control Strategies
2.3.2.1 MPPT Control
2.3.2.2 Charge Controller Control
2.3.2.3 Bidirectional Converter Control
2.3.3 Power Supervisory and Control Algorithm (PSCA)
2.3.3.1 Grid Available Mode
2.3.3.2 Grid Fault Mode
2.3.4 Results and Discussions
2.3.4.1 Grid Available Mode
2.3.4.2 Grid Failure Mode
2.4 Conclusion
References
Chapter 3 A Review on Advanced Control Techniques for Multi-Input Power Converters for Various Applications
3.1 Introduction
3.2 Multi-Input Magnetically Connected Power Converters
3.2.1 Dual-Source Power DC to DC Converter with Buck-Boost Arrangement
3.2.2 Bidirectional Multi-Input Arrangement
3.2.3 Full-Bridge Boost DC-DC Converter Formation
3.2.4 Multi-Input Power Converter with Half-Bridge and Full Bridge Configuration
3.3 Electrically Coupled Multi-Input Power DC-DC Converters
3.3.1 Combination of Electrically Linked Multi-Input DC/DC Power Converter
3.3.2 Multi-Input Power Converters in Series or Parallel Connection
3.3.3 Multi-Input DC/DC Fundamental Power Converters
3.3.4 Multiple-Input Boost Converter for RES
3.3.5 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter
3.3.6 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter
3.3.7 Multi-Input DC/DC Converter Using ZVS (Zero Voltage Switching)
3.3.8 Multi-Input DC-DC Converter Based Three Switches Leg
3.3.9 Multi-Input Converter Constructed on Switched Inductor/Switched Capacitor/Diode Capacitor
3.3.10 High/Modular VTR Multi-Input Converters
3.3.11 Multi/Input and Multi/Output (MIMO) Power Converter
3.4 Electro Magnetically Coupled Multi-Input Power DC/DC Converters
3.4.1 Direct Charge Multi-Input DC/DC Power Converter
3.4.2 Boost-Integrated Full-Bridge DC-DC Power Converter
3.4.3 Isolated Dual-Port Power Converter for Immediate Power Management
3.4.4 Dual Port Converter with Non-Isolated and Isolated Ports
3.4.5 Multi-Port ZVS And ZCS DC-DC Converter
3.4.6 Combined DC-Link and Magnetically Coupled DC/DC Power Converter
3.4.7 Three-Level Dual-Input DC-DC Converter
3.4.8 Half-Bridge Tri-Modal DC-DC Converter
3.4.9 Bidirectional Converter with Various Collective Battery Storage Input Sources
3.5 Different Control Methods Used in Multi-Input DC-DC Power Converters
3.5.1 Proportional Integral Derivation Controller (PID)
3.5.2 Model Predictive Control Method (MPC)
3.5.3 State Space Modelling (SSM)
3.5.4 Fuzzy Logic Control (FLC)
3.5.5 Sliding Mode Control (SMC)
3.6 Comparison and Future Scope of Work
3.6.1 Comparison and Discussion
3.7 Conclusion
References
Chapter 4 Case Study: Optimized LT Cable Sizing for an IT Campus
Abbreviations
4.1 Introduction
4.2 Methodology
4.2.1 Algorithm for Cable Sizing
4.3 Results and Discussion
4.3.1 Feeder Schedule
4.3.2 Design Consideration for LT Power Cable
4.3.3 Cable Sizing & Voltage Drop Calculation
4.4 Conclusion
References
Chapter 5 Advanced Control Architecture for Interlinking Converter in Autonomous AC, DC and Hybrid AC/DC Micro Grids
5.1 Introduction
5.2 Prototype Model of IC
5.3 Implemented Photo Voltaic System
5.4 Highly Reliable and Efficient (HRE) Configurations
5.5 MATLAB Simulink Results
5.6 Conclusion
References
Chapter 6 Optimal Power Flow Analysis in Distributed Grid Connected Photovoltaic Systems
6.1 Introduction
6.2 System Development and Design Parameters
6.3 Proposed Algorithm
6.4 Results and Discussion
6.5 Conclusion
References
Chapter 7 Reliability Assessment for Solar and Wind Renewable Energy in Generation System Planning
7.1 Introduction
7.2 Generation & Load Model
7.2.1 Generation Model-RBTS
7.2.2 Wind Power Generation Model
7.2.2.1 Wind Speed and Wind Turbine Output Model
7.2.3 Solar Power Generation Model
7.2.3.1 Solar Radiation and Solar Power Output Model
7.2.4 Load Model
7.3 Results and Analysis
7.3.1 Reliability Indices Evaluation for Different Scenario
7.4 Conclusion
References
Chapter 8 Implementation of Savonius Blad Wind Tree Structure by Super Lift Luo Converter for Smart Grid Applications and Benefits to Smart City
8.1 Introduction
8.2 Savonius Wind Turbine – Performance Design
8.3 Design Modules
8.4 Results and Discussion
8.5 Positive Output Super Lift Luo Converter
8.6 Conclusion
References
Chapter 9 Analysis: An Incorporation of PV and Battery for DC Scattered System
9.1 Introduction
9.2 Block Diagram of Proposed System
9.2.1 Determine the Load Profile
9.2.2 Duration of Autonomy and Recharge
9.2.3 Select the Battery Rating
9.2.4 Sizing the PV Array
9.2.5 Analysis of Boost Converter
9.2.5.1 To Select a Proper Inductor Value
9.2.5.2 To Select a Proper Capacitor Value
9.3 Proposed System Simulations
9.4 Conclusion
References
Chapter 10 Dead Time Compensation Scheme Using Space Vector PWM for 3Ø Inverter
10.1 Introduction
10.2 Concept of Space Vector PWM
10.3 Proteus Simulation
10.4 Hardware Setup
10.4.1 Total Harmonic Distortion
10.4.2 Hardware Configuration
10.5 Conclusion
References
Chapter 11 Transformer-Less Grid Connected PV System Using TSRPWM Strategy with Single Phase 7 Level Multi-Level Inverter
11.1 Introduction
11.2 Proposed System
11.3 DC-DC Influence Converter
11.4 Controlling of 7-Level Inverter
11.5 Controlling for Boost Converter and Inverter
11.6 MATLAB Simulation Results
11.7 Conclusion
References
Chapter 12 An Enhanced Multi-Level Inverter Topology for HEV Applications
12.1 Introduction
12.2 E-MLI Topology
12.2.1 Switching Operation of the E-MLI Topology
12.2.2 Diode-Clamped Multi-Level Inverter (DC-MLI)
12.3 PWM for the E-MLI Topology
12.3.1 SPWM Based Switching for the E-MLI Topology
12.3.2 Phase Opposition Disposition (POD) Scheme for DC-MLI
12.4 Simulation Results & Discussions
12.5 Conclusion
References
Chapter 13 Improved Sheep Flock Heredity Algorithm-Based Optimal Pricing of RP
13.1 Introduction
13.2 RP Flow Tracing
13.2.1 Intent Function
13.2.1.1 System’s Price Loss After RP Compensation
13.2.1.2 SVC Support Price for RP
13.2.1.3 Diesel Generator RP Production Price
13.2.1.4 Minimization Function
13.3 Existing Methodologies
13.3.1 Particle Swarm Optimization (PSO)
13.3.1.1 PSO Parameter Settings
13.3.2 Hybrid Particle Swarm Optimization (HPSO)
13.3.2.1 Flowchart for HPSO
13.4 Proposed Methodology
13.4.1 Improved Sheep Flock Heredity Algorithm
13.4.2 ISFHA Algorithm
13.5 Case Study
13.5.1 Realistic Seventy-Five Bus Indian System Wind Farm
13.6 Conclusion
References
Chapter 14 Dual Axis Solar Tracking with Weather Monitoring System by Using IR and LDR Sensors with Arduino UNO
14.1 Introduction
14.2 Associated Hardware Components Details
14.2.1 Arduino Uno
14.2.2 L293D Motor Driver
14.2.3 LDR Sensor
14.2.4 Solar Panel
14.2.5 RPM 10 Motor
14.2.6 Jumper Wires
14.2.7 16×2 LCD (Liquid Crystal Display) Module with I2C
14.2.8 DTH11 Sensor
14.2.9 Rain Drop Sensor
14.3 Methodology
14.3.1 Dual Axis Solar Tracking System Working Model
14.3.2 Dual Axis Solar Tracking System Schematic Diagram
14.4 Results and Discussion
14.5 Conclusion
References
Chapter 15 Missing Data Imputation of an Off-Grid Solar Power Model for a Small-Scale System
Abbreviations and Nomenclature
15.1 Overview
15.2 Literature Review
15.3 AI/ML for Imputation of Missing Values
15.3.1 CBR
15.3.2 MICE
15.3.3 Results and Discussion
15.3.3.1 Data Collection
15.3.3.2 Error Metrics
15.3.3.3 Comparison Between CBR and MICE
15.4 Applications of MICE in Imputation
15.5 Summary
References
Chapter 16 Power Theft in Smart Grids and Microgrids: Mini Review
16.1 Introduction
16.2 Smart Grids/Microgrids Security Threats and Challenges
16.2.1 Security Threats to Smart Grid/Microgrid by Classification of Sources
16.2.1.1 Smart Grid/Microgrid Threats Sources in Technical Point of View
16.2.2 Sources of Smart Grids/Microgrids Threats in Non-Technical Point of View
16.2.2.1 Security of Environment
16.2.2.2 Regulatory Policies of Government
16.3 Conclusion
References
Chapter 17 Isolated SEPIC-Based DC-DC Converter for Solar Applications
17.1 Introduction
17.2 Converter Operation and Analysis
17.2.1 Mode A
17.2.2 Mode B
17.3 Design Equations
17.4 Simulation Results
17.5 Conclusion
References
Chapter 18 Hybrid Converter for Stand-Alone Solar Photovoltaic System
18.1 Introduction
18.2 Review on Converter Topology
18.3 Block Diagram
18.4 Existing Converter Topology
18.5 Proposed Tapped Boost Hybrid Converter
18.5.1 Novelty in the Circuit
18.5.2 Converter Modes of Operation
18.6 Derivation Part of Tapped Boost Hybrid Converter
18.6.1 Voltage Gain
18.6.2 Modulation Index
18.7 Design Specification of the Converter
18.8 Simulation Results for Both DC and AC Power Conversion
18.9 Hardware Results
18.10 TBHC Parameters for Simulation
18.11 Conclusion
References
Chapter 19 Analysis of Three-Phase Quasi Switched Boost Inverter Based on Switched Inductor-Switched Capacitor Structure
19.1 Introduction
19.1.1 Conventional Inverter (VSI)
19.1.2 Z-Source Inverter (ZSI)
19.1.3 SBI Based on SL-SC Structure
19.2 Working Modes of Three-Phase SL-SC Circuit
19.2.1 Shoot-Through State
19.2.2 Non-Shoot-Through State
19.3 Design of Three-Phase SL-SC Based Quasi Switched Boost Inverter
19.3.1 Steady State Analysis of SL-SC Topology
19.3.2 Design of Passive Elements
19.3.3 Design Equations
19.3.4 Design Specifications
19.4 Simulation Results and Discussions
19.4.1 Simulation Diagram of SBC PWM Technique
19.4.2 SBC PWM Technique
19.4.3 Switching Pulse Generated for the Power Switches
19.4.4 Expanded Switching Pulse
19.4.5 Input Current
19.4.6 Current in Inductor L1
19.4.7 Current in Inductor L2
19.4.8 Capacitor Voltage VC2
19.4.9 DC Link Voltage
19.4.10 Output Load Voltage
19.4.11 Output Load Current
19.5 Performance Analysis
19.6 Conclusion
References
Chapter 20 Power Quality Improvement and Performance Enhancement of Distribution System Using D-STATCOM
20.1 Introduction
20.2 Distribution Static Synchronous Compensator (D-STATCOM)
20.3 Modelling of Distribution System
20.3.1 Single Machine System
20.3.2 Modeling of IEEE 14 Bus System
20.4 Simulation Results & Discussions
20.4.1 Power Flow Analysis on Single Machine System
20.4.2 Different Modes of Operation of D-STATCOM on Single Machine System
20.4.3 Step Change in Reference Value of DC Link Voltage
20.5 IEEE-14 Bus Systems
20.6 Conclusion
References
Index
EULA

Citation preview

Smart Grids for Smart Cities Volume 1

Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

Smart Grids for Smart Cities Volume 1

Edited by

O.V. Gnana Swathika K. Karthikeyan and

Sanjeevikumar Padmanaban

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2023 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep­ resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-­ ability or fitness for a particular purpose. No warranty may be created or extended by sales representa­ tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa­ tion does not mean that the publisher and authors endorse the information or services the organiza­ tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 9781119872078 Front cover image: Pixabay.com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1

Contents Preface xvii 1 Carbon-Free Fuel and the Social Gap: The Analysis Saravanan Chinnusamy, Milind Shrinivas Dangate and Nasrin I. Shaikh 1.1 Introduction 1.2 Objectives 1.3 Study Areas 1.3.1 Community A 1.3.2 Community B 1.3.3 Community C 1.3.4 Community D 1.4 Data Collection 1.5 Data Analysis 1.6 Conclusion References 2 Opportunities of Translating Mobile Base Transceiver Station (BTS) for EV Charging Through Energy Management Systems in DC Microgrid A. Matheswaran, P. Prem, C. Ganesh Babu and K. Lakshmi 2.1 Introduction 2.1.1 Telecom Sector in India 2.1.2 Overview of Base Transceiver Station (BTS) 2.1.3 Electric Vehicle in India 2.1.4 Evolution of EV Charging Station 2.2 Translating Mobile Base Transceiver Station (BTS) for EV Charging 2.2.1 Mobile Base Transceiver Station (BTS) for EV Charging – A Substitute or Complementary Solution?

1 2 3 3 4 4 5 5 6 9 10 13

15 16 16 17 19 21 21 21 v

vi  Contents 2.2.2 Proposed Methodology 2.2.3 System Description 2.2.3.1 Solar PV Array 2.2.3.2 DC-DC Boost Converter 2.2.3.3 Rectifier 2.2.3.4 Battery Backup System 2.2.3.5 Charge Controller 2.2.3.6 Bidirectional Converter 2.3 Implementation of Energy Management System in Base Transceiver Station (BTS) 2.3.1 Introduction 2.3.2 Control Strategies 2.3.2.1 MPPT Control 2.3.2.2 Charge Controller Control 2.3.2.3 Bidirectional Converter Control 2.3.3 Power Supervisory and Control Algorithm (PSCA) 2.3.3.1 Grid Available Mode 2.3.3.2 Grid Fault Mode 2.3.4 Results and Discussions 2.3.4.1 Grid Available Mode 2.3.4.2 Grid Failure Mode 2.4 Conclusion References

23 24 24 25 25 26 27 28 29 29 30 31 31 32 33 33 33 35 35 35 35 38

3 A Review on Advanced Control Techniques for Multi-Input Power Converters for Various Applications 41 Kodada Durga Priyanka and Abitha Memala Wilson Duraisamy 3.1 Introduction 42 3.2 Multi-Input Magnetically Connected Power Converters 46 3.2.1 Dual-Source Power DC to DC Converter with Buck-Boost Arrangement 46 3.2.2 Bidirectional Multi-Input Arrangement 47 3.2.3 Full-Bridge Boost DC-DC Converter Formation 48 3.2.4 Multi-Input Power Converter with Half-Bridge and Full Bridge Configuration 49 3.3 Electrically Coupled Multi-Input Power DC-DC Converters 50 3.3.1 Combination of Electrically Linked Multi-Input DC/DC Power Converter 50 3.3.2 Multi-Input Power Converters in Series or Parallel Connection 51 3.3.3 Multi-Input DC/DC Fundamental Power Converters 52

Contents  vii 3.3.4 Multiple-Input Boost Converter for RES 53 3.3.5 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter 54 3.3.6 Multi-Input Buck-Boost/Buck/Boost-Boost Based Converter 55 3.3.7 Multi-Input DC/DC Converter Using ZVS (Zero Voltage Switching) 57 3.3.8 Multi-Input DC-DC Converter Based Three Switches Leg 57 3.3.9 Multi-Input Converter Constructed on Switched Inductor/Switched Capacitor/Diode Capacitor 58 3.3.10 High/Modular VTR Multi-Input Converters 59 3.3.11 Multi/Input and Multi/Output (MIMO) Power Converter 60 3.4 Electro Magnetically Coupled Multi-Input Power DC/DC Converters 61 3.4.1 Direct Charge Multi-Input DC/DC Power Converter 61 3.4.2 Boost-Integrated Full-Bridge DC-DC Power Converter 62 3.4.3 Isolated Dual-Port Power Converter for Immediate Power Management 63 3.4.4 Dual Port Converter with Non-Isolated and Isolated Ports 63 3.4.5 Multi-Port ZVS And ZCS DC-DC Converter 64 3.4.6 Combined DC-Link and Magnetically Coupled DC/DC Power Converter 65 3.4.7 Three-Level Dual-Input DC-DC Converter 65 3.4.8 Half-Bridge Tri-Modal DC-DC Converter 66 3.4.9 Bidirectional Converter with Various Collective Battery Storage Input Sources 75 3.5 Different Control Methods Used in Multi-Input DC-DC Power Converters 75 3.5.1 Proportional Integral Derivation Controller (PID) 76 3.5.2 Model Predictive Control Method (MPC) 77 3.5.3 State Space Modelling (SSM) 78 3.5.4 Fuzzy Logic Control (FLC) 79 3.5.5 Sliding Mode Control (SMC) 80 3.6 Comparison and Future Scope of Work 82 3.6.1 Comparison and Discussion 82 3.7 Conclusion 85 References 86

viii  Contents 4 Case Study: Optimized LT Cable Sizing for an IT Campus 101 O.V. Gnana Swathika, K. Karthikeyan, Umashankar Subramaniam and K.T.M.U. Hemapala Abbreviations 102 4.1 Introduction 102 4.2 Methodology 103 4.2.1 Algorithm for Cable Sizing 103 4.3 Results and Discussion 103 4.3.1 Feeder Schedule 104 4.3.2 Design Consideration for LT Power Cable 104 4.3.3 Cable Sizing & Voltage Drop Calculation 107 4.4 Conclusion 114 References 114 5 Advanced Control Architecture for Interlinking Converter in Autonomous AC, DC and Hybrid AC/DC Micro Grids M. Padma Lalitha, S. Suresh and A. Viswa Pavani 5.1 Introduction 5.2 Prototype Model of IC 5.3 Implemented Photo Voltaic System 5.4 Highly Reliable and Efficient (HRE) Configurations 5.5 MATLAB Simulink Results 5.6 Conclusion References 6 Optimal Power Flow Analysis in Distributed Grid Connected Photovoltaic Systems Neenu Thomas, T.N.P. Nambiar and Jayabarathi R. 6.1 Introduction 6.2 System Development and Design Parameters 6.3 Proposed Algorithm 6.4 Results and Discussion 6.5 Conclusion References 7 Reliability Assessment for Solar and Wind Renewable Energy in Generation System Planning S. Vinoth John Prakash and P.K. Dhal 7.1 Introduction 7.2 Generation & Load Model 7.2.1 Generation Model-RBTS

115 116 117 118 120 122 127 127 131 131 132 138 138 141 141 143 144 146 146

Contents  ix 7.2.2 Wind Power Generation Model 7.2.2.1 Wind Speed and Wind Turbine Output Model 7.2.3 Solar Power Generation Model 7.2.3.1 Solar Radiation and Solar Power Output Model 7.2.4 Load Model 7.3 Results and Analysis 7.3.1 Reliability Indices Evaluation for Different Scenario 7.4 Conclusion References 8 Implementation of Savonius Blad Wind Tree Structure by Super Lift Luo Converter for Smart Grid Applications and Benefits to Smart City Jency Joseph J., Anitha Mary X., Josh F. T., Vinoth Kumar K. and Vinodha K. 8.1 Introduction 8.2 Savonius Wind Turbine – Performance Design 8.3 Design Modules 8.4 Results and Discussion 8.5 Positive Output Super Lift Luo Converter 8.6 Conclusion References 9 Analysis: An Incorporation of PV and Battery for DC Scattered System M. Karuppiah, P. Dineshkumar, A. Arunbalaj and S. Krishnakumar 9.1 Introduction 9.2 Block Diagram of Proposed System 9.2.1 Determine the Load Profile 9.2.2 Duration of Autonomy and Recharge 9.2.3 Select the Battery Rating 9.2.4 Sizing the PV Array 9.2.5 Analysis of Boost Converter 9.2.5.1 To Select a Proper Inductor Value 9.2.5.2 To Select a Proper Capacitor Value 9.3 Proposed System Simulations 9.4 Conclusion References

147 147 150 150 152 152 153 155 156

159 160 160 163 167 170 171 172 175 176 179 180 180 181 182 184 187 187 188 192 193

x  Contents 10 Dead Time Compensation Scheme Using Space Vector PWM for 3Ø Inverter Sreeramula Reddy, Ravindra Prasad, Harinath Reddy and Suresh Srinivasan 10.1 Introduction 10.2 Concept of Space Vector PWM 10.3 Proteus Simulation 10.4 Hardware Setup 10.4.1 Total Harmonic Distortion 10.4.2 Hardware Configuration 10.5 Conclusion References 11 Transformer-Less Grid Connected PV System Using TSRPWM Strategy with Single Phase 7 Level Multi-Level Inverter S. Sruthi, K. Karthikumar, D. Narmitha, P. Chandra Sekhar and K. Karthi 11.1 Introduction 11.2 Proposed System 11.3 DC-DC Influence Converter 11.4 Controlling of 7-Level Inverter 11.5 Controlling for Boost Converter and Inverter 11.6 MATLAB Simulation Results 11.7 Conclusion References 12 An Enhanced Multi-Level Inverter Topology for HEV Applications Premkumar E. and Kanimozhi G. 12.1 Introduction 12.2 E-MLI Topology 12.2.1 Switching Operation of the E-MLI Topology 12.2.2 Diode-Clamped Multi-Level Inverter (DC-MLI) 12.3 PWM for the E-MLI Topology 12.3.1 SPWM Based Switching for the E-MLI Topology 12.3.2 Phase Opposition Disposition (POD) Scheme for DC-MLI 12.4 Simulation Results & Discussions 12.5 Conclusion References

195 195 197 200 201 206 209 210 211

213 214 215 216 218 221 221 224 225 227 227 228 229 232 233 234 234 236 249 249

Contents  xi 13 Improved Sheep Flock Heredity Algorithm-Based Optimal Pricing of RP 253 P. Booma Devi, Booma Jayapalan and A.P. Jagadeesan 13.1 Introduction 254 13.2 RP Flow Tracing 257 13.2.1 Intent Function 257 13.2.1.1 System’s Price Loss After RP Compensation 257 13.2.1.2 SVC Support Price for RP 258 13.2.1.3 Diesel Generator RP Production Price 258 13.2.1.4 Minimization Function 258 13.3 Existing Methodologies 259 13.3.1 Particle Swarm Optimization (PSO) 259 13.3.1.1 PSO Parameter Settings 259 13.3.2 Hybrid Particle Swarm Optimization (HPSO) 260 13.3.2.1 Flowchart for HPSO 260 13.4 Proposed Methodology 261 13.4.1 Improved Sheep Flock Heredity Algorithm 261 13.4.2 ISFHA Algorithm 263 13.5 Case Study 263 13.5.1 Realistic Seventy-Five Bus Indian System Wind Farm 263 13.6 Conclusion 266 References 267 14 Dual Axis Solar Tracking with Weather Monitoring System by Using IR and LDR Sensors with Arduino UNO Rajesh Babu Damala and Rajesh Kumar Patnaik 14.1 Introduction 14.2 Associated Hardware Components Details 14.2.1 Arduino Uno 14.2.2 L293D Motor Driver 14.2.3 LDR Sensor 14.2.4 Solar Panel 14.2.5 RPM 10 Motor 14.2.6 Jumper Wires 14.2.7 16×2 LCD (Liquid Crystal Display) Module with I2C 14.2.8 DTH11 Sensor 14.2.9 Rain Drop Sensor 14.3 Methodology

269 269 270 270 271 272 273 274 274 275 276 276 277

xii  Contents 14.3.1 Dual Axis Solar Tracking System Working Model 277 14.3.2 Dual Axis Solar Tracking System Schematic Diagram 279 14.4 Results and Discussion 279 14.5 Conclusion 281 References 282 15 Missing Data Imputation of an Off-Grid Solar Power Model for a Small-Scale System Aadyasha Patel, Aniket Biswal and O.V. Gnana Swathika Abbreviations and Nomenclature 15.1 Overview 15.2 Literature Review 15.3 AI/ML for Imputation of Missing Values 15.3.1 CBR 15.3.2 MICE 15.3.3 Results and Discussion 15.3.3.1 Data Collection 15.3.3.2 Error Metrics 15.3.3.3 Comparison Between CBR and MICE 15.4 Applications of MICE in Imputation 15.5 Summary References 16 Power Theft in Smart Grids and Microgrids: Mini Review P. Tejaswi and O.V. Gnana Swathika 16.1 Introduction 16.2 Smart Grids/Microgrids Security Threats and Challenges 16.2.1 Security Threats to Smart Grid/Microgrid by Classification of Sources 16.2.1.1 Smart Grid/Microgrid Threats Sources in Technical Point of View 16.2.2 Sources of Smart Grids/Microgrids Threats in Non-Technical Point of View 16.2.2.1 Security of Environment 16.2.2.2 Regulatory Policies of Government 16.3 Conclusion References

285 286 286 287 288 288 290 291 291 292 293 296 296 297 299 299 300 301 302 304 304 304 304 304

Contents  xiii 17 Isolated SEPIC-Based DC-DC Converter for Solar Applications 309 Varun Mukesh Lal, Pranay Singh Parihar and Kanimozhi. G 17.1 Introduction 309 17.2 Converter Operation and Analysis 311 17.2.1 Mode A 311 17.2.2 Mode B 313 17.3 Design Equations 314 17.4 Simulation Results 316 17.5 Conclusion 321 References 321 18 Hybrid Converter for Stand-Alone Solar Photovoltaic System R.R. Rubia Gandhi and C. Kathirvel 18.1 Introduction 18.2 Review on Converter Topology 18.3 Block Diagram 18.4 Existing Converter Topology 18.5 Proposed Tapped Boost Hybrid Converter 18.5.1 Novelty in the Circuit 18.5.2 Converter Modes of Operation 18.6 Derivation Part of Tapped Boost Hybrid Converter 18.6.1 Voltage Gain 18.6.2 Modulation Index 18.7 Design Specification of the Converter 18.8 Simulation Results for Both DC and AC Power Conversion 18.9 Hardware Results 18.10 TBHC Parameters for Simulation 18.11 Conclusion References 19 Analysis of Three-Phase Quasi Switched Boost Inverter Based on Switched Inductor-Switched Capacitor Structure P. Sriramalakshmi, Vachan Kumar, Pallav Pant and Reshab Kumar Sahoo 19.1 Introduction 19.1.1 Conventional Inverter (VSI) 19.1.2 Z-Source Inverter (ZSI) 19.1.3 SBI Based on SL-SC Structure

323 324 324 325 326 326 327 327 327 328 328 329 330 330 332 334 334 337 337 339 339 340

xiv  Contents 19.2 Working Modes of Three-Phase SL-SC Circuit 19.2.1 Shoot-Through State 19.2.2 Non-Shoot-Through State 19.3 Design of Three-Phase SL-SC Based Quasi Switched Boost Inverter 19.3.1 Steady State Analysis of SL-SC Topology 19.3.2 Design of Passive Elements 19.3.3 Design Equations 19.3.4 Design Specifications 19.4 Simulation Results and Discussions 19.4.1 Simulation Diagram of SBC PWM Technique 19.4.2 SBC PWM Technique 19.4.3 Switching Pulse Generated for the Power Switches 19.4.4 Expanded Switching Pulse 19.4.5 Input Current 19.4.6 Current in Inductor L1 19.4.7 Current in Inductor L2 19.4.8 Capacitor Voltage VC2 19.4.9 DC Link Voltage 19.4.10 Output Load Voltage 19.4.11 Output Load Current 19.5 Performance Analysis 19.6 Conclusion References

341 341 342 342 342 344 344 344 344 344 345 347 348 348 349 349 350 350 351 351 351 353 354

20 Power Quality Improvement and Performance Enhancement of Distribution System Using D-STATCOM 357 M. Sai Sandeep, N. Balaji, Muqthiar Ali and Suresh Srinivasan 20.1 Introduction 358 20.2 Distribution Static Synchronous Compensator (D-STATCOM) 360 20.3 Modelling of Distribution System 361 20.3.1 Single Machine System 361 20.3.2 Modeling of IEEE 14 Bus System 362 20.4 Simulation Results & Discussions 363 20.4.1 Power Flow Analysis on Single Machine System 363 20.4.2 Different Modes of Operation of D-STATCOM on Single Machine System 365 20.4.3 Step Change in Reference Value of DC Link Voltage 368

Contents  xv 20.5 IEEE-14 Bus Systems 20.6 Conclusion References

370 374 374

Index 377

Preface

What makes a regular electric grid a “smart” grid? It comes down to digital technologies that enable “two-way communication between the utility and its customers, and the sensing along the transmission lines,” according to SmartGrid.gov. Based on statistics and available research, even though Internet of Things (IoT) is the talk of the town, smart grids globally attract the largest investment venues in smart cities. Smart grids and city buildings that are connected in smart cities contribute to significant financial savings and contribute to improve the country economy globally. The smart grid evolves around its efficient portfolio in the forte of how and when to utilize electricity and other forms of energy. Smart Grids vastly involve IoT sensors and real-time communication features that contribute to control loads based on available supply and peak demand characteristics. Phenomenal research and deployment is witnessed in the area of smart meters enabled smart cities. In the traditional electrical grid, “power flows in one direction — from centralized generation facilities, through transmission lines, and finally to the customer via distribution utilities.” The smart grid has a multitude of components, including controls, computers, automation, and new technologies and equipment working together. Also these technologies will work in conjunction with the electrical grid to respond digitally to our quickly changing electric demand. The investment in smart grid technology also has certain challenges. Ideally the interconnected feature of smart grids is valuable but it tremendously increases their susceptibility to threats. Smart Grid stakeholders also agree on the fact that since numerous non-utility stakeholders and devices are connected to smart grids, even in the best conditions possible, the secure operations can no longer be guaranteed by a single organization or security department. It is crucial to make sure that smart grid is made secure wherein number of technologies are employed to increase the real-time situational awareness and the ability to support renewables and xvii

xviii  Preface system automation to increase the reliability, efficiency and safety of the electric grid. Various secure communications solutions are available for public utilities to contribute to the newest smart grid applications including  advanced metering infrastructure, distribution automation, voltage optimization and substation automation. 5 Salient Features: 1. Smart grids: Concepts, Challenges, Architecture, Standards, and Communication 2. Renewable Energy Systems (RES) enhanced smart grids 3. Smart Grid Applications and Benefits to Smart City 4. The synergy of Sustainability, ICT, and Urbanization in Smart Cities 5. Smart City: IoT, Cloud, Big Data Convergence and Wireless Networks

1 Carbon-Free Fuel and the Social Gap: The Analysis Saravanan Chinnusamy1, Milind Shrinivas Dangate1* and Nasrin I. Shaikh2† 1

Chemistry Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamilnadu, India 2 Department of Chemistry, Nowrosjee Wadia College, Pune, Maharashtra, India

Abstract

Many consider utility-scale photovoltaic solar power to be an essential component of decarbonizing the Indian power sector and mitigating climate change. This technology is well accepted by the public in general surveys, yet often faces local resistance during project siting. This phenomenon is known as the “social gap.” Using social gap theory from the wind energy literature as a foundation, this study examines the causes of and offers recommendations for addressing the solar social gap in Maharashtra. The study relied on 33 semi-structured interviews with citizens, government officials, and developers across four Maharashtra communities, each facing a prospective utility-scale solar project. Through thematic analysis, the study shows that the solar social gap can be attributed to both a vocal minority that dominated community sentiment and project proposals that failed to meet the community’s standards for acceptable development. The gap was exacerbated by the presence of organized opposition groups as well as decision-makers relying on ineffective public processes to engage citizens. This research makes it clear that government officials and developers need to adopt practices that enhance community representation, process transparency, and decision-influence. Though decisionmaking strategies are not the only factor that affects community acceptance, implementing improved procedures could help close the solar social gap. Keywords:  Renewable energy, carbon-free fuel, smart cities, solar cells, communication gap *Corresponding author: [email protected] † Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (1–14) © 2023 Scrivener Publishing LLC

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2  Smart Grids for Smart Cities Volume 1

1.1 Introduction Solar PV is undoubtedly a key player in the future of energy [1]. This technology continues to see cost reductions and is significantly contributing to new additions in generation capacity [2]. Utility-scale solar projects, i.e., ground-mounted systems that produce 50 MW of power or more for consumption by utility-users have a distinct competitive edge. As solar PV becomes increasingly attractive in the market, there will likely be a surge in development of large-scale solar arrays on what has been termed “subprime land” or land lacking one or more of the three prime requirements for development: solar resource potential, aesthetic buffers or distance from communities, and necessary grid capacity [3]. Maharashtra may already be experiencing this trend. Additionally, there is high national public acceptance for solar energy; over 80% of India supports its development, although, as we have learned from wind, favorable survey results do not always adequately reflect what is happening in reality. There has been documentation of community disapproval of solar developments in southern India; one researcher has even identified the solar social gap in that area [4]. These utility-scale solar farms have been scrutinized for intermittency, aesthetics, socioeconomic impacts, wildlife hazards, human health hazards, and cultural infringement [5]. This response may provide a glimpse into what is to come as large-scale solar farm proposals expand beyond the Sun Belt. Therefore, there is a need to study how the deployment of utility-scale solar farms in unprecedented areas are received by the public compared to hypothetical circumstances, i.e., the unfolding of a midwestern solar social gap. There are a limited number of studies that have examined the acceptance [1] of people living near large-scale solar farms or having experienced local solar development in the south. This may have been previously due to a lack of projects available to study; however, continued improvements are inviting more solar energy onto the grid which is creating new opportunities to capture the public’s reaction. [6] were among one of the first to seize this research potential. They performed a content analysis of newspapers to understand reasons for citizens’ support and opposition to solar projects in Gujarat and Rajasthan. My research will take a deeper dive into the Gujarat by using semi-structured interviews to examine community acceptance of and related decision-making processes for proposed utility-scale solar projects.

Carbon-Free Fuel and the Social Gap  3

1.2 Objectives The objectives of this research guided my inquiry and analysis to sufficiently identify and describe the various elements of the solar social gap. I attempted to set up the layout of my results and discussion to match the order of my objectives to demonstrate clear connections. The objectives of this research are as follows: i.

Determine public support or opposition, attitudes, perceptions, and values associated with utility-scale solar projects. ii. Analyze the solar social gap using [7] wind social gap determinants. iii. Investigate how government- and developer-led public engagement processes address or contribute to the solar social gap. iv. Identify best practices for public engagement in utilityscale solar project siting to help diminish the solar social gap.

1.3 Study Areas Four communities [8] in Maharashtra have been targeted to examine acceptance and procedures related to large-scale solar projects. The locations of these study sites are left unnamed to protect participants’ privacy. Instead, I will refer to the four communities as Community A, B, C, and D. I also redacted the site-specific references (e.g., media sources, public records, project websites) from this report as a further discretionary precaution. Site selection was based on what is already known about each community’s public response to a solar farm proposal, zoning level, and estimated project size. According to online news articles and public records, Communities A and C have yet to report much, if any, controversy regarding their projects (Redacted 3; Redacted 4), while Communities B and D have experienced notably contentious development processes (Redacted 2; Redacted 6). Within both groupings, there is one township that is zoned locally and one that is (or was) zoned at the county level. See Figure 1.1 for a visual. This case selection was done to achieve a more accurate representation of the views on and approaches to utility-scale solar [9]. Additionally,

4  Smart Grids for Smart Cities Volume 1 Remote Zone

A Community

B Community

Favorable

Disliked

C Community

D Community

City Zone

Figure 1.1  Matrix of study areas by zoning level and anticipated acceptance.

at the time of this writing, these projects would be the largest solar farms in Maharashtra.

1.3.1 Community A Community A consists of two townships, each housing less than 2,500 residents (Redacted 10; Redacted 12). Both townships are zoned at the county level. A special use permit was unanimously approved by the county planning commission to permit construction of a solar farm that will span over 1,000 acres and produce more than 200 MW of power. Based on information from the developer’s website, they worked closely with township residents to hear their thoughts and answer any questions that came up. They facilitated this discussion by hosting several community forums (Redacted 1). Overall, media accounts have claimed that the public has been receptive to this solar farm (Redacted 3). Even back when the project was first introduced to the area, there were few complaints from the residents (Redacted 5).

1.3.2 Community B Community B is a single township and home to just over 2,800 people (Redacted 8). This area was formerly county zoned until the prospects of solar development were introduced. The county established a large-scale solar ordinance and a developer subsequently submitted a proposal to

Carbon-Free Fuel and the Social Gap  5 build a solar array shy of 1,000 acres on rural land primarily in Community B (Redacted 6). Many of the township residents were reportedly unenthusiastic about the idea of living next to a large solar farm (Redacted 6). Further, township officials claimed that the solar array was not in accordance with their master plan (Redacted 6). In response, Community B moved to execute their right to self-zone and created an interim ordinance that would temporary block any large-scale solar development. The township’s actions caused the county to postpone consideration of the solar farm application [6]. The developer subsequently sued the township, and litigations are pending at the time of this writing. The proposed project will remain on hold until the township finalizes their zoning ordinance and settles matters in court.

1.3.3 Community C Community C has an estimated population of just over 2,100 (Redacted 11). This self-zoned municipality unanimously passed a solar energy ordinance several years back and has since approved multiple utility-scale solar projects collectively exceeding 1,000 acres. Both developers in Community C claimed to have used a similar public engagement approach as the developer in Community A (Redacted 7). Online news articles have not identified residents raising concerns or disapproval (Redacted 4).

1.3.4 Community D Community D has a population of roughly 3,400 residents and is locally zoned (Redacted 9). The township board initially approved a solar ordinance from which a developer proposed a utility-scale project that would cover nearly 1,000 acres. However, due to some technicalities, the original ordinance was not legal and had to be sent back to the planning commission for modifications (Redacted 2). At this point, the community began to get involved and significant opposition developed. The planning commission worked with the developer to tailor the logistics of the zoning amendment (and subsequent project design) to better balance community interests. For example, the original setback distance of 250 feet from residential areas was increased to 500 feet. Despite these changes, there remained strong public resistance. Regardless, the planning commission attempted to move forward and made a motion to recommend the zoning amendment to the township board. The amendment was denied by the board and sent back to the planning commission for further revisions (Redacted 2). There have

6  Smart Grids for Smart Cities Volume 1 been numerous additional meetings, but the ordinance has yet to be finalized. At the time of this writing, the project remains on standby.

1.4 Data Collection Qualitative methods were used to examine the solar social gap in these four research communities. Three groups within each of the four communities were targeted for semi- structured interviews: government officials, solar project developers, and nearby citizens. These groups were chosen to demonstrate perspectives of decision-makers and the public about both the solar projects and the specific public-engagement approaches used to develop them. A number of methods were used to recruit each group; however, no methods could or did involve in-person contact due to restrictions put in place as a result of the COVID-19 pandemic. TERI – The Energy and Resources Institutional Review Board – approved this research. For government officials, I tried to contact everyone involved in the decision-making of the project, e.g., township officials, zoning administrators, planning commissioners, board members, etc. If both county and township authorities were involved, I made attempts to talk to a representative from each level. Emails and phone numbers were found through the counties or townships’ websites. I connected with developers through emails or phone numbers that were made available online. Efforts were made to speak with one individual per project; this was most often the project manager. Citizens were the most difficult group to contact because their information was not as virtually accessible. Thus, we tried multiple tactics to contact people, including: i.

Scanned the meeting minutes of public hearings related to the solar projects and identified individuals that made comments. We reached out through Facebook Messenger if we could confidently locate someone’s profile. If not, we searched county parcel mapping websites to get their address and mailed them a letter. ii. Searched for Facebook groups linked with the solar projects and messaged contributors. iii. Drove through accessible communities and noted the addresses with “no solar” signs to later mail them a letter. iv. Emailed government clerks to request contact information for potential land-leasers and mailed them a letter.

Carbon-Free Fuel and the Social Gap  7 v. Identified the solar project site maps and overlaid them with parcel mapping to find individuals near the development site. Letters were mailed to the 25 non-land-leasing property owners that were the closest to each project. vi. Used snowball sampling from other participants. Throughout all these attempts, I explicitly searched for both public opponents and supporters of the project. Acceptance was estimated based on the comments that individuals made about the project, their affiliation with the project, or what others had labeled them in referrals. My initial judgment of a person’s acceptance would be later confirmed or denied through interview questions (no participant’s initial classification was incorrect). I followed up with all unresponsive individuals two weeks after the first contact attempt (sent another e-message or mailed another letter). In total, I reached out to 141 individuals and secured interviews with 33 people, resulting in a response rate of 23.4%. One developer spoke about projects in two communities; thus, the overall number of interviews was 34. Table 1.1 shows the layout of the interviewees. Interviews were done via phone and typically had a duration of 40 minutes. A short list of open-ended questions was prepared to help direct the interviews, but the semi-structured Table 1.1  Interview participants by group and community. Community

A

B*

C

D

Supporters

2

2

1

2

Opponents

5

3

1

3

Neutral

0

0

2

0

GOVT Employee

5

1

1

1

Developer/Consultant

1

0

2

2

Sub total

13

6

7

8

Total**

34

*Unfortunately, the developer in Community B was unable to speak with me due to their pending litigations. **The same developer was interviewed for both Community A & Community C; this was counted as two separate interviews. Thus, 33 individuals resulted in 34 interviews.

8  Smart Grids for Smart Cities Volume 1 Table 1.2  Citizens’ commonly stated concerns and benefits of local utility-scale solar farms#. Stated concern

Number of unique reports#

Poor aesthetics

13

Diminished property values

10

Misuse of agricultural land

9

Low economic benefits (e.g., small tax base, few jobs)

7

Inefficient and still emerging technology

7

Substantial size/growth

7

Ground water/soil contamination

6

Human safety hazards (e.g., natural disasters, EMF exposure)

6

Technology is too reliant on financial assistance

6

Electricity does not stay in the community

6

Fear of failure to decommission the project after its lifespan

5

Transfer in project ownership makes accountability questionable

5

Imported materials

4

Construction disturbance

4

Wildlife barrier

4

Drainage issues

3

TOTAL

102

Stated benefits

Number of unique reports##

Economic benefits for individual land-leaser

8

Economic benefits for community

7 (Continued)

Carbon-Free Fuel and the Social Gap  9 Table 1.2  Citizens’ commonly stated concerns and benefits of local utility-scale solar farms#. (Continued) Stated benefits

Number of unique reports##

Clean source of energy

6

It is not a more burdensome development (e.g., wind, housing)

5

Land-leasers’ profits can help keep farmers in farming

4

Gives land break/serves as a land bank

4

Technology is advanced enough to work in Michigan

3

Energy exporter

2

Not that visible

2

Farming solar energy is another form of producing

2

Less pesticide sprayed on ag land with solar

2

Native plants good for pollinators in PA 116 land

2

Technology is safe

2

TOTAL

49

This does NOT include perceptions from government officials or developers. Nor is this inclusive of every concern or benefit stated in the interviews. This table is intended to show how many different people spoke about each concern or benefit; this is NOT a ranking of importance. ## A concern or benefit was only counted once per individual regardless of how many times that individual may have stated it. #

nature of the data collection allowed flexibility for the interviewees to talk about what was important to them. Responses were captured with written notes and audio recordings to ensure the accuracy of note transcriptions. Table 1.2 further discusses about Citizens’ commonly stated benefits of local utility-scale solar farms.

1.5 Data Analysis Data was analyzed using thematic coding, which is the process of finding and labeling (i.e., tagging) information that represents ideas relevant to the research questions similar to [10]. The first step in the process involved

10  Smart Grids for Smart Cities Volume 1 transcribing each recorded interview verbatim. Trint software [11] was used to help transform the audio file into written text; however, due to transcription errors, I reviewed and corrected each interview transcript. This process worked to maximize descriptive validity, or the factual accuracy of the participants’ statements [12]. After the transcripts were completed, I read through all interviews several times in consultation with friends working in similar areas. During this process, we wrote memos about tentative themes in the data, and discussed each theme during regular meetings. The memos generated for all interviews were then used to start the construction of an initial codebook that would provide the guidelines for how we eventually coded the data. We built a codebook in Excel that had separate columns for the code names, definitions, rules, and examples. All authors here tested this codebook on several interviews, which led me to iteratively revise it until the codes were appropriate for all the interviews. The first finalized codebook was dubbed Codebook and we used it to begin tagging data in MAXQDA software [13]. Upon completion of my first cycle coding, we reviewed the data within each code to see if more specific themes had emerged. Again, we collaborated with known peoples to make memos that were ultimately used to develop a second-cycle codebook which I called Codebook 2. The codes in both codebooks were further categorized as “neutral,” “positive,” or “negative” to help organize the passages by perspective. After the coding was complete, I pulled all the tagged data for each community and wrote summaries of the content for each parent code and child code. I laid out these summaries so that I could look at themes within and across all the communities. This method of comparison was used to understand differences and similarities between communities to help generate meaning and assess threats to validity [14]. The next section depicts the findings of this work.

1.6 Conclusion This study looked at the public acceptance of and decision-making processes for utility-scale solar project development in Maharashtra. We interviewed 33 citizens, government officials, and developers across four potential host communities. Conducting interviews with individuals that have experienced solar project development allowed me to gather rich information on participants’ perspectives. This thorough understanding was necessary to provide a detailed explanation of the solar social gap. At the same time, this method and my findings may be limited due to the

Carbon-Free Fuel and the Social Gap  11 purposive selection and small sample size. Nevertheless, I discuss four key takeaway points below. The first conclusion is that the public in all four communities saw both negative and positive traits of large-scale solar development. Impacts on viewshed, property value, and agricultural land were the most consistently cited concerns, while projects’ economic additions and contribution to clean energy were common benefits. The second conclusion is that [15, 16] social gap theory has considerable operational constraints. The qualified support and self-interest explanations were particularly troublesome. The former had a broad definition for what constitutes as a qualification. I had to make a distinction between a qualification and a concern to make this explanation more meaningful. There is a fine line between not liking something about a project and not accepting a project because it lacks necessary features. Yet the theory does not illustrate where that line is drawn. I attempted to do so by deeming a qualification as a project or process attribute that was explicitly identified as the contingent factor for an individuals’ support. But this method is heavily subjective to phrasing. More clarification on the characterization of a qualification and how a qualification can be differentiated from a concern in practice would help improve this theory. The social gap theory’s portrayal of self-interest was impractical for use. To discern self-interest from qualified support, I had to establish standards that were nearly impossible to meet. Namely, I would only proclaim NIMBYism if I could find instances where an individual claimed that they did not want a project to affect them, but were indifferent if it affected someone else. This requirement was particularly sensitive to social desirability bias and therefore likely resulted in an underestimation of self-interest. The social gap theory should incorporate better ways to identify self-interest in application so that this explanation retains relevance. A lack of ability to detect NIMBYism may be misconstrued as a lack of NIMBYism, which might not be true. One way to improve the self-interest explanation could be to adjust its definition to merely mean any personal grievances against a project, instead of its current two-part definition which is any personal grievances against a project in combination with not caring about how the project impacts others. The former would be easier to identify because it does not require people to do the socially undesirable act of throwing others under the bus to be considered a NIMBY. Plus, a simplified definition would eliminate the nuances that set apart these two explanations, meaning that self-interest and qualified support could be merged into one. This may take the form of “self-interest” becoming a type of qualification

12  Smart Grids for Smart Cities Volume 1 in which an individual’s support would depend on how a project impacts them personally. Overall, the social gap theory, as currently described, is unsuitable for application. My expectation is that other researchers who desire to operationalize this theory may struggle to do so. My third conclusion is that despite the shortcomings of the social gap theory in distinguishing between self-interest and qualified support, I was still able to characterize the solar social gap in Maharashtra. The most likely explanations are a combination of democratic deficit and qualified support. A democratic deficit may be occurring because standard public processes are relied upon, which mostly attract extreme opposing views. These voices are the ones that tend to be reflected in the decision outcomes. Whether or not they represent the minority can only be determined with more representative sampling of the community’s preferences. Regardless, measures to improve public representation should be used to complement the public process to ensure all voices are present at the decision-making table. As far as qualified support, a lot of individuals that I interviewed had criteria that needed to be met in order to accept proposed solar developments. Some qualifications were attempted to be addressed through adjustments in zoning ordinances or site plans. However, these actions were often perceived as insufficient, leading community members to remain unsatisfied with the projects. And in some instances, qualifications were simply not addressed either due to decision-makers’ lack of effort or ability. Instilling practices that encourage early and meaningful public involvement during the zoning process and throughout project siting is crucial to help appease qualified supporters. My final conclusion is that decision-making strategies make a difference in community acceptance [17–21]. Though they are not the only factor that matter—indeed, organized opposition also plays an important role—but government officials and developers have at least a modicum of control over these processes. Decision-makers in my study communities used three effective strategies, including expanded measures to collect public input, adopting proactive planning where the solar ordinance was formed prior to developer interest, and zoning at the township level rather than at the county level. However, not all of these practices were used to the fullest extent and several recommendations I describe above were not implemented at all, such as aggressive awareness raising or descriptive information sharing. Therefore, it remains difficult to compare the outcomes of a comprehensive and collaborative approach with a business-as-usual approach. Practitioners must deploy best practices for community engagement in solar farm siting, not only to improve the development process and maximize community well-being, but also to

Carbon-Free Fuel and the Social Gap  13 provide opportunities for researchers to confirm the effects of those practices using empirical studies. These initiatives are necessary next steps for closing the solar social gap.

References 1. Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., & Boer, M. M. (2021). Connections of climate change and variability to large and extreme forest fires in southeast Australia. Communications Earth & Environment, 2(1), 1–17. https://doi.org/10.1038/s43247-020-00065-8 2. Acosta, R. (2017, October 10). Lapeer becomes home to Michigan’s largest solar park. MLive MediaGroup. https://www.mlive.com/news/flint/2017/10/ lapeer_becomes_home_to_michiga.html 3. Adeh, E. H., Selker, J. S., & Higgins, C. W. (2018). Remarkable agrivoltaic influence on soil moisture, micrometeorology and water-use efficiency. PLOS ONE, 13(11), e0203256. https://doi.org/10.1371/journal.pone.0203256 4. Aitken, M., Haggett, C., & Rudolph, D. (2016). Practices and rationales of community engagement with wind farms: Awareness raising, consultation, empowerment. Planning Theory & Practice, 17(4), 557–576. https://doi.org/1 0.1080/14649357.2016.1218919 5. Aitken, M. (2010). Wind power and community benefits: Challenges and opportunities. Energy Policy, 38(10), 6066–6075. https://doi.org/10.1016/j. enpol.2010.05.062 6. American Planning Association. (2016). Solar powering sunnyside. https:// www.planning.org/research/solar/sunnyside.htm 7. Armstrong, A., Ostle, N. J., & Whitaker, J. (2016). Solar park microclimate and vegetation management effects on grassland carbon cycling. Environmental Research Letters, 11(7), 074016. https://doi. org/10.1088/1748-9326/11/7/074016 8. Asplund, S. (2020, September 28). 2,000-acre solar farm proposed for Marquette County. WLUC-TV. https://www.uppermichiganssource.com/2020/ 09/28/2000-acre-solar-farm-proposed-for-marquette-county/ 9. Balaskovitz, A. (2020, February 2). West Michigan renewable energy projects: wind out, solar in. MiBiz. https://mibiz.com/sections/energy/ west-michigan-renewable-energy-projects- wind-out-solar-in 10. Barrett, K. L., Lynch, M. J., Long, M. A., & Stretesky, P. B. (2018). Monetary Penalties and Noncompliance with Environmental Laws: A Mediation Analysis. American Journal of Criminal Justice, 43(3), 530–550. https://doi. org/10.1007/s12103-017-9428-0

14  Smart Grids for Smart Cities Volume 1 11. Barry, J., Ellis, G., & Robinson, C. (2008). Cool Rationalities and Hot Air: A Rhetorical Approach to Understanding Debates on Renewable Energy. Global Environmental Politics, 8(2), 67–98. https://doi.org/10.1162/glep.2008.8.2.67 12. Bassler, A., Brasier, K., Fogle, N., & Taverno, R. (2008). Developing effective community engagement: A how-to guide for community leaders. The Center for Rural Pennsylvania. https://www.rural.palegislature.us/Effective_ Citizen_Engagement.pdf 13. Bell, D., Gray, T., & Haggett, C. (2005). The ‘social gap’ in wind farm siting decisions: Explanations and policy responses. Environmental Politics, 14(4), 460–477. https://doi.org/10.1080/09644010500175833 14. Bell, D., Gray, T., Haggett, C., & Swaffield, J. (2013). Re-visiting the ‘social gap’: Public opinion and relations of power in the local politics of wind energy. Environmental Politics, 22(1), 115–135. https://doi.org/10.1080/096 44016.2013.755793 15. Bessette, D. L., & Mills, S. B. (2021). Farmers vs. lakers: Agriculture, amenity, and community in predicting opposition to United States wind energy development. Energy Research & Social Science, 72, 101873. https://doi. org/10.1016/j.erss.2020.101873 16. Bessette, D., Schelly, C., Schmitt Olabisi, L., Halvorsen, K., Gagnon, V., Fiss, A., Arola, K. & Matz, E. (2021). Energy democracy in practice: Centering energy sovereignty in rural communities and tribal nations. In A. FeldpauschParker, D. Endres, T.R. Peterson, & S. Gomez (Eds.), Routledge Handbook of Energy Democracy. Routledge Press [submitted for publication]. 17. Bodwell EnviroAcoustics (2018, October). Sound level assessment Three Rivers Solar Energy Project Hancock County, Maine. State of Maine. https:// www.maine.gov/dep/ftp/projects/three-rivers/application/sloda/section%20 5.%20noise.pdf 18. Boudet, H. S. (2019). Public perceptions of and responses to new energy technologies. Nature Energy, 4(6), 446–455. https://doi.org/10.1038/ s41560-019-0399-x 19. Bradley, R. (2020, June 3). Big wind throws in the towel in Lapeer County, Michigan. Master Resource. https://www.masterresource.org/windturbine-noise-issues/big-wind-loses-lapeer- county/ 20. Brannstrom, C., Jepson, W., & Persons, N. (2011). Social Perspectives on Wind-Power Development in West Texas. Annals of the Association of American Geographers, 101(4), 839–851. https://doi.org/10.1080/00045608. 2011.568871 21. Brittan, G.G. (2001). Wind, energy, landscape: Reconciling nature and technology. Philosophy & Geography, 4(2), 169–184. https://doi.org/ 10.1080/10903770124626.

2 Opportunities of Translating Mobile Base Transceiver Station (BTS) for EV Charging Through Energy Management Systems in DC Microgrid A. Matheswaran1*, P. Prem2, C. Ganesh Babu3 and K. Lakshmi4 1

Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, India 2 Operations & Business Development, Switchgear Electromechanical, Chennai, India 3 Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Tamil Nadu, India 4 Member in Institution of Engineering and Technology (IET), Coimbatore, India

Abstract

Base Transceiver Station (BTS) is a gear to aid the wireless communication between the mobile phone and the network in a telecommunication system. BTS is supplied by the grid and a diesel generator is used as an additional power source to cater during grid interruption. The battery backup ensures the continuous supply to loads in BTS during the transition of supply from grid to diesel generator during interruption. Electric vehicles and charging stations are complementary merchandise so that electric vehicle’s charging stations are need to establish everywhere similar to fuel filling stations at present. Installation of new EV charging stations required land at desired locations and ultimately increase the high capital cost. One of the alternative solution is to utilize the Base Transceiver Station (BTS). Converting the existing BTS to EV charging stations avoids congestion in charging station at city/urban areas. Nowadays most of the mobile phone operators are undergo in huge losses due to competitions and they spent significant expense on capital and maintenance cost on BTS. Hence the proposed solution of converting BTS to EV charging stations gives a great *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (15–40) © 2023 Scrivener Publishing LLC

15

16  Smart Grids for Smart Cities Volume 1 relief in the expenditure for the cellular companies. In addition to that, following are find to the benefits, utilizing existing -48 V supply of BTS, Export power to grid which give additional revenue generation for mobile phone operator, minimizing the usage of DGs, avoiding the acquirement of land for establishing the new infrastructure for EV charging stations. When diesel generator (DG) is used as the additional power source in BTS the sluggish transition of supply to loads occur during the absence of grid. Further, the cost for capital and maintenance and CO2 emission are increases. The combination of solar PV source, battery and energy management algorithm facilitates the opportunity to save the electricity cost by exporting excess power from PV source to grid during non-accessing period of electric vehicles. Here the energy management system which comprises of 1 kW grid connected solar PV system for the BTS and the Control Algorithm is proposed to overcome the various issues. In the energy management system, solar PV array connected as a source, battery and grid are act as either source or sink. A 1 kW grid connected solar PV system-based EMS is modeled and simulated using MATLAB/Simulink tool and the results are presented. Keywords:  Base transceiver station, EV charging stations, diesel generator, solar PV system, EMS

2.1 Introduction 2.1.1 Telecom Sector in India The telecom sector is one of the most important sectors in contributing economic benefits in India and it is the second-largest sector in the world. The number of Base Transceiver Stations (BTS) which are installed by the telephone companies is increasing every year and its growth rate is significantly high, especially during the last five years across the country [1]. The yearly trend on the total number of BTS installations is shown in Figure 2.1. Here, the BTS which provides all the services, which includes CDMA, 2G, 3G and 4G, is considered. The telecom network infrastructure is generally classified into three classes, i.e., active infrastructure, passive infrastructure and backhaul infrastructure. Antenna, switches and transceivers are the active Infrastructure. Passive infrastructure represents the telecom tower and its power supply units. The way the telecom tower is erected will decide its type [2]. The common two types are ground-based tower and rooftop tower. In rural and semi-urban areas, a ground-based tower is installed due to the low cost and availability of land. However, the rooftop tower is established in

BTS for EV Charging Through EMS  17 1988099

Total Number of BTS

2000000

1672732

1800000 1600000 1400000 1200000

1445827

853650

975679

1000000 800000 600000 400000 200000 0

March 2015 March 2016 March 2017

March 2018 March 2019

Figure 2.1  Yearly total number of BTS installations [1].

urban areas. Backhaul infrastructure is nothing but the intermediate link between the networks. In these, the passive infrastructure incurred huge initial cost. Some countries allow the sharing of the passive infrastructure amongst the different telecom companies to optimize the cost. In India, this policy is being implemented and practiced for the welfare of the telecom industry [3, 4].

2.1.2 Overview of Base Transceiver Station (BTS) A Base Transceiver Station (BTS) aids the establishment of the wireless connection between the mobile phone and or other user devices and a network. The heart of the network switching subsystem is a mobile switching center (MSC), and it carries out the switching functions to establish the proper communication. The MSC is also integrating with the public switched telephone network (PSTN). The communication is made through MSC and there is an interconnection between MSC and Base Transceiver Station. The number of MSCs deployed is based on the size or the capacity of the network service provider. The block diagram of the cellular service network is shown in Figure 2.2. A few researchers proposed the scheme for suitable location identification through programming and optimizing the process of network planning process [5]. The methodology for identifying the optimum cell sites and number of cells is intended to satisfy the system requirements with minimum financial and spectral cost. This has been

18  Smart Grids for Smart Cities Volume 1 Base Station

MSC

MSC

Exchange

PSTN

Figure 2.2  Cellular service network.

Utility mains

Automatic Transfer Switch

G

Distribution Panel

Air conditioning (2 kW unit) 6x start)

4x SMPS units: 3 cont. use + 1 battery boost only (2.5 kW per unit)

Lighting and receptacles (0.2 kW)

−48 Vdc

Misc (~10A)

BTS 1 (~30A)

BTS 2 (~30A)

Battery 1 (~40A at max charge)

Figure 2.3  Electrical layout of Base Transceiver Station (BTS).

Battery 2 (~40A at max charge)

BTS for EV Charging Through EMS  19 obtained through the development of an algorithm based on a fuzzy expert system [6]. The problems associated with network service coverage often encountered at the urban areas are addressed by developing a point‐based abstraction of demand approach [7–9]. Figure 2.3 shows a typical electrical layout of telecommunication radio base station. Mostly it uses the three-phase supply and diesel generator as a backup source during a power outage. Microwave radio devices, lighting and air conditioners are the major loads at the telecom base station. The automatic transfer switch (ATS) select either the grid supply or the diesel generator’s supply. The BTS is mainly operated through switched mode power supply (SMPS) and the capacity of SMPS is selected by calculating the loads connected to it [10]. The battery banks are the vital part in providing uninterrupted electrical supply to the entire system. When any fault occurs in rectifier and it is unable to support the equipment connected with it, the battery supplies the -48V voltage to the equipment and enables it to keep functioning [11]. The capacity of the battery bank is chosen based on the required hours of autonomy. Also, the battery bank operates the AC transfer switch during a power failure. In general, deep cycle batteries are adopted for BTS so as to discharge up to zero state of charge (SoC) and later the battery can be charged to a full state of charge [12, 13]. The life cycle of deep cycle batteries do not get affected when using them for deep discharge and they recover into a full state of charge. In addition to that, DC surge protector, lightning arresters must be incorporated at the site to protect all the equipment in the BTS.

2.1.3 Electric Vehicle in India Presently the automobile Industry in India secured fifth position in the world, and it is expected to reach third position by 2030. Internal combustion (IC) engine-based vehicles have predominated in society for many decades. Almost all kinds of vehicles, such as public transportation ­systems—buses, trains and private transportations like two-wheelers, cars, trucks, etc.—use the internal combustion (IC) engine technology. This technology is matured and provides good performance in all aspects. However,

20  Smart Grids for Smart Cities Volume 1 it has a negative impact on the environment, viz., emitting more CO2 and thus polluting the environment. The limited reserves of the fossil fuels restrict the IC engine vehicles for utilization in the long run. Also, the government has been taking the necessary initiatives to replace these vehicles with electric vehicles. There are many reasons for this switchover; however, technology transfer and environmental safety are the two main concerns [14, 15]. From the inception of electric vehicle technology, many issues have been found and those have all been sorted out, and the e­ -vehicles are being commercialized in a slow-paced manner. One such issue is battery and its durability. The battery is the source for electric vehicles and its lifetime is based on the number of cycles of usage. Due to the present invention in the battery, it can store more energy and discharge the same for a longer distance of vehicle drive with good speed and torque capability. As far as environmental safety is concerned, e-vehicles don’t emit any hazardous smoke while operating, and hence are more eco-friendly in nature. The fast depletion of fossil fuels does not affect transportation which is based on e-vehicles. Charging stations are essential for the e-vehicle for its continuous moving, and only a few charging stations for electric vehicles are established at present. The charging of electric vehicles is “green,” since it can be done through various renewable energy sources. The fast-evolving technology in the power conversion system and microgrid offers the possibility of exploring the utilization of more and more renewable energy-based charging stations. Also, the government of India supports plans like Faster Adoption and Manufacturing of (Hybrid &) Electric Vehicles (FAME) and National Electric Mobility Mission Plan 2020, which explore the possibilities for the manufacturing and usage of electric vehicles in enormous numbers in the near future. This will contribute to a major shift to electric vehicles by 2030. The annual sales of electric vehicles In India are shown in Figure 2.4.

97850

Two wheelers

350

97500

2011-12

22000

Cars

24000

2000

2000

20000

22000

2011-16

2016-17

Figure 2.4  Annual sales growth of electric vehicles in India [16].

BTS for EV Charging Through EMS  21

2.1.4 Evolution of EV Charging Station As per the recent policy of the government of India, charging stations along with the protection infrastructure need to be installed at every 25 km in the highways and a minimum of one charging station in a grid of 3 km x 3 km in urban areas [17]. There should be 4 Lakh charging stations installed by 2026 to meet the charging requirement for 20 Lakh electric cars. Generally, three types of electrical vehicle charging stations are recommended: Level 1, Level 2 and DC charging. The specification of level 1 charging station is fixed as 120 V and maximum of 16 A. The vehicle itself is equipped with this type of charger as an on-board charger which can be easily plugged into any common power sockets found in the market. 240 V and 12-80 A ratings are adopted in level 2 charging stations. It helps in reducing energy consumption and it has the capability of automatic recovery and restart after main power loss and ground fault. The average power rating of the charging station is about 20 kW. The DC fastcharge stations follow the standards of both the North American SAEJ 1772 Combo and the Japanese JEVS G105-1993. Its rating is 480 V and up to 125 A. Due to the higher charging current and voltage in DC form it is able to charge the electric vehicle with less time effectively. Amongst all the type of charging stations level 2 is more suitable for charging applications due to its protection and safer function [18, 19]. Currently about 270 units of EV Charging stations are installed in India in which AC Slow chargers are 246 and DC Fast Chargers 24. The organization of the chapter is as follows. Section 2.1 presents the Telecom sector in India, an overview of Base Transceiver Station (BTS), Electric Vehicle in India and Evolution of EV charging stations. Possibilities and challenges in translating Mobile Base Transceiver Station (BTS) for EV Charging station is given in detail in section 2.2. Section 2.3 elaborates on the implementation of energy management system in BTS along with the simulation results. The conclusion is reported in section 2.4.

2.2 Translating Mobile Base Transceiver Station (BTS) for EV Charging 2.2.1 Mobile Base Transceiver Station (BTS) for EV Charging – A Substitute or Complementary Solution? In the present scenario, the Mobile Base Transceiver Stations (BTS) are used for communication purposes only. The high capital and operating

22  Smart Grids for Smart Cities Volume 1 cost creates trouble for the mobile phone operators, including the struggle for customer retention. The high capital cost is due to renting the land in the residential/commercial area, and the operating cost involves electricity cost, and cost for manpower and maintenance; ultimately, the telecom operators meet huge losses in their business. Hence, it is the right time to move the telecom sector from the loss column to a profitable industry. This can be done through two ways, first through the policy decision making by the government or by the management of telecom industries, and second by providing the technical solution. This chapter discusses the technical solution towards the remaking of the telecom sector into a profitable industry. We found some similarities between the BTS and electric vehicle charging systems and thus explore the possibility of making the BTS as supplementary system for electric vehicle charging. The DC power supply system is being implemented in BTS for its loads. It has the following benefits: • DC power supply provides the opportunity of using multiple numbers of rectifiers connected in parallel. • This leads to sharing the current in each rectifiers when the capacity of the BTS is expanded in future. • Also, the parallel connection of rectifiers offers the provision of making any one rectifier as a stand-alone unit and the same can be utilized when fault occurs in other rectifiers. This results in reducing the mean time between the failures (MTBF) in rectifier units. And especially, in BTS, -48 V DC power supply system (positive grounded) is being used for the following reasons [20–22]: • DC power can be stored easily through battery. • The system which uses the DC voltage level less than 50 V doesn’t require any compliances like National Electric Code (NEC), International Electro technical Commission (IEC), etc. • The voice signals can be carried by the DC system. • DC Power is deemed to be reliable enough for critical telecom applications. • Sulphate formation is one of the major causes for the durability of battery. The sulphation on battery terminals leads

BTS for EV Charging Through EMS  23 to frequent battery failure. The negative voltage-based power supply in the BTS protects against this sulphation on the battery’s terminals so as to enhance the battery lifetime. • The positive grounding is a system that was formerly practiced in the industry for some decades, and it is used to protect the cables from getting damaged in wet conditions by mitigating the electrochemical reactions in it. • Also, the positive grounded system reduces the corrosion problems associated with underground cables and conduits. The above-discussed salient features of the BTS’s power supply system make it suitable to use as a charging station counterpart in addition to the mobile communication.

2.2.2 Proposed Methodology Integration of a vehicle charging system with the existing power supply scheme in BTS is proposed and the same is shown in Figure 2.5. In the +

− BIDIRECT IONAL CHARGER

48V BATTERY

DC LOADS OF BTS SOLAR PV SOURCE

DC DC CONV BIDIRCTI ONAL CONVERT ER

GRID DISEL GENERAT OR AC LOADS OF BTS

PORT 1 SLOW CHARGING (DC-DC)

PORT 2 FAST CHARGING (AC-DC)

Figure 2.5  Proposed scheme for integrating vehicle charging system with the existing BTS.

24  Smart Grids for Smart Cities Volume 1 existing power supply system of BTS, 48 V battery, grid and diesel generator (DG) are used as the source. In addition to this, the following modifications are required to make BTS a charging station: • Ah rating of battery should be increased to store more energy for charging the vehicle. • Renewable energy sources such as solar PV unit, wind generator shall be interconnected. It helps in conserving the energy at BTS/Charging station. Here, the solar PV unit is proposed. • Implement DC-DC converter in between solar PV source and the DC bus. • Adopting DC-DC charge controller with DC bus and the same can be used for slow charging of electric vehicles. • Installing the rectifier unit in AC bus element and it can be utilized if fast charging option is required. Here, the standby rectifier unit can be utilized if suitable rating is chosen. The following are the different power converters being employed in the proposed system, • • • • •

DC-DC converter Rectifier Battery Backup System Charge Controller Bidirectional charge controller.

2.2.3 System Description 2.2.3.1 Solar PV Array Selection of number of panels required to satisfy the estimated daily load,



Number of panels

Total units required for loads/day( A) Total units produced by single panel/day(B)

Total units required for q kW of loads/day (A) = 5 units Total units produced by single panel/day (B) = Actual power output 8hrs/day



BTS for EV Charging Through EMS  25 Actual power output = power output of PV panel combined efficiency Power ouput of PV panel = Peak power output of PV panel operating factor = 260W 0.75 = 195W Actual power output = 195W 0.81 = 157.95W Hence, Total units produced by single panel/day (B) = 157.95W 8hrs Number of panels required = 5/1.26 = 3.95 Therefore the number of panels selected as 4. When four such panels are connected in series to make an array the net voltage at maximum power point is 145.72V and the current is 7.12 A. This array is connected with the input of the DC-DC converter.

2.2.3.2 DC-DC Boost Converter In the proposed BTS cum charging station the DC-DC converter is used for a) voltage modulation of input PV voltage b) maximum power point tracking and c) voltage regulation [23]. Also, the DC-DC converters charge the battery through solar PV array in an effective and safe way. The DC-DC converter can either be used in a single stage or a two-stage power conversion system. It is broadly classified as non-isolated and isolated converters. Buck, boost, buck-boost converters are the broad categories of the converter. In a typical isolated DC-DC converter the isolation is provided by using a high-frequency ferrite core mutual inductor. The non-isolated DC-DC converters are more widely adopted than the isolated DC-DC converters as the latter have demerits like less efficiency, bulk circuitry and excessive heat generated in the coupled inductor. A simple perturb and observe (P&O) algorithm is implemented in the converter to extract the maximum power from the PV array.

2.2.3.3 Rectifier A rectifier converts AC supply to DC through the power semiconductor device SCR. SCR is also known as thyristor, which is a unidirectional device. A rectifier uses the isolation transformer in its input side along with the controller which ensures the constant output voltage and current. Figure 2.6 shows the block diagram of the rectifier unit. In industrial applications mostly either a 6 Pulse converter or a 12 pulse converter are used based on the power and wave shape necessities. If the fully controlled 12 pulse converter is used it acted as two independent

Input AC

Filter Elements

Output AC

26  Smart Grids for Smart Cities Volume 1

Isolation Transformer

Figure 2.6  Block diagram of the rectifier.

units due to the employment of delta to star transformer connection. It has a lower voltage and current total harmonic distortion (THD) which follows the IEEE 519 standards. This lower harmonic content in its output parameters results in getting very minimum value of distortion power factor and hence the total power factor is maintained near to unity. A controller part is integrated with the rectifier unit so as to change the parameter of charging current. Level of voltage and float charge voltage according to the battery terminal’s voltage can be selected through this controller. The converters must be protected against over temperature, under voltage, over voltage, line disturbances, and inappropriate usage so that the reliability will be ensured. Hence the load can be in a safer zone in the abovementioned conditions. The failure rate of the converter is minimized since it uses the solid state devices by suitably selected to withstand large voltage, current and temperature. Some novel topologies proposed by the researchers also support for these kind of protection. To attend the fault and do the maintenance or services in the converter, it needs to be designed in a modular way.

2.2.3.4 Battery Backup System Battery, Diesel Generator (DG), Super Conducting Magnetic Energy Storage (SMES), super capacitor and fuel cell are the various electrical energy storage elements. The combination of solar PV-fuel cell acts as a reliable, controllable and smooth power source. Also it overcomes the intermittency problem associated with solar PV source. Diesel Generator (DG) can be integrated with PV unit for the uninterrupted supply. Even though its capital cost is less than battery backup for the same rating, the DG suffers from poor dynamic characteristics. The SMES, super capacitors and fuel cell have improved dynamic characteristics; they have less

BTS for EV Charging Through EMS  27 discharge time but the cost of fuel cell and SMES is high. Therefore, due to matured technology, quick response time and high autonomy, battery is the best choice for energy storage [24]. The Valve Regulated Lead Acid Battery (VRLA) has advantages like low maintenance, frequent cyclic duty and superior life cycle over other types of batteries. Hence, a VRLA type of battery is adopted in the proposed system. In this work, the appropriate sizing of the battery has been done for the required hours of autonomy and is given below. 6SGL120 Exide battery- 12 V, 120 Ah No. of batteries connected in series = 4 Net voltage of battery bank = 48 V Current delivered by each battery = 12 A @C10 rating Power delivered by battery bank 48V × 12A = 576 W = 600 W Energy stored in battery 48V × 120 Ah = 5760 Whr Hours of autonomy = 10 Hrs @DOD = 100 % Hours of autonomy = 5 Hrs @DOD = 50 %

2.2.3.5 Charge Controller The Bidirectional charge controller is used in charging the battery in the BTS power supply unit. A unidirectional charge controller can be used for charging the battery in an electric vehicle. If this unidirectional charge controller is replaced with a bidirectional charge controller then transfer of energy from vehicle to grid is possible. The primary function of a charge controller is to ensure proper charging and discharging of the battery with protection of the battery from overcharge and over discharge so as to extend its lifetime. A non-isolated half bridge bidirectional DC-DC converter is used as the charge controller, as it has fewer elements and less complex of circuitry. This circuit is an anti-parallel connection of conventional buck and boost converter while replacing the diodes to make the converter to carry the current in both forward and reverse direction. The bidirectional energy transfer capability of this converter allows the battery to charge and discharge. The battery is connected with the low voltage side of the converter and the DC bus is connected with the high voltage side of the converter. The circuit diagram of the charge controller is shown in Figure 2.7.

28  Smart Grids for Smart Cities Volume 1 +

Buck Switch (S1) LL CH

Battery 48 V CL

220 V

Boost Switch (S2)

Figure 2.7  Non-isolated half bridge bidirectional DC-DC converter.

When the battery is charging, the charge controller operates in buck mode and DC link voltage. Switch (S1) and diode (D1) are in charging mode. Switch (S1) is ON and OFF continuously but S2 remains OFF. During discharging, the charge controller operates in boost mode. Switch (S2) and diode (D1) are in charging mode. In this mode, switch S1 is OFF while switch S2 is ON and OFF continuously.

2.2.3.6 Bidirectional Converter A 1 kW single-phase bidirectional converter is connected between the DC link and the load or grid. When the battery is to be charged using the grid supply, the bidirectional converter acts as the AC/DC converter and supplies power to the battery from the grid [25–27]. When the bidirectional converter acts as a DC/AC converter, the load takes power from either PV array or battery and excess power is fed to the grid from PV array or both. Figure 2.8 shows the circuit diagram of a single-phase bidirectional converter. The input of the bidirectional converter is 220 V DC. This input voltage has to be converted to 110 V AC and then it is stepped up to 230 V

BTS for EV Charging Through EMS  29 S4

S6

Linv

Lg Cf

Cdclink S7

S5

Figure 2.8  Bidirectional converter.

using step-up transformer. The converted AC voltage waveform is non-­ sinusoidal and therefore THD of the voltage and current waveform is higher. The sinusoidal pulse with modulation switching scheme is adopted along with the LCL filter elements circuits to overcome the issue of harmonics and it ensures the power quality at PCC.

2.3 Implementation of Energy Management System in Base Transceiver Station (BTS) 2.3.1 Introduction The electricity consumption is found to be one of the major contributions to the total operating cost of the telecom tower site. More electrical energy is required to operate the base transceiver station (BTS) and its associated components along with the air conditioning system installed at the site. This higher electricity consumption is the obstacle to the process carried out by the cellular service provider in reducing the carbon footprint. This creates the motivation for the development of the proposed Energy Management System exclusively for BTS. An Energy Management System comprises the solar of PV source, DC-DC boost converter, battery, charge controller, bidirectional converter and diesel generator. Solar PV array is connected as a source, and battery and grid act as either source or sink. DC-DC boost converter is used to boost the input voltage and for tracking the maximum power from the solar PV array. A Power Supervisory and Control Algorithm (PSCA) is proposed and implemented through TMS320F2812 DSP controller. The algorithm works in two modes, grid available mode and grid failure mode. This algorithm supplies power to loads on a priority basis during grid failure

30  Smart Grids for Smart Cities Volume 1 S4 ...

Cpv

S6

DC-DC Cdclink

S7 LL Battery Bank

Lg

Grid

Cf

Converter

PV Array

Linv

S5

S2

110:230 V Transformer Load

S3 Charge Controller

Figure 2.9  Electrical scheme of energy management system.

mode. Further it focuses on maximum reduction of voltage fluctuation, utilization of solar power, uninterrupted power supply, power quality and less grid independency. The solar PV system with the proposed energy management algorithm is deployed in base transceiver station (BTS) of 1 kW for telecom network and the cost benefit analysis is presented. The system is modeled and simulated using MATLAB/Simulink tool and the results are presented. Figure 2.9 shows the electrical scheme of an energy management system. The proposed power supervisory and control algorithm (PSCA) is implemented in the system and the performance of the system is evaluated in the aspects of power quality at grid, power and voltage fluctuation, continuous and priority-based supply to loads, maximum utilization of solar energy and electricity savings.

2.3.2 Control Strategies The control strategies are developed for the solar PV system for the proper functioning of the system. The control strategies consist of three control sections: i) MPPT control which controls the DC-DC converter to extract the maximum power from the PV array, ii) charge controller control which controls the battery charging/discharging current, thereby maintaining the DC link voltage, iii) bidirectional converter control which controls the grid current and thereby maintains the DC link voltage and provides the synchronization of PV system with grid to power exchange. Figure 2.10 shows the control strategies of the system.

BTS for EV Charging Through EMS  31 Vpv Ipv

MPPT

Limiter

PI

+ -

MPPT Control V*dclink

S1(To Boost Converter)

Driver

S2

I*bat

+ Smb PI – + – Vdclink Ibat Charge Controller Control PI

Driver

S3

(To Charge Controller)

Bidirectional Converter Control – V*dclink PI Vdclink V*ac

Vgrid PI

S1mig

I*ac – Iac Grid Connection Control

PI Vac

PLL Sin(θ)

V*dclink

I*ac +

PI

– Iac Standalone Control

+

FF

S1mis +

(To Bidirectional Converter)

Smig +

V1grid

OR

Smis + V1 grid FF

+ –

S4 D

S5 S6 S7

Figure 2.10  Control strategies of energy management system.

2.3.2.1 MPPT Control Due to the simple and effective power extraction capability, the P&O MPPT technique has been adopted in MPPT control. The controller’s output is a reference voltage, and it is compared with the desired PV voltage. The error signal is given to limiter and PI controller to get the control signal. The comparator compares the control signal and 100 kHz of carrier signal to generate the PWM pulse for the DC-DC converter’s switch (S1). The driver circuit converts this PWM pulse to the required voltage level such that the gate of the switch can be drive. Perturbation of duty cycle of the PWM pulse changes the effective net resistance of the converter and makes the PV array’s operating point near to maximum point of V-I characteristics so that maximum power from PV array is extracted.

2.3.2.2 Charge Controller Control The control part of the battery charge controller controls the battery charging/discharging current and maintains the DC link voltage even at sudden variation of both irradiance (G) and load. The transient in the battery current is avoided due to which the life of the battery gets increased.

32  Smart Grids for Smart Cities Volume 1

2.3.2.3 Bidirectional Converter Control The control part of the bidirectional converter consists of two sections, (i) grid connected control, (ii) grid failure control.

2.3.2.3.1 Grid Connected Control

This control part comprises the combination of outer voltage control loop and inner current control loop. The outer voltage control loop generates the AC reference current for the inner current control loop which generates the control signal for the PWM pulses. The error signal, which is the difference between reference DC link voltage and measured DC link voltage, is processed by the PI controller. The output of the controller is multiplied with unity magnitude sinusoidal pattern obtained from phase locked loop (PLL) and the reference AC current created. This signal will ensure the synchronization of the solar PV system with the grid. In the inner current control loop, the error between the actual AC grid current and reference AC current is processed with second PI controller. The controller output is added with the feed forward signal to generate the control signal. A feed forward loop minimizes the impact of the grid voltage disturbance in the output current regulation. This control signal is compared with the carrier signal of 20 kHz and PWM pulse is generated. The PWM pulse and its delayed version are given to the switches S4 & S5 and the complementary version of these two signals is given to the switches S6 & S7 of the bidirectional converter through the driver circuit. The delay or dead time for the pulses avoid a short circuit in the converter.

2.3.2.3.2 Grid Failure Control

The control part for grid failure mode is similar to grid connected control except the generation of sinusoidal pattern (V*ac) with desired magnitude of load voltage using lookup table. The error between V*ac and actual load voltage (Vac) processed by PI controller and the reference signal for inner current controller is generated without the need for PLL. The rest of the functions are similar to grid connected control. The magnitude of the reference current of the DC-AC converter is adjusted so as to regulate the magnitude of the DC link voltage.

BTS for EV Charging Through EMS  33

2.3.3 Power Supervisory and Control Algorithm (PSCA) A Power Supervisory and Control Algorithm (PSCA) is developed and implemented in the central control unit of a grid connected solar PV system. The central control unit measures the voltage and current of PV array, battery, load instantaneously and calculates the power of each. Also, it gets the information of SOC of battery. All these parameters are fed to the central control unit which has a control algorithm and is based on the logic; it controls the DC-DC converter, bidirectional converter, charge controller and relays that control the connected loads. The proposed algorithm is developed such that it is operated in two modes, grid available mode and grid failure mode. Both modes ensure the maximum utilization of solar PV power, voltage regulation, energy savings and extended battery life. The grid available mode maintains the power quality at grid, and minimum rely on grid and grid failure mode provides the uninterrupted power to the loads in priority basis when grid power is absent.

2.3.3.1 Grid Available Mode The algorithm starts functioning when the grid power is available. In this mode the central control unit only controls the charge controller and bidirectional converter through the generated control signal. The algorithm for grid available mode is given in Figure 2.11. Initially PV array voltage, current, battery voltage, current, load voltage and current, grid voltage and SOC of battery is measured. Through these data, PV power, battery power and load power are calculated. Once the grid availability is ensured then the appropriate control signal is produced by comparing solar PV power and the load power. Also, the control signal is decided by the SOC of battery bank.

2.3.3.2 Grid Fault Mode The logic given in the algorithm for grid fault mode is different when compared to grid available mode. Charge controller, load relays and bidirectional converter are controlled through control signal generated by the central control unit. The algorithm for grid fault mode is given in Figure 2.12. The appropriate control signal is produced by comparing solar PV power and the prefixed load power values and also by considering the SOC

34  Smart Grids for Smart Cities Volume 1 start senser Vpv, Ipv, Vbat, Ibat, Vload Vgrid, Iload, soc of battery calculate Ppv, Pbat, Pload Grid Available

No

A

No No

Yes Ppv-Pload Prem=Ppv-Pload

Yes No

No

soc0 YES Vref=Vref-dV

dV |vu| > Vdc/3

off

PWM

on

off

off

on

|vu| > 2Vdc/3

PWM

on

on

off

off

on

|vu| < Vdc/3

off

off

off

on

PWM

off

2Vdc/3> |vu| > Vdc/3

off

PWM

off

on

on

off

|vu| > 2Vdc/3

PWM

on

off

on

on

off

negative half cycle

range voltages. To obtain the output voltage of –Vdc/3 or 0, the switch S3 is button in PWM plus the 7-height inverter is button in mode six and five to an voltage production of -2Vdc/3 otherwise –Vdc/3. By comparing the proposed system with the nominal or normal converters we can say that the level of complexity is reduced as we are using only 6-power electronic switches for 7-level output voltage. All the range of utility voltages for operating the 7-level inverter is placed in the Table 11.1, power-electronic switches conduction states during the utility voltage Ranges. In the above Table 11.1 we can observe that in all the cases only 3 power Electronic switches are conducting in series expect in the case Vu > 2Vdc/3. In both +Ve and –Ve cycle the 4 power electronic switches are conducting. So we can say that the switching losses will be reduced due to fewer conducting switches. The impediment of the planned 7-height inverter is so as to the electrical energy score of the filled-connect converter be more prominent than so as to of customary staggered inverter geography. The spillage present is a significant boundary in a PV framework for ­transformer-less activity. The spillage present is reliant upon the scrounging capacitance and the adverse terminal voltage of the PV exhibit concerning earth. To diminish the spillage present, the channel inductor LF have to to be reinstated by a symmetric geography plus the PV framework is redrawn as displayed in the Figure 11.4.

Single Phase 7 Level Multi-Level Inverter   221

L3

S5

D

S1

S3

L1

S6 L2 C3

S8

C1

utility

C2 S7

S2

solar cell array boost power converter isolation switch set

S4

full-bridge inverter

Figure 11.4  Pattern of the planned PV system for suppressing the leak present.

11.5 Controlling for Boost Converter and Inverter In the proposed framework, a Direct Current-Direct Current authority converter and a 7 height inverter. The Direct Current-Direct Current authority converter takes care of the inverter with capacitance furnishing 2 self-governing electrical energy sources with a variety of relations and carry out Maximum Power Point Tracking (MPPT) to separate the greatest force yield from the PV exhibit. The 7-level inverter changes over the capacitance voltage into a high reach AC Voltage for utility voltage framework. To play out the activity of the lift converter and inverter impeccably we are building a controlling framework for both the converter and inverter.

11.6 MATLAB Simulation Results The capacitor circuit contains three reaction signals, indicating that electrical energy ranges (0, Vdc/3), (Vdc/3, 2Vdc/3), and (2Vdc/3, Vdc) are developing. The provide for onward control prevents the usefulness voltages, Vdc/3 and 2Vdc/3, from being disrupted. The total value of the usefulness voltage, as well as the reactions of the separated circuit, is provided, as well as a nourish onward controller for generating the nourish onward signal. The current controller’s reaction and the nourish onward sign are then combined and sent into a Pulse Width Modulation circuit,

222  Smart Grids for Smart Cities Volume 1 which transmits the Pulse Width Modulation sign. To produce a square sign that is synchronized by means of the usefulness electrical energy, the perceived utility voltage is isolated and zeroed. Finally, according to Table 11.1, the Pulse Width Modulation signal, the four-sided figure sign, and the reactions of the stood apart circuit are supplied from the changing circuit to generate the control signals for the 7-height inverter’s force electronic switches. The yield current of the height inverter is controlled by the current controller, which has a sinusoidal symbol of 60 Hz. Because the control circuit employs feed-forward manage, the current controller can function as a key intensifier, resulting in impressive results as shown in Figures 11.5, 11.6, 11.7, 11.8. The current controller’s development picks the transfer speed and the solid state blunder, as shown in (11.6), (11.8), and (11.10). To provide a quick reaction and a low dependable state mistake, the present controller’s extension should be as high as possible. The inductor current is forbidden by the internal present manage circle, which moves toward a constant current and limits the flood voltages in C1 and C2. MPPT is provided using the problem and notice technique. The PV show’s reaction and inductor current are observed and fed into an MPPT regulator, which selects the best yield voltage for the PV bunch. After that,

250 200 150 100 50 0 –50 –100 –150 –200 –250 0.39

0.4

0.41

0.42

0.43

0.44

0.45

0.46

Figure 11.5  Simulation waveforms of the grid voltage & current.

0.47

0.48

0.49

Single Phase 7 Level Multi-Level Inverter   223 400 200 0 –200 –400 20 0 –20 400 200 0 –200 –400 20 0 –20

0.2

0.22

0.24

0.26

0.28

0.3

0.32

Figure 11.6  Simulation waveforms of the output voltage (V), current (A), filtered voltage (V) & filtered current (A).

FFT analysis

Mag (% of Fundamental)

Fundamental (60Hz) = 218.3, THD = 13.59% 10 8 6 4 2 0

0

5 10 Harmonic order

15

Figure 11.7  Total harmonic distortion (THD in %) of the output current (Iout).

224  Smart Grids for Smart Cities Volume 1

Figure 11.8  Seven level inverter output.

a PI controller is used to transmit the PV show’s perceived yield voltage and best yield voltage. The capacitor circuit has 3 reaction signals, which infers the development of electrical energy ranges, (0, Vdc/3), (Vdc/3, 2Vdc/3), plus (2Vdc/3, Vdc). The feed forward control takes out the disruption of the usefulness electrical energy, Vdc/3 and 2Vdc/3. The whole worth of the utility voltage and the reactions of the separated route are given and a nourish onward controller to make the feed forward signal. Then, the reaction of the present manager plus the nourish onward signal are added and given to a Pulse Width Modulation. The inward current control circle’s reference signal is the PI controller’s yield. To finish the internal present manage circle, the orientation sign and saw inductor current are dispatched from a take away or, and the yield is given to an intensifier. The PWM circuit decides the speaker’s yield. The Direct Current – Direct Current authority converter’s authority electronic button require the Pulse Width Modulation circuit, which creates countless free signals.

11.7 Conclusion The PV framework suggested in this study converts the dc power make through a photovoltaic system into power that may be transmitted into the usefulness cross section. The organized PV framework includes a dc-dc authority converter and a height inverter. The 7-height inverter’s circuit design is smoothed out due to the fact that it only has 6 authority electronic switches. Furthermore, only one power electronic button is worked at a high repeat at some arbitrary time to deliver a 7-level yield voltage. This improves influence adequacy by reducing the difficulty of trading influence. Because the voltages of the two direct current capacitors are altered,

Single Phase 7 Level Multi-Level Inverter   225 the proposed 7-level inverter’s control circuit is smoothed out. Test findings demonstrate that the proposed PV construction produces a 7-level yield electrical powerplus a sinusoidal present that is in phase with the utility voltage, resulting in a high authority issue.

References 1. X. She, A. Q. Huang, T. Zhao, and G. Wang, “Coupling effect reduction of a voltage-balancing controller in single-phase cascaded multilevel converters,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3530–3543, Aug. 2012. 2. J. Chavarria, D. Biel, F. Guinjoan, C. Meza, and J. J. Negroni, “Energy balance control of PV cascaded multilevel grid-connected inverters under level-­ shifted and phase-shifted PWMs,” IEEE Trans. Ind. Electron., vol. 60, no. 1, pp. 98–111, Jan. 2013. 3. J. M. Shen, H. L. Jou, and J. C. Wu, “Novel transformer-less grid connected power converter with negative grounding for photovoltaic generation system,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 1818–1829, Apr. 2012. 4. N. A. Rahim, K. Chaniago, and J. Selvaraj, “Single-phase seven-level grid-connected inverter for photovoltaic system,” IEEE Trans. Ind. Electr., vol. 58, no. 6, pp. 2435–2443, Jun. 2011. 5. Y. Ounejjar, K. Al-Hadded, and L. A. Dessaint, “A novel six-band hysteresis control for the packed U cells seven-level converter: Experimental validation,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3808–3816, Oct. 2012. 6. I. Abdalla, J. Corda, and L. Zhang, “Multilevel DC-link inverter and control algorithm to overcome the PV partial shading,” IEEE Trans. Power Electron., vol. 28, no. 1, pp. 11–18, Jan. 2013. 7. Karthikumar, K., Senthil Kumar, V., A new opposition crow search optimizer-­based two-step approach for controlled intentional islanding in microgrids. Soft Comput 25, 2575–2588 (2021). https://doi.org/10.1007/ s00500-020-05280-1. 8. Karthikumar, K., Senthil Kumar, V., Karuppiah, M., Arunbalaj, A., Krishnakumar, S., “Modeling of solar panel as gendralised structure”, International Journal of Applied Engineering Research, 2015, 10(14), pp. 34244–34250. 9. P. Sathyanathan, Dr. P. Usharani (2019) “Design of a single-phase H-Bridge Cascaded Multilevel Inverter 9-level for solar-powered utilities” in International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume 9, Issue 1S, November 2019. 10. M. Babalou, M. Dezhbord, R. S. Alishah, and S. H. Hosseini, “A soft-switched ultra high gain DC-DC converter with reduced stress voltage on semiconductors” Proc. 10th Int. Power Electron. Drive Syst. Technol. Conf. (PEDSTC) pp. 677-682 Feb. 2019.

226  Smart Grids for Smart Cities Volume 1 11. S. P. Syrigos, G. C. Christidis, T. P. Mouselinos, and E. C. Tatakis, “A non-­ isolated DC-DC converter with low voltage stress and high step-down voltage conversion ratio” IET Power Electron. vol. 14 no. 6 pp. 1219-1235 May 2021. 12. M. Zaid, S. Khan, M. D. Siddique, A. Sarwar, J. Ahmad, Z. Sarwer et al., “A transformerless high gain DC–DC boost converter with reduced voltage stress” Int. Trans. Electr. Energy Syst. vol. 31 no. 5, May 2021. 13. A. M. S. S. Andrade, T. Faistel, R. A. Guisso, and A. Toebe, “Hybrid high voltage gain transformerless DC-DC converter”, IEEE Trans. Ind. Electron. Mar. 2021. 14. S. Khan, M. Zaid, A. Mahmood, J. Ahmad, and A. Alam, “A single switch high gain DC-DC converter with reduced voltage stress” Proc. IEEE 7th Uttar Pradesh Sect. Int. Conf. Electr. Electron. Comput. Eng. (UPCON) pp. 1-6 Nov. 2020. 15. M. R. A. Pahlavani and E. S. Asl, “DC–DC SIDO converter with low-voltage stress on switches: Analysis of operating modes and design considerations” IET Power Electron. vol. 13 no. 2 pp. 233-247 Feb. 2020.

12 An Enhanced Multi-Level Inverter Topology for HEV Applications Premkumar E.1 and Kanimozhi G.2* School of Electrical Engineering, Vellore Institute of Technology, Chennai, India 2 Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India

1

Abstract

This paper presents an Enhanced Multi-Level Inverter (E-MLI) topology with better performance characteristics for electric hybrid vehicle applications. The 7-level E-MLI operates in Switched Capacitor (SC) mode. The inverter operating in this mode is more self-balanced and produces more output voltage with only one input supply. To justify the topology’s performance, it is compared to a Diode Clamped Multi-Level Inverter (DC-MLI) topology in terms of efficiency and Total Harmonic Distortion (THD). Results obtained for the above study have been validated in MATLAB Simulink, and the findings have been summarized. Keywords:  Multi-level inverter, diode clamped inverter, switched-capacitor, current, voltage harmonics

12.1 Introduction Multi-Level Inverters (MLI) find a significant part in energy change frameworks like Electric vehicles and Renewable energy. The converters used, which comprise DC voltage sources, power diodes and active power switches, can deliver staircase voltage waveforms with lower THD. Power switches in MLIs typically endure reduced voltage stress in examination with the conventional 3-level voltage source inverters. Different benefits of MLIs are reducing the output filter size and losses. Though, the normal MLIs like Neutral Point Clamped (NPC) and Flying Capacitor (FC) have *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (227–252) © 2023 Scrivener Publishing LLC

227

228  Smart Grids for Smart Cities Volume 1 difficulties in regard to capacitor voltage balancing, particularly when the quantity of levels increases. Separate DC voltage supplies are needed for Cascaded H-bridge (CHB) MLIs. MLIs have traditionally been organized into three categories: Cascaded H-bridge, Diode Clamped, and the Flying capacitors. Because of their great performance, these inverters play a key role in many industrial applications in terms of motor drivers and high-quality ac supplies. Their disadvantages, on the other hand, are readily obvious. For H-bridge cascade topologies, for example, many isolated voltage sources are necessary. In both Flying capacitor inverters and neutral-point-clamped, the voltage balancing between dc-link series capacitors is a problem. In the last decade, many new MLI topologies have been published in [1–5], none of which fit within the standard three classes. In the modern multi-level inverter topology [1], numerous full-bridge converters and sub-multi-level converter units are used. Although [2] proposes a simplified structure, multiple independent DC voltage supplies are still necessary. [4] developed the coupled-inductor approach for multi-level inverters. Although the structures are simplified, expanding this approach to higher-­ level applications is tough. The multi-level topology presented in [3], on the other hand, can be scaled to higher levels. Indeed, the size, price, and quantity of component drivers all grow when more power switches are used. This study presents an E-MLI inverter topology connected to a basic RL load based on the SC approach that can be used in various applications. Only one input voltage is required with the improved design, and other issues like many power switches, complex gate drivers, and voltages balancing are minimized [6–8]. Hence, the E-MLI output voltage levels may be changed flexibly by using a different number of SC cells. In section 12.2, the switching operation of the E-MLI with different output voltage levels and the operation of DC-MLI are discussed. In section 12.3, Pulse Width Modulation (PWM) techniques along with switching strategies for the E-MLI as well as the DC-MLI are explained. Section 12.4 summarizes the Simulation models, results, and discussions consisting of comparative study with DC-MLI. The conclusion of this paper is presented in section 12.5.

12.2 E-MLI Topology The E-MLI topology [9] has been presented in Figure 12.1. It comprises power switches from SW1 to SW11 and only one bidirectional switch, i.e., SW4. In

An E-MLI Topology for HEV Applications  229 SW1

A

C1

VC1

SW3

SW2

C2

VC2

SW4

SW5

SW7

SW6

SW9

SW8 Vdc

SW11

SW10

B

Figure 12.1  E-MLI topology circuit arrangements.

the capacitor switched mode, it connects two capacitors (i.e., VC1 and VC2) and one input DC voltage supply (VDC). In the operation of capacitor switched mode, the capacitors are attached in series with the DC voltage supply during discharge and parallel with the DC voltage supply during charging due to the MLI’s switching sequence configurations [10–13]. The E-MLI can produce seven-level output voltages such as 0, ±VDC, ±2VDC, and ±3VDC.

12.2.1 Switching Operation of the E-MLI Topology The control function of the E-MLI detailed for the negative half cycle. is described in this section. Table 12.1 presents the switching states and working of the E-MLI.

230  Smart Grids for Smart Cities Volume 1

Table 12.1  E-MLI topology switching states. Switching sequences

Capacitor supply output

SW1 SW2 SW3 SW4 SW5 SW6 SW7 SW8 SW9 SW10 SW11 VC1

VC2

0

1

0

1

1

1

0

1

1

0

1

-

+Vdc 0

0

1

0

1

1

1

0

1

1

1

0

-

+Vdc +Vdc +Vdc

0

1

1

1

0

0

1

1

1

1

0

+Vdc 0

+Vdc +2Vdc

0

1

1

0

0

1

1

0

1

1

0

0

+Vdc +3Vdc

1

0

1

1

0

0

1

1

1

1

0

+Vdc -

0

0

1

0

1

1

0

0

1

1

1

0

1

+Vdc -

-Vdc

-Vdc

1

0

0

1

1

1

0

1

1

0

1

0

+Vdc -Vdc

-2Vdc

1

0

0

0

1

1

1

1

0

0

1

0

0

-3Vdc

0

Vdc

-Vdc

VAB 0

An E-MLI Topology for HEV Applications  231 a) Zero Voltage Level (VAB = 0): SW1, SW3, SW8, and SW10 power switches are triggered to generate the zero voltage level. The power switches SW4, SW7, and SW9 are also triggered to charge the capacitor VC1 by input DC voltage supply -VDC. VAB = 0 and VC1 = -VDC is produced from this switching sequence. By disconnecting the SW5 and SW6 power switches, capacitor VC2 is entirely isolated from the DC voltage supply. b) First Negative Voltage Level (VAB = -VDC): Turning on the SW1, SW3, SW8, and SW11 switches produce this voltage level. SW4, SW7, and SW9 remained on in the same way as in the previous scenario to keep the capacitor VC1 charged. The DC supply is delivering the voltage to the RL load by connecting it in series in this case, and the output voltage is VAB = -VDC. The condition of capacitor VC2 is not changed. c) Second Negative Voltage Level (VAB = -2VDC): Switches SW1, SW4, SW6, SW8, and SW11 are used at this level to attach the fully charged capacitor VC1 in series with the input DC voltage supply -VDC and use the energy stored in it. Therefore, over the load VAB, a voltage of -2VDC is produced. The capacitor VC2 is connected and charged in parallel with the input DC source by turning on switches SW5 and SW9. d) Third Negative Voltage Level (VAB = -3VDC): Using SW1, SW5, SW6, SW7, SW8, and SW11 switches, as well as the energies stored in both capacitors, the E-MLI generates the highest voltage in this condition. As a result, VAB = -3VDC is the output voltage. After the negative cycle has completed, the positive cycle begins. The capacitors charging and discharging states have been reversed in that case. During the whole operation cycle, both capacitors have identical charging and discharging times. It confirms that the voltages of both capacitors are balanced. With reference to voltage steps (NL) delivered by the MLI, the equations employed in this E-MLI topology are as follows:





DC voltage sources required (N DC ), N DC

(N L 1) (12.1) 6

Switches required (NS), NS = 2(NL – 1) + 1

(12.2)

(N L 1) 3

(12.3)

Capacitors required (NC ), NC

232  Smart Grids for Smart Cities Volume 1

Maximum voltage (VMax ), VMax



(N L 1) 2 VDC

(12.4)

Where NL represents the number of voltage steps in the E-MLI.

12.2.2 Diode-Clamped Multi-Level Inverter (DC-MLI) The inverter shown in Figure 12.2 is also known as a Neutral-Point Clamped Inverter (NPC). The usage of voltage clamping diodes is critical in the NPC-MLI topology. An even number of bulk capacitors in series

S1

C1

D1 S2 D2

C2

S3 S4

D3

C3 Vdc

S5

D4

D5

D1' D2'

+100V

D3'

C4

C5 D5'

D4'

S6 S1' S2' S3' S4'

C6

S5' S6'

Figure 12.2  Circuit topology of 7-level DC-MLI.

An E-MLI Topology for HEV Applications  233 with a neutral point in the middle of the line split of a common DC bus. The quantity of capacitors is determined by the inverter’s voltage levels.



An ‘NL’ level inverter needs, NDC = (NL – 1)

NS = 2(NL – 1)

(12.5) (12.6)

Number of Diodes required (ND), ND = (NL – 1) ∗ (NL – 2)

(12.7)

NC = (NL - 1)

(12.8)

The output voltage quality improves as the number of voltage steps grows, and the output voltage approaches that of a sinusoidal waveform [14]. Advantages: • The converter’s capacitance requirements are reduced because all of the phases share a common DC bus. • It is possible to pre-charge all of the capacitors at the same time. • Fundamental frequency switching has high efficiency. Disadvantages: • Without accurate monitoring and control, the intermediate DC voltage levels of the capacitors are likely overcharge or discharge. • The problem of capacitor voltage balance necessitates complicated modulation. • Clamping diodes required are proportional to levels of the inverter that might be inconvenient for systems with many levels.

12.3 PWM for the E-MLI Topology This section elaborates on the PWM techniques employed for EMLI topology.

234  Smart Grids for Smart Cities Volume 1

12.3.1 SPWM Based Switching for the E-MLI Topology A level-shifted SPWM approach is used to produce the improved modules gate pulses. The SPWM proposed is based on a carrier wave arrangement in which all positive plane carriers are 180° out of phase with negative plane carrier [15, 16]. This module can generate NL=7 for SC operation, it needs 6 carrier signals, each with an amplitude of Acr and a frequency of fcr. The equations for the SPWM approach are provided in (12.9) and (12.10) below:





Frequency modulation (m f ), m f

Amplitude modulation (ma ), ma

f cr f

(12.9)

Am (N L 1) (12.10) Acr

The modulating signal’s amplitude is Am (VAB ref). Figure 12.3 (a) depicts the control diagram for the SPWM technique as well as the carrier wave production. To utilize this technique, first, determine which carrier signals are below and above the zero references. Every carrier signal is compared with the reference signal at any given time [17]. When the positive carrier signals are lower than the reference signals, the comparison yield ‘1’ as output and ‘0’ otherwise. Negative carriers, on the other hand, if they are larger than the reference wave, the comparison provide ‘-1’ as output and ‘0’ otherwise.

12.3.2 Phase Opposition Disposition (POD) Scheme for DC-MLI In POD, the carrier in the positive plane is 180° out of phase with the negative plane [18, 19]. There are no harmonics in the carrier frequency and its multiples and the dispersion of harmonics takes place around them. Figure 12.4 (a) depicts the control diagram for POD scheme-based pulse generation and Figure 12.4 (b) depicts the POD output of DC-MLI.

An E-MLI Topology for HEV Applications  235 Reference Signal

IV

Positive Carrier1

Positive Carrier2 IV

+ +

Positive Carrier3

Negative Carrier1

IV

+ +

–1

+

VI

x

VI

x

u

+

fcn

y

Negative Carrier2

Negative Carrier3 VI

x

(a) 3 2 1 0 –1 –2 –3 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

(b)

Figure 12.3  (a) SPWM based switching for the E-MLI topology. (b) PWM output of the adder to generate a switching sequence of the E-MLI topology.

236  Smart Grids for Smart Cities Volume 1 Positive Carrier 1

Positive Carrier 2

NOT

NOT

Positive Carrier 3 ≥

Negative Carrier 1

NOT

Reference Signal

NOT

Negative Carrier 2 Mux1 Sine Wave1

7

1

8

2

9

3

10

4

NOT

11

5

NOT

12

6

Negative Carrier 3

(a) 3 2 1 0 –1 –2 –3 0.94

0.942

0.944

0.946

0.948

0.95 (b)

0.952

0.954

0.955

0.958

0.96

Figure 12.4  (a) POD scheme for 7 levels DC-MLI. (b) POD output for 7 level DC-MLI.

12.4 Simulation Results & Discussions For the simulation, MATLAB Simulink has been used to validate the results. The MATLAB Simulink model for the E-MLI topology [20] is portrayed in Figure 12.5. The result is tabulated and the performance parameters of the E-MLI topology are compared with the 7-level DC-MLI. The THD spectrums as well as the efficiency of the above MLIs are displayed to justify their performances. Table 12.2 gives the input parameters used to simulate the E-MLI topology. The DC supply voltage and current Figure 12.6(a), the output load voltage and current Figure 12.6(b), the THD of both current and voltage (VTHD & ITHD) in Figure 12.6(c) and Figure 12.6(d) for E-MLI with Ma=3. The DC supply voltage and current are given in Figure 12.7(a). The output load

An E-MLI Topology for HEV Applications  237

E

[S11]

c

11

[S8]

g

[S9] [S10]

E

9 10

[S3]

E

E

E

c

m

g

–

c

[S8]

[S4]

g

[S7]

8

E

E

7

[S5]

E

E

E

[S6]

+

g

[S5]

6

[S2]

c

5

g

c g

[S4]

c

m

Parallel RLC Branch

E

4

E

E

[S3]

[S1]

g c

[S2]

3

g

E

2

c

m

c

[S1]

g

1

E

[S6]

+

E

c

m

g

[S7]

[S9] [S11]

Figure 12.5  MATLAB Simulink model of the E-MLI topology.

c

g

g E

E

c E

[S10]

E

+v –

238  Smart Grids for Smart Cities Volume 1

Parameters

Specifications

DC Source (VMax)

100 V

Capacitance (C)

2700 μF

Ma

3, 2.75, 2.5, 2.25, and 2

Frequency (Fr)

50 Hz

Carrier Frequency (FC)

2.5 kHz

Load Resistance (R)

100 Ohms

Load Inductance (L)

0.01 H Input DC Voltage

110 105 100 95 90

Input DC Current

800 600 400 200 0 0.11

0.12

0.13

0.14

0.15

0.16

(a)

0.17

0.18

0.19

0.2

Output Voltage 200 0 –200

Output Current in Amps

Output Voltage in volts

Input DC Current in Amps Input DC Voltage in volts

Table 12.2  Inverter specification used for E-MLI.

Output Current 2 0 –2 0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

(b)

Figure 12.6  (a) DC supply voltage and current of the E-MLI (Ma=3). (b) Output load voltage and current of the E-MLI (Ma=3).  (Continued)

An E-MLI Topology for HEV Applications  239 Fundamental (50Hz) = 297.1, THD = 18.27%

Mag (% of Fundamental)

0.5 0.4 0.3 0.2 0.1 0

0

2

4

6

8 10 12 Harmonic order (c)

14

16

18

20

16

18

20

Fundamental (50Hz) = 2.969, THD = 8.07%

Mag (% of Fundamental)

0.5 0.4 0.3 0.2 0.1 0

0

2

4

6

8 10 12 Harmonic order (d)

14

Figure 12.6 (Continued)  (c) VTHD for the E-MLI (Ma=3). (d) ITHD for the E-MLI (Ma=3).

Input DC Current in Amps

Input DC Voltage in volts

240  Smart Grids for Smart Cities Volume 1 Input DC Voltage

110 105 100 95 90

Input DC Current

600 400 200 0 0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.2

Output Voltage 200 0 –200

Output Current in Amps

Output Voltage in volts

(a)

Output Current 2 0 –2 0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.2

(b)

Figure 12.7  (a) DC Supply voltage and current of the E-MLI (Ma=2.5). (b) Output load voltage and current of the E-MLI (Ma=2.5).  (Continued)

An E-MLI Topology for HEV Applications  241 Fundamental (50Hz) = 248.4, THD = 23.94%

0.6

Mag (% of Fundamental)

0.5 0.4 0.3 0.2 0.1 0

0

2

4

6

8 10 12 Harmonic order (c)

14

16

18

20

Fundamental (50Hz) = 2.483, THD = 10.95% 0.5

Mag (% of Fundamental)

0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0

0

2

4

6

8 10 12 Harmonic order (d)

14

16

18

20

Figure 12.7 (Continued)  (c) VTHD of the E-MLI (Ma=2.5). (d) ITHD of the E-MLI (Ma=2.5).

242  Smart Grids for Smart Cities Volume 1 Table 12.3  Inverter specification used for DC-MLI simulation. Parameters

Specifications

DC Source (VMax)

600 V

Capacitance (C)

47 μF

Ma

3, 2.75, 2.5, 2.25, and 2

Frequency (Fr)

50 Hz

Carrier Frequency (FC)

2.5 kHz

Load Resistance (R)

100 Ohms

Load Inductance (L)

0.01 H

voltage and current (refer Figure 12.7(b)), the THD of both current and voltage (VTHD & ITHD) in Figure 12.7(c) and Figure 12.7(d) for E-MLI are displayed for Ma = 2.5. Table 12.3 gives the input parameters used to simulate the DC-MLI topology. The DC supply voltage and current are illustrated in Figure 12.8 (a). For DC-MLI, the output load voltage and current (refer Figure 12.8 (b)), THD of both current and voltage (VTHD & ITHD) in Figure 12.8(c) and, Figure 12.8 (d) are portrayed for Ma = 3. The DC supply voltage and current are represented in Figure 12.9 (a) for DC-MLI with Ma = 2.5. The corresponding output load voltage and current are represented in Figure 12.9 (b). THD of both current and voltage (VTHD & ITHD) is depicted Figure 12.9(c) and Figure 12.9 (d) for DC-MLI with Ma=2.5. The efficiency of the E-MLI topology and the DC-MLI for various modulation indexes (Ma=2, 2.25, 2.5, 2.75, 3), are calculated. The results are tabulated below to justify its performance over various modulation indexes. The efficiency of the E-MLI over the DC-MLI has also been plotted to compare their performance. The current and voltage harmonics of the E-MLI and the DC-MLI are tabulated to determine the level of harmonics.

An E-MLI Topology for HEV Applications  243

Input DC Current in Amps Input DC Voltage in volts

Table 12.4 and Table 12.5 shows that the efficiency of the E-MLI model is higher when compared to the POD based DC-MLI. The efficiency remains the same for various modulation indexes in the E-MLI topology as portrayed in Figure 12.10(a) then for the DC-MLI, when the modulation index decreases the efficiency decreases as shown in Figure 12.11(a). The choice of choosing modulation indexes depends on the type of application where Input DC Voltage

700 650 600 550 500

Input DC Current 1.5 1 0.5 0 0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

Output Voltage in volts

(a) Output Voltage 200 0

Output Current in Amps

–200 Output Current 2 0 –2 0.1

0.11

0.12

0.13

0.14

0.15

(b)

0.16

0.17

0.18

0.19

0.2

Figure 12.8  (a) DC supply voltage and current of the DC-MLI for (Ma=3). (b) Output load voltage and current of the DC-MLI for (Ma=3).  (Continued)

244  Smart Grids for Smart Cities Volume 1 Fundamental (50Hz) = 289, THD = 17.37%

1.4

Mag (% of Fundamental)

1.2 1 0.8 0.6 0.4 0.2 0

0

2

4

6

8 10 Harmonic order

12

14

16

18

20

(c)

Fundamental (50Hz) = 2.88, THD = 7.78%

Mag (% of Fundamental)

0.6 0.5 0.4 0.3 0.2 0.1 0

0

2

4

6

8 10 Harmonic order

12

14

16

18

20

(d)

Figure 12.8 (Continued)  (c) VTHD of the DCMLI for (Ma=3). (d) ITHD of the DCMLI for (Ma=3).

Input DC Voltage

700 650 600 550 500

Input DC Current in Amps

Input DC Voltage in volts

An E-MLI Topology for HEV Applications  245

Input DC Current

1.5 1 0.5 0 0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

0.2

(a) Output Voltage in volts

Output Voltage 300 200 100 0 –100 –200 –300

Output Current in Amps

Output Current 2 0 –2 0.1

0.11

0.12

0.13

0.14

0.15

0.16

0.17

0.18

0.19

(b)

Figure 12.9  (a) DC supply voltage and current of DC-MLI for (Ma=2.5). (b) Output load voltage and current of the DC-MLI for (Ma=2.5).  (Continued)

246  Smart Grids for Smart Cities Volume 1 Fundamental (50Hz) = 238.5, THD = 24.04%

Mag (% of Fundamental)

1.5

1

0.5

0

0

2

4

6

8 10 Harmonic order

12

14

16

18

20

(c) Fundamental (50Hz) = 2.396, THD = 10.64%

Mag (% of Fundamental)

0.6 0.5 0.4 0.3 0.2 0.1 0

0

2

4

6

8 10 Harmonic order

12

14

16

18

20

(d)

Figure 12.9 (Continued)  (c) VTHD of the DCMLI for (Ma=2.5). (d) ITHD of the DCMLI for (Ma=2.5).

An E-MLI Topology for HEV Applications  247 Table 12.4  The performance parameters of the E-MLI for various modulation indexes (Ma). Ma

Vin

Iin

Vout

Iout

VTHD ITHD

Pin

-

Volts Amps Volts Amps %

3

100

4.561

213.4 2.105

18.27 8.07

456.1

449.2

98.49

2.75

100

3.854

197.1 1.934

21.92 9.98

385.4

381.2

98.91

2.5

100

3.2

179.5 1.775

23.94 10.95 320.0

315.0

98.44

2.25

100

2.596

161.8 1.579

24.67 10.63 259.6

255.5

98.41

2

100

2.102

146.1 1.421

26.85 11.8

207.6

98.77

%

Pout

Efficiency

Watts Watts %

210.2

Table 12.5  The performance parameters of DC-MLI for various modulation indexes (Ma). Ma

Vin

3

Vout

Iout

VTHD

ITHD

Pin

Volts Amps

Volts

Amps

%

%

Watts Watts %

600

0.7398

206.8

2.037

17.37 7.78

443.9

421.3 94.90

2.75 600

0.6225

192.5

1.878

21.83 9.7

373.5

361.5 96.79

2.5

Pout

Efficiency

0.544

172.31 1.699

24.51 10.6

326.4

292.8 89.69

2.25 600

0.4283

154.4

1.523

24.14 10.37 257.0

235.2 91.51

2

0.3526

139

1.34

26.74 11.56 211.6

186.3 88.04

600

120.00

30

100.00 98.77 80.00

25 26.85

98.41

98.44

98.91

98.49 THD (%)

Efficiency (%)

600

Iin

60.00 40.00

24.67

2

(a)

2.2

2.4 2.6 Modulation Index

2.8

3

23.94

21.92 18.27

15 1011.8

10.63

5

20.00 0.00

20

0

2

(b)

2.2

10.95

9.98

2.4 2.6 2.8 Modulation Index

VTHD ITHD

8.07 3

Figure 12.10  E-MLI for various modulation indexes (a) efficiency (b) THD spectrum.

248  Smart Grids for Smart Cities Volume 1 MLI is used. Similarly, when the modulation index increases the current harmonics (I THD) and the voltage harmonics (V THD) decrease for both E-MLI (refer Figure 12.10(b)) and the DC-MLI (refer Figure 12.11(b)). The efficiency and harmonics of the E-MLI over the DC-MLI has also been plotted in Figure 12.12(a), (b) and (c) to compare their performances. The number of switches, diodes, capacitors and DC sources used for both E-MLI as well as DC-MLI are tabulated in Table 12.6. It presents the 120.00

30 91.51

80.00 88.04

96.79

89.69

25 26.74

94.90

20

THD (%)

Efficiency (%)

100.00

60.00 40.00

10 5

0.00

0

2.2

(a)

2.4 2.6 Modulation Index

2.8

21.83 17.37

15

20.00 2

24.51

24.14

3

11.56

2

10.6

10.37

2.2

9.7

2.4 2.6 Modulation Index

(b)

VTHD ITHD

7.78

2.8

3

Figure 12.11  DC-MLI for various modulation indexes (a) efficiency (b) THD spectrum. E-MLI 100.00 98.77

98.41

98.00

DC-MLI 98.91

98.44

98.49

96.79

Efficiency (%)

96.00

94.90

94.00

91.51

92.00

89.69

90.00 88.04 88.00 86.00 84.00 82.00 80.00 2

2.1

2.2

2.3

2.4 2.5 2.6 Modulation Index

(a)

VTHD E-MLI

E-MLI

ITHD (%) 2

2.2

2.4 2.6 Modulation Index

(b)

2.8

2.8

3

2.9

3

ITHD

DC-MLI

VTHD (%)

13 12 11 10 9 8 7 6 5

2.7

29 27 25 23 21 19 17 15

2

2.2

DC-MLI

2.4 2.6 Modulation Index

2.8

(c)

Figure 12.12  (a), (b) and (c) efficiency and harmonic spectrum of E-MLI over DC-MLI.

3

An E-MLI Topology for HEV Applications  249 Table 12.6  Table showing the components used in the E-MLI and DCMLI. Parameters

E-MLI

DC-MLI

NDC

01

01

NS

11

12

ND

0

30

NC

02

06

comparison of the parameters of the E-MLI over the DC-MLI. The E-MLI has reduced number of capacitors and other components, thereby reducing the size of the inverter, and hence losses are reduced and efficiency is improved.

12.5 Conclusion In the above discussions, the efficiency of the E-MLI is compared with the POD-based DC-MLI. The result shows that the E-MLI topology has an efficiency of 98.49% when Ma=3 and the current harmonics of 8.07%. When the same condition is applied for the DC-MLI, it possesses an efficiency of 94.90% and current harmonics of 8.07%. Thus, it can be concluded that the E-MLI has higher efficiency than the DC-MLI, due to the reduction of total switches in the topology. The harmonics in the current waveform is found to be slightly higher in the E-MLI topology than the DC-MLI. The E-MLI work can be enhanced by performing stability analysis.

References 1. Javad Ebrahimi, Ebrahim Babaei, and Gevorg B. Gharehpetian, “A New Multilevel Converter Topology with Reduced Number of Power Electronic Components,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 655–667, Feb. 2012. 2. Ehsan Najafi, and Abdul Halim Mohamed Yatim, “Design and Implementation of a New Multilevel Inverter Topology,” IEEE Trans. Ind. Electron., vol. 59, no. 11, pp. 4148–4154, Nov. 2012. 3. Youhei Hinago, and Hirotaka Koizumi, “A Switched-Capacitor Inverter Using Series/Parallel Conversion With Inductive Load,” IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 878–887, Feb. 2012.

250  Smart Grids for Smart Cities Volume 1 4. Zixin Li, Ping Wang, Yaohua Li, and Fanqiang Gao, “A Novel Single-Phase Five-Level Inverter with Coupled Inductors,” IEEE Trans. Power Electron., vol. 27, no. 6, pp. 2716–2725, Jun. 2012. 5. Jia-Min Shen, Hurng-Liahng Jou, Jinn-Chang Wu, Kuen-Der Wu, “FiveLevel Inverter for Renewable Power Generation System,” IEEE Trans. Energy Convers., vol. 28, no. 2 pp. 257-266, Jun. 2013. 6. D. Ahmadi and J. Wang, “Online selective harmonic compensation and power generation with distributed energy resources,’’ IEEE Trans. PowerElectron., vol. 29, no. 7, pp. 3738-3747, Jul. 2014. 7. Y. Ye, K.W. E. Cheng, J. Liu, and K. Ding, “A step-up switched-capacitor multilevel inverter with self-voltage balancing,’’ IEEE Trans. Ind. Electron., vol. 61, no. 12, pp. 6672-6680, Mar. 2014. 8. K. K. Gupta and S. Jain, “A novel multilevel inverter based on switched DC sources,’’ IEEE Trans. Ind. Electron., vol. 61, no. 7, pp. 3269-3278, Jul. 2014. 9. E. Babaei, M. F. Kangarlu, and M. Sabahi, “Extended multilevel converters: An attempt to reduce the number of independent DC voltage sources in cascaded multilevel converters,’’ IET Power Electron., vol. 7, no. 1, pp. 157-166, Jan. 2014. 10. R. Raushan, B. Mahato, and K. C. Jana, “Comprehensive analysis of a novel three-phase multilevel inverter with minimum number of switches,’’ IET Power Electron., vol. 9, no. 8, pp. 1600-1607, Jun. 2016. 11. P. Kala and S. Arora, “A comprehensive study of classical and hybrid multilevel inverter topologies for renewable energy applications,’’ Renew. Sustain. Energy Rev., vol. 76, pp. 905-931, Sep. 2017. 12. B. Mahato, S. Mittal, S. Majumdar, K. C. Jana, and P. K. Nayak,” Multi-level inverter with optimal reduction of power semiconductor switches,’’ Renew. Energy Innov. Technol., pp. 31-50, 2018. 13. Y. C. Fong, S. R. Raman, M. M. Chen, and K. W. E. Cheng, “A novel switched-capacitor multilevel inverter offering modularity in design,’’ in Proc. IEEE Appl. Power Electron. Conf. Expo. (APEC), Mar. 2018, pp. 1635-1640. 14. A. Taghvaie, J. Adabi, and M. Rezanejad, “A self-balanced step-up multi-level inverter based on switched-capacitor structure,’’ IEEE Trans. Power Electron., vol. 33, no. 1, pp. 199-209, Jan. 2018. 15. H. K. Jahan, M. Abapour, and K. Zare, “Switched-capacitor-based singlesource cascaded H-bridge multilevel inverter featuring boosting ability,’’ IEEE Trans. Power Electron., vol. 34, no. 2, pp. 1113-1124, Feb. 2019. 16. X.-J. Ge, Y. Sun, Z.-H. Wang, and C.-S. Tang, “A single-source switched-­ capacitor multilevel inverter for magnetic coupling wireless power transfer systems,’’ Electr. Eng., vol. 101, no. 4, pp. 1083-1094, Dec. 2019. 17. S. T. Meraj, N. Z. Yahaya, K. Hasan, and A. Masaoud, “Single phase 21 level hybrid multilevel inverter with reduced power components employing low frequency modulation technique,’’ Int. J. Power Electron. Drive Syst., vol. 11, no. 2, pp. 810-822, 2020.

An E-MLI Topology for HEV Applications  251 18. A. K. M. A. Habib, M. K. Hasan, M. Mahmud, S. M. A. Motakabber, M. I. Ibrahimya, and S. Islam, “A review: Energy storage system and balancing circuits for electric vehicle application,’’ IET Power Electron., vol. 14, no. 1, pp. 113, Jan. 2021. 19. L. G. Fernandes, A. A. Badin, D. F. Cortez, R. Gules, E. F. R. Romaneli, and A. Assef, “Transformerless UPS system based on the half-bridge hybrid switched-capacitor operating as AC-DC and DC-DC converter,’’ IEEE Trans. Ind. Electron., vol. 68, no. 3, pp. 2173-2183, Mar. 2021. 20. S. T. Meraj et al., “A Diamond Shaped Multilevel Inverter with Dual Mode of Operation,” in IEEE Access, vol. 9, pp. 59873-59887, 2021, doi: 10.1109/ ACCESS.2021.3067139.

13 Improved Sheep Flock Heredity Algorithm-Based Optimal Pricing of RP P. Booma Devi1*, Booma Jayapalan2 and A.P. Jagadeesan3 Department of Electrical & Electronics Engineering, Christian College of Engineering & Technology, Dindigul, India 2 Department of Electronics & Communication Engineering, PSNA College of Engineering and Technology, Dindigul, India 3 Department of Computer Science and Engineering, RVS College of Engineering, Dindigul, India 1

Abstract

One of the rapidly growing research fields in the power system is most favorable cost allocation. Wind Farms (WFs) have been promoted as a cost-effective and long-term alternative to nonrenewable energy sources. The Windmill’s induction generator needs a substantial amount of Reactive Power (RP) for active power transmission. Using generators, shunt capacitors and Static VAr compensators (SVCs) RP is supplied. The cost of reactive power was previously investigated and altered using a Particle Swarm Optimization (PSO) process depending on usage characteristics and quality. The Improved Sheep Flock Heredity Algorithm (ISFHA) is used in this paper to achieve optimal RP pricing using power flow tracing. The RP is calculated using the power flow tracing approach, which selects only the generators with the fewest harmonics, minimizing power loss and increasing RP generation, based on the maximum load ability limitations, transmission power, reserve, and power loss provided. ISFHA’s local and global searching processes assist in obtaining the best price. For Realistic Seventy-Five Bus Indian System (RSFIBS) wind farms, simulation-based tests are implemented and confirmed using MATLAB software, and the results are compared to the existing HPSO and PSO to show that the proposed ISFHA is sound and realistic.

*Corresponding author: [email protected] P. Booma Devi: (ORCID: https://orcid.org/0000-0001-5925-1013) Booma Jayapalan: (ORCID: https://orcid.org/0000-0001-8073-0668) O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (253–268) © 2023 Scrivener Publishing LLC

253

254  Smart Grids for Smart Cities Volume 1 Keywords:  Reactive power, reactive power pricing, power flow tracing, realistic seventy-five bus Indian system (RSFIBS), embedded cost

13.1 Introduction Transmission of real power is mostly supported by RP. Because the number of transactions is increased with the assistance of voltage and the transmission system, RP has a critical task in eliminating unnecessary power transfer. Self-balanced differential evolution algorithm using block chain technology is proposed in the microgrid architecture for estimating the cost of RP [1]. In [2], 10% increase in RP demand and input wind power are introduced for analyzing the compensation effect in nominal voltage level. A novel improved differential evolution algorithm adopted for optimal RP dispatch is proposed [3]. In [4], coordination of linear active and RP optimization model for reducing the operational cost is presented and tested on IEEE 33-bus system. A survey on the various algorithms for RP Cost Management in Wind Farms is discussed [5]. Paper [6] proposed a novel investment scheduling software called NETPLAN. This NETPLAN is used to recognize nationwide investment strategies for meeting expectations using network topology. As per the wind speed variations, the outfitted effects of wind generation are modeled. The main considerations are price, discharge of pollutants and system flexibility. Additionally, the requisite yearly reserves are allocated on an hourly basis. In the view of paper [7] sorted out a novel methodology of optimization with genetic algorithms to assess the introduction of the Thyristor-Controlled Series Capacitors (TCSCs) and SVCs both technically and economically in a transmission network to assist the integration of wind power and also the congested regions are identified. Maximum loading on the network is identified to be not responsible for maximum profit. Reduction in savings occurs due to the diversion of power away from blocking due to the additional load. In this paper [8], the hypothesis of Electricity Tracing was developed to eradicate the unpredictability in addition to biasing variations. Then biasing variation in the service charge distribution also support with a reasonable and unbiased pricing of transmission service of a deregulated power system were also viewed. The complication in computation is resolved with tolerable computational time using load tracing method of optimization. [9] have recommended a pale and realistic cost allocation and pricing of RP based on the principle of power tracing with the inclusion of the capacitance (line charging) as a detach foundation of RP. The bidirectional RP flow problem

Optimal Reactive Power Pricing   255 can be handled by implementing a very simple circuit theory modeling. Paper [11] recognized the concept of an integrated dispatch of real and RP in a competitive context utilizing a two-level optimization crisis with the greater and subordinate level criterion of least opportunity cost and least accessible price of real power respectively. It also investigated the impact of lowering the opportunity price criterion on system functioning, as well as its performance on real and RP pricing. [10] proposed a multiobjective simulated annealing strategy for RP adjustment that took into account the capacitors size and configuration. [12] devised a tracing method and economically defined RP pricing by factoring in the direct and indirect costs of generation and transmission. A branch approach for calculating generator involvement has also been developed. [13] presented a proportional analysis of a mixture of symbol of RP limits in highest load ability, with a particular emphasis on the appropriate modeling of generator capability curves during changes in voltages (terminal), which has been a limitation in preceding studies. [14] presented RP optimization utilizing PSO to reduce cost allocation by minimizing overall support cost from generators and reactive compensators by maintaining minimum power loss in the entire system. Reactive Optimal Power Flow (OPF) has been created to overcome the challenge of reactive dispatch in order to attain this goal. [17] suggested a value-based sensitivity technique for computing both the utilization factor for distributing the RP production/absorption cost and the participation factor for computing the VAr reserve to support system security and reimbursement for its used capacity. GAMS software is used to analyze and show the data in this method. Because the cost of VAr assistance for MW load transportation is higher than the net cost, all generators pay the same amount in this strategy. [15] developed a mathematical model to examine the deliberate behavior of system, which taking into account the schedule of the system operator. To regulate the effects of strategic behaviour, some regulatory systems have been established. Another alternative, known as the price cap method, is offered to reduce the impact of generator strategic behaviour on RP procurement by the system operator and to ease market power abuse. [16] developed a novel RP pricing strategy based on a tracing algorithm that accounts for the cost of both active and RP loss, as well as all investment, operation, and opportunity costs associated with RP support. [18] reported minute-by-minute wind power changes divided into slow, rapid, and ramp components, as well as an assessment of each component’s effect on power system functioning. To study the system response for fast wind fluctuations, detailed, long-term simulation models are developed and enhanced to include load dynamics and AGC time delays. From the standpoint of system reliability,

256  Smart Grids for Smart Cities Volume 1 however, creating a long-term penetration strategy is not recommended. For appropriate placement of SVCs in power networks, [19] developed a new RP spot price index at each bus based on RP spot prices under various loading situations. This methodology for locating SVC placement aids in the reduction of real and RP prices in both normal and critical unforeseen event situations. With the help of an optimization model, [20] examined the general backdrop, objectives, constraints, and solution techniques of RP planning. RP’s production cost was overlooked in traditional OPF models. Power factor penalties have been used to price reactive energy for decades. Furthermore, traditional optimization techniques rely on a number of mathematical assumptions, such as the objective function’s analytic and differential qualities, and may only yield a local optimum solution in some cases. These models, which are derived from the calculation of power factor and the matrix inversion process, also result in convoluted pricing behaviour and take a long time to complete. Because of its volatile and chaotic nature, the minimal RP price determined using traditional OPF algorithms cannot be used in practice. Furthermore, due to the great limitations in solving real-time bigger power systems and the inability to address qualitative constraints, traditional methods necessitate the simplification of mathematical formulations in order to get solutions. They can also cause poor convergence, get trapped at a local optimum, and only yield a single optimal solution for a single simulation run, become very slow when dealing with a huge number of variables, and cost a lot of money to solve a large-scale system. The existing pricing methodologies are inadequate, inefficient and they fail to produce accurate price. Existing pricing approaches are insufficient, inefficient, and fail to create reliable price signals, which are required in an open access system. First, the RP support’s inherent cost is evaluated. The cost of producing reactive electricity for each source and the cost of transmitting RP for each transmission line are then calculated. Finally, the research focuses on how to properly and quickly allocate RP production and transmission costs across many loads in large power networks while staying below maximum loadability limitations. On the basis of power flow tracing, an ISFH algorithm is employed to optimize the cost allocation approach for RP support. The proposed optimization strategy was proven to be reasonable and concrete in a sample case study on practical 75-bus Indian system wind farms. The transmission grid’s network structure allows a number of different paths for power to move from a source to a load. Load tracing is a method for tracing the power consumed or derived by specific loads that was created with physical power system limits in mind. On the basis of power flow

Optimal Reactive Power Pricing   257 tracing, an ISFH algorithm is employed to optimize the cost allocation approach for RP support. The proposed optimization strategy was proven to be reasonable and concrete in a sample case study on practical 75-bus Indian system wind farms.

13.2 RP Flow Tracing The transmission grid’s network structure allows a number of different paths for power to move from a source to a load. The contributions of generators and loads can be estimated via generation and load tracing. This technique simplifies the tracing of power flows as well as the loss associated with each transmission service transaction. The power flow tracing algorithm is a mechanism for tracking each user’s contribution to a transmission system in order to assign charges for transmission line usage. Node technique, Graph method, Common method, Bialek’s tracing, and Kirschen’s tracing are some of the tracing methods. Because RP and real power can have different directions, it’s critical to employ the RP surge to calculate the split of RP spent on load buses while tracing. RP extracted by a load is traced using ISFHA which finds the nearby generating source for the respective load; thereby it reduces the RP cost of the load.

13.2.1 Intent Function The goal of this presentation is to minimize the entire price of RP and it is further constituted with three price parts, loss, installation, and production [21].

13.2.1.1 System’s Price Loss After RP Compensation The load on the distribution system is assumed to be constant throughout time, but the outputs of wind power generators vary with variations in wind speed. System’s Price loss Sp is the inclusion of the price loss of every one of the wind power output states, as shown below.



Sp

K

T t 1

Tt APt (x)

(13.1)

where APt(x) is the active power loss and it is applicable for the power output position t from Wind generated power; Tt is time span of position t;

258  Smart Grids for Smart Cities Volume 1 K is cost of energy. Minimizing the power system’s active power loss is the same as minimizing the slack generator’s active power.

13.2.1.2 SVC Support Price for RP In most cases, the price of SVC is marked by an approximated linear function and it is referred with a price of fixed installation and a variable price of operation. SVC ‘F’ costs are calculated as follows: I i 1 i

e (riQ0ci ci )

F



(13.2)

where ri and ci denotes the marginal price and fixed price of installation at node i. The value of binary variable ei depends availability of SVC. If SVC is installed then ei = 1 and if it is not installed ei = 0 at node i. Q0ci denotes the SVC capacity required at node i.

13.2.1.3 Diesel Generator RP Production Price To manage the voltages of distribution networks, distributed generators can directly serve local loads without the need for long-distance transmission. To describe the operation and opportunity prices for producing RP, a quadratic price function is used. The production price R can be changed as follows:

R



T t 1

I i 1

(ai biQtDGi ciQtDGi 2 )

(13.3)

Where I denotes the total number of nodes. Coefficients of diesel unit production price can be determined function of the at node i are ai, bi and t is the diesel unit RP at node i. ci. QDGi

13.2.1.4 Minimization Function The intent function of the RP planning model for a distribution system with distributed generations is to minimize the sum of the three cost functions indicated in (13.4)

min f K

T t 1

Tt Pt (x)

I i 1 i

e (riQ0ci C i )

T t 1

I i 1

(ai biQtDGi ciQtDGi 2 )



(13.4)

Optimal Reactive Power Pricing   259

13.3 Existing Methodologies 13.3.1 Particle Swarm Optimization (PSO) The PSO algorithm is based on the idea that birds flock together to obtain food rather than searching for it individually. PSO starts with a swarm of random solutions and updates generations to look for optima. A particle is a single member of the population. It has crossover and mutation evolution operators and only a few settings that need to be adjusted. The PSO algorithm is simple, needs little calculation time, and uses little memory. The fitness function analyses the fitness values of all the particles to be optimized and has velocities that straighten the particles as they fly from the start to the finish of the issue space, succeeding the present optimum particles. The particles which update its position and velocity as given below as soon as pbest(t) and gbest(t) are found.

Vid(t + 1) = wVid(t) + C1rand(pbest,d(t) – pid(t)) + C2rand (gbest,d(t) – xid(t))

(13.5)

pid(t + 1) = pid(t) + Vid(t + 1)

(13.6)

where r is a random value and it is in the range [0,1]. w is inertia weight. Impact of previous velocities on the current velocity during optimization process is controlled by w. Since w drops linearly from around 0.95 for global exploration to 0.3 for local exploration rather frequently over a run, it can be an excellent option of w. The following weighing function (wit) in equation (13.7) is used in equation (13.5) to update velocity.



wit

wmax

wmax wmin iter itermax

(13.7)

Where wmax- weight at initial position wmin- weight at final position itermax- iteration number maximum iter - iteration number at present

13.3.1.1 PSO Parameter Settings For the application of PSO to optimization problems, two important steps must be considered. First is solution representation, and second is fitness

260  Smart Grids for Smart Cities Volume 1 function. PSO’s key benefit is that it uses real numbers as particles and then uses the usual method to find the best solution. The searching is an iterative procedure that ends when the maximum number of iterations has been reached or the least error condition has been met. PSO does not necessitate the fine tweaking of numerous parameters. The most important parameters to modify are (i) The number of particles (iv) Vmax

(ii) Length of (iii) Range of particles particles (v) Learning (vi) Termination criteria factors (vii) Global version vs. (viii) Inertia local version weight (w)

13.3.2 Hybrid Particle Swarm Optimization (HPSO) When the velocities in PSO reach zero, all particles will stop moving at the point where the current position corresponds with the global optimal location, causing the algorithm to stagnate prematurely. To address this problem, HPSO was developed, which included breeding and subpopulation search. This search was done in different zones in the search space rather than local optima. HPSO explores the viable solution hyperspace in pursuit of an optimal solution using an initial population of particles or probable solutions. The position of each particle is used as a possible initial guess in the power flow algorithm. Each particle in HPSO contains a memory element (pbest) that remembers the best visited location based on its own flight experience in order to find the best solution. When this knowledge crosses the problem’s borders, it is employed. Using the memory element, each particle must preserve its feasible status during the hybridization process. Instead of a thorough elimination, this restoration procedure maintains the infeasible particle active as a possible option for tracing the optimal solution.

13.3.2.1 Flowchart for HPSO The flowchart for HPSO-GA is depicted in Figure 13.1.

Optimal Reactive Power Pricing   261 Define cost function, variables and select HPSO-GA parameters

Start

Initialize population

Update velocity and position

Selection Crossover

Fitness function Evaluation

Mutation No PSO sub iteration over?

No GA sub-iteration over?

Yes Yes Store the best solution

HPSO-GA iteration over?

No

Yes End

Figure 13.1  Flowchart for HPSO.

13.4 Proposed Methodology 13.4.1 Improved Sheep Flock Heredity Algorithm Sheep Flock Heredity Algorithm (SFHA) was used to solve scheduling issues. The algorithm was based on how sheep in a flock naturally evolved. Some sheep with high fitness qualities to their environment reproduce in the flock, and some unique characteristics in one flock develop solely

262  Smart Grids for Smart Cities Volume 1 within the flock through inheritance. In the SFHA, the pairwise mutation process is replaced with a single mutation process with a probabilistic property that ensures the feasibility of solutions in the local search region. An improved SFHA (ISFHA) is presented to improve the performance of genetic searches and increase convergence. The precision of cost assessment and allocation is good at the ISFHA since it uses optimization methodology. The possessions of ISFHA characteristics such as population, crossover, and chromosomal production, as well as the fitness function, are also

Begin

Population Initialization Sub-chromosomal crossover No New make span < Old make span

Use old sequence and corresponding make span

Yes Use new sequence and corresponding make span

Sub-chromosomal crossover

No New make span < Old make span

Use old sequence and corresponding make span

Yes R-R heuristic

Sub chromosome mutation

New sequence and corresponding make span

Chromosome mutation

No New make span < Old make span

Use old sequence and corresponding make span

Yes New sequence and corresponding make span

Sequence for the next iteration

End

Figure 13.2  Flowchart of ISFHA.

Yes Termination condition satisfied

Optimal Reactive Power Pricing   263 taken into account to get the best possible result. A novel robust-replace (R-R) heuristic is developed to improve the neighbour search templates, allowing for greater exploring capability and a bigger exploring zone. This heuristic aids the algorithm’s solution search in the global domain. As a result, the suggested method ISFHA is simple to build and may be used to solve any combinatorial or functional optimization problems.

13.4.2 ISFHA Algorithm It consists of following step-by-step procedure as shown in Figure 13.2.

13.5 Case Study 13.5.1 Realistic Seventy-Five Bus Indian System Wind Farm Figure 13.3 shows a one-line diagram of the RSFIB-wind farm. The proposed static and dynamic value-based cost allocation approach for RP support. It is examined and investigated on a RSFIB. This realistic system includes 15 generators, 97 transmission lines, and 60 load buses for a 400, 220, and 132 KV network of one of India’s electricity boards. This system

G 6

G 5 31

32

G 70 60

39 62

72

3

33 25

65 21

11

64

8 34 54

55 44

G

73

1 17

Zone 1 G

23

10 24

G

36

46

35 9 41

67

63 45

69

19 37

G

G

G

20

G

4 28

15

40

Zone 2 66

43

56

49

14

53 29 30

71 27

22

57

75

48

51 68

5

59

61

52

18

Zone 4

36

G

47

Zone 3

16

G

13

G FOR HIGH CONSUMPTION

2

G

12

FOR LOW CONSUMPTION

Figure 13.3  One-line diagram of the RSFIB wind farm.

G

50

264  Smart Grids for Smart Cities Volume 1 uses a Three phase induction generator (squirrel cage) having power of 843 KVA nominal, 690 V (ϕ-ϕ), 50 Hz. The best place to put the SVC in each zone is at the high- and low-traffic bus stops. SVC is located at bus-47 and bus-63 for the chosen zone 3. As a result, only these buses are charged for SVC. High and low consumption buses are recognized in the same way for all other zones, and SVC is optimally placed on such buses. The position of an SVC has been found to have a significant impact on swing mode controllability. In general, the optimum location is one with the highest voltage swings. Typically, the transmission line’s midpoint between the high and low consumption areas is a viable candidate for placement. Table 13.1 depicts the Constraint values adopted in various algorithms like PSO, and HPSO. In addition, parameters like C1, C2, maximum and minimum inertia weight (w), upper limit of velocity (Vmax), and lower limit of velocity (Vmin) are also selected. Crossover should have a better convergence properties and MP should be very low to obtain the best optimum solution. The convergence characteristics for RSFIBS-wind farm of the ­population-based algorithms such as PSO, HPSO and ISFHA are shown in Figure 13.4. Compared to these algorithms, ISFHA takes a lesser computational time per iteration and hence it has faster convergence. Figure 13.5 represents the cost analysis charts for Minimum RP cost, Maximum RP cost, Average RP cost and Standard deviation of RP cost for various evolutionary multi-objective optimization algorithms like PSO, HPSO and ISFHA. It may also be deduced that the suggested method, ISFHA, has provided near ideal RP costs for various cost analysis criteria. Table 13.1  Constraint values for PSO and HPSO. Constraints

PSO

HPSO

No. of variables

50

50

Population Size

50

50

No. of iterations

100

100

C1, C2

2

2

Inertia weight

0.3 - 0.95

0.3 - 0.95

CP

-

0.5

MP

-

0.1

CP - Crossover probability; MP - Mutation probability.

Optimal Reactive Power Pricing   265 Convergence graph for practical 75-bus Indian System

4

ISFHA HPSO PSO

3.5

Computational Time

3 2.5 2 1.5 1 0.5 0

0

5

10

15

20

25 30 Iterations

35

40

45

50

Figure 13.4  RSFIBS - wind farm - convergence graph.

200

Final Q cost solutions after 50 iterations

180 160

Cost in Rs/hr

140 120 ISFHA HPSO PSO

100 80 60 40 20 0 Min Qcost

Figure 13.5  Cost analysis.

Max. Qcost

Avg. Qcost

S.D.

266  Smart Grids for Smart Cities Volume 1 Table 13.2  Cost-based comparison of PSO, HPSO, value-based approach and ISFHA.

Cost

PSO

HPSO

Value-based approach [17]

Total RP cost (Rs./hr)

1,25,240.03

1,48,346.45

96,133.39

1,02,678.02

Computation time (min)

60.12

52.78

30.00

40.72

ISFHA

Table 13.2 compares the results of the ISFHA with the results of the PSO, HPSO, and traditional value-based approach. The separation of various cost components for RP payments obtained from ISFHA is examined and proven to be optimal when compared to other algorithms such as PSO and HPSO, and will offer more intelligibility to consumers in the electricity market. PSO and HPSO terminate when the encoded utmost number of iterations is reached and do not converge to the global optimal solution, resulting in higher cost values.

13.6 Conclusion Because population-based algorithms have not converged to a global optimum solution, the cost of RP estimated by PSO and HPSO is 30.28% and 54.31% higher, respectively, than the traditional value-based approach [17]. However, when compared to the traditional value-based approach [17], ISFHA produces just a 6.8% greater cost, which is attributed to the system’s inclusion of a RP loss estimate. Due to poor convergence, PSO and HPSO computation times are 50.1% and 43.16% longer than the traditional value-based technique [17]. However, due to the loss calculation, ISFHA only takes 26.33% of the computing time of the traditional ­value-based technique [17]. As a result, the suggested ISFHA is justified in being more accurate than the traditional value-based approach because it includes the loss computation. Both sensitivity and tracing methods appear to be nearly better for the same electrical network setup. However, in the restructured market, those strategies are not as competitive for the distribution of RP costs in the auxiliary service environment:

Optimal Reactive Power Pricing   267 • The tracing method is comparatively better for achieving better accuracy because it considers the electrical network’s line losses. • The sensitivity analysis method is comparatively better for achieving increased speed of response or execution because it does not require reconfiguring the electrical network. Despite delivering a near-optimal and efficient solution, the proposed algorithm ISFHA also serves as a potential tool to assist power system operators in real-time situations. Because of its effectiveness in computation with a higher convergence rate, it may also be used to handle non-­ linear, non-differentiable, and high dimensionality optimization problems.

References 1. Rui Chi, Zheng Li, Xuexin Chi, Zhijian Qu, and Hong-bin Tu, “Reactive Power Optimization of Power System Based on Improved Differential Evolution Algorithm”, Mathematical Problems in Engineering, Hindawi, 2021, pp. 1–19. https://doi.org/10.1155/2021/6690924 2. Nitin Kumar Saxena, Ashwani Kumar, Gebrehiwot Gebreyohans, “Reactive Power Cost Optimization Acquiring the Combined Properties of Static and Aggregate Dynamic Load as Composite Load Model”, International Journal of Engineering and Advanced Technology, ISSN: 2249–8958, Volume 9, Issue 3, February 2020, pp. 3185–3192. 3. Danalakshmi D., Gopi R., Hariharasudan A., Iwona Otola and Yuriy Bilan, “Reactive Power Optimization and Price Management in Microgrid Enabled with Blockchain”, Energies, 2020, Issue 13, pp. 1–20. 4. Chenchen Yong, Yong Li, Zilong Zeng, Zhiwen Zhang, Zhenyu Zhang and Yanlun Liu, “Coordinated Active and Reactive Power Optimization Considering Load Characteristics for Active Distribution Network”, Chinese Journal of Electrical Engineering, 2020, Vol. 6, No. 4, pp. 97–105. 5. Booma Devi P., Booma J and Deivakani M, “A survey on Reactive Power Cost Management in Wind Farms”, Purakala (UGC Care Journal), ISSN No. 0971-2143, 2020, Vol. 31, Issue 40, pp. 10–21. 6. V. Krishnan, T. Das, E. Ibanez, C.A. Lopez, “Modeling Operational Effects of Wind Generation Within National Long-Term Infrastructure Planning Software”, IEEE Trans. on Power Systems, Vol. 28, 2013, pp. 1308–1317. 7. Faisal B. Alhasawi and Jovica V. Milanović, “Techno-Economic Contribution of FACTS Devices to the Operation of Power Systems with High Level of Wind Power Integration”, IEEE Transactions on Power Systems, Vol. 27, No. 3, 2012, pp. 1414–1421.

268  Smart Grids for Smart Cities Volume 1 8. Z. Hamid, I. Musirin, M.N.A. Rahim, N.A.M. Kamari, “Optimization Assisted Load Tracing via Hybrid Ant Colony Algorithm for Deregulated Power System”, WSEAS Transactions on Power Systems, Volume 7, Issue 3, 2012, pp. 145–158. 9. Mala De, Swapan K. Goswami, “RP cost allocation by power tracing based method”, Elsevier, Energy Conversion and Management, 2012, pp. 43–51. 10. Carlos Henggeler Antunes, Paulo Lima, Eunice Oliveira and Dulce F. Pires, “A multi-objective simulated annealing approach to RP compensation”, Taylor & Francis, Engineering Optimization, Vol. 43, No.10, 2011, pp. 1063–1077. 11. K.C. Almeida, and F.S. Senna, “Optimal Active-RP Dispatch Under Competition via Bilevel Programming”, IEEE Transactions on Power Systems, Vol. 26, No. 4, 2011, pp. 2345–2354. 12. Seyed Mohammad Hossein Nabavi, Somayeh Hajforoosh, Sajad Hajforosh, Nazanin Alsadat Hosseinipoor, “Using Tracing Method for Calculation and Allocation of RP Cost”, International Journal of Computer Applications (0975 – 8887), Volume 13, No. 2, 2011, pp. 14–17. 13. Behnam Tamimi, Claudio A. Cañizares, and Sadegh Vaez-Zadeh, “Effect of RP Limit Modeling on Maximum System Loading and Active and RP Markets”, IEEE Transactions on Power Systems, Vol. 25, No. 2, 2010, pp. 1106–1116. 14. P.R. Sujin, Dr. T. Ruban Deva Prakash and M. Mary Linda, “Particle Swarm Optimization Based RP Optimization”, Journal of Computing, Volume 2, Issue 1, 2010, pp. 73–78. 15. Puneet Chitkara, Jin Zhong, and Kankar Bhattacharya, “Oligopolistic Competition of Gencos in RP Ancillary Service Provisions”, IEEE Transactions on Power Systems, Vol. 24, No. 3, 2009, pp. 1256–1265. 16. S. Hasanpour, R. Ghazi and M.H. Javidi, “A new approach for cost allocation and RP pricing in a deregulated environment”, Electrical Engg., Springer, 2009, 91:27–34. 17. S.K. Parida, S.N. Singh and S.C. Srivastava, “RP Cost allocation by using a value-based approach”, IET Gen., Trans. & Dist., Vol. 3, Issue 9, 2009, pp. 872–884. 18. Hadi Banakar, Changling Luo, and Boon Teck Ooi, “Impacts of Wind Power Minute- to-Minute Variations on Power System Operation”, IEEE Transactions on Power Systems, Vol. 23, No. 1, 2008, pp.150–160. 19. J.G. Singh, S.N. Singh, and S.C. Srivastava, “An Approach for Optimal Placement of Static VAr Compensators Based on RP Spot Price”, IEEE Transactions on Power Systems, Vol. 22, No. 4, 2007, pp. 2021–2029. 20. Wenjuan Zhang, Fangxing Li, and Leon M. Tolbert, “Review of RP planning: Objectives, Constraints and Algorithms”, IEEE Trans. on Power Sys., Vol. 22, No. 4, 2007, pp. 2177– 2186. 21. Lin Chen, Jin Zhong & Deqiang Gan 2006, “RP planning and its cost allocation for distribution systems with distributed generation”, IEEE Power Engineering Society General Meeting, PES, pp. 1-6.

14 Dual Axis Solar Tracking with Weather Monitoring System by Using IR and LDR Sensors with Arduino UNO Rajesh Babu Damala* and Rajesh Kumar Patnaik Department of EEE at GMR Institute of Technology, Rajma, Srikakulam, Andhra Pradesh, India

Abstract

Of renewable energies, solar energy is the fast-growing source to fill the gap between demand and supply. The efficiency of solar electrical energy is not up to the mark as of now due to stationary solar arrays. To solve this problem, a solar panels tracking system is the best way. In addition, tracing a solar path in dual-mode can enhance the efficiency even better. Tracing the sun like a sunflower to convert maximum radiant to electrical energy can be possible with help of LDR and IR sensors, and Arduino. Further, if the atmosphere monitoring mechanism is associated with the proposed method it can make the system robust and reliable. The efficiency of the proposed method is compared with stationary solar panels and its importance is proved. Keywords:  Solar tracking, Twin Axis, Arduino UNO, weather monitoring, maximum power

14.1 Introduction Distributed generation is one of the alternative sources as most of the traditional power plants are running out of fossil fuels [1]. It generates 19% of all power in the world. Electricity consumption continues to rise year *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (269–284) © 2023 Scrivener Publishing LLC

269

270  Smart Grids for Smart Cities Volume 1 after year, but the primary supplies of oil, gas, and coal are decreasing [2]. The government has identified radiant energy, which obviously is one sort of non-conventional energy source, as the most effective solution to this problem. Radiant energy is a limitless energy source that will continue to exist in the future [3]. In the long run, it will become increasingly crucial for delivering all available energy sources to all living creatures. A twin-­ direction solar tracing mechanism is introduced in this method in order to take advantage of solar energy’s supremacy [4]. Making solar arrays to follow the path of sun rays to produce as best as electrical energy is the key challenge in the solar tracking field. Moreover, making panels move in both directions such as circular and linear motion to grab maximum radiant energy and to overcome seasonal effects is another challenge [5]. Furthermore, in the proposed method, the weather monitoring system can help the solar tracing mechanism to increase reliability and robustness as it can predict cloudy and sunny days. Due to all these numerous advantages, the efficiency of solar panels can be boosted by 30 to 60% approximately; a variable elevation solar tracker can provide up to 40% more electricity each year. A single-direction tracker can have either a linear or circular, whereas a twin direction tracker is able to trace the sun’s instantaneous path particularly [6]. The aim of this study is to investigate twin-direction solar tracing systems, comparing them with stationary and single-direction tracing systems in terms of converting solar energy efficiency [7]. The input, controller, and output were all separated into three sections [8]. Sensing of light energy portion job done by LDRs as input, the controlling of various mechanism is done by Arduino and the final required output as per need done the servo motor.

14.2 Associated Hardware Components Details 14.2.1 Arduino Uno Figure14.1 shows the Arduino Uno having an 8-bit ATmega328P microcontroller board which is a microcontroller-based one. The Arduino Uno also has a few more additional elements to support the ATmega328P, which are serial communication, voltage regulator and a crystal oscillator, and so on [9]. The 14 digital input and output pins consist of the Arduino Uno, and USB connection six analog input pins a reset button, a Power barrel

Solar Tracking System  271

Figure 14.1  Arduino UNO used in this method.

connector, and an ICSP header. A range of 20-50 KΏ internal pull-up resistor is being included by each pin which is not connected by defaulting and runs with 5Volts and can give or take a maximum current of 40mA.

14.2.2 L293D Motor Driver Figure 14.2 shows the L293D Motor Driver works well with DC and stepper motors. It makes use of the well-known L293 motor driver IC. It can turn on and off four DC motors or control the speed and direction of two DC motors. It makes it considerably simpler and improves the microcontrollers that may controlmotors, relays, and other devices. The DC current having it by is up to 600mA and drives motors to a maximum of 12V. This driver is ideal for controlling motors from microcontrollers, switches, relays, and other devices in robotics and mechatronics projects. It is perfect for line-­following robots micro-mouse, Stepper motor applications, and robot arms. Figure 14.3 is the IR sensor which has certain features of the environment can be sensed by the infrared light generated from the infrared sensors which are electronic devices. The motion and measurement of the heat of any element can be detected by the infrared sensor. Rather than emitting radiation measures, it can be done by a passive IR sensor.

272  Smart Grids for Smart Cities Volume 1

Figure 14.2  Motor driver C. IR sensor.

Figure 14.3  IR sensor.

14.2.3 LDR Sensor Figure 14.4 is the LDR sensor which works on the concept of the photoconductivity phenomenon. The LDR is a passive element whoseresistance value is proportional to light intensity or illumination. This optoelectronic element is generally found in light-changing sensor networks and dark and light-activated switching networks. The applications of LDR are as follows: 1) outdoor clocks, 2) camera light meters, 3) clock radios, 4) streetlights, 5) light beam alarms, 6) smoke alarms, and so on.

Solar Tracking System  273

Figure 14.4  LDR sensor.

14.2.4 Solar Panel Figure 14.5 is the solar panel used in this method. Solar plates are classified as monocrystalline and polycrystalline as per their purity of silicon wafers.

Figure 14.5  Solar panel.

274  Smart Grids for Smart Cities Volume 1 The monocrystalline type efficiency is higher than the polycrystalline purity of monocrystalline at about 99%, whereas polycrystalline is about 70-80%. Though the polycrystalline panels have less efficiency, most industries use polycrystalline only due to economic reasons. In the proposed method, class ‘A’ type polycrystalline is used.

14.2.5 RPM 10 Motor Figure 14.6 is motor used in this work. The geared motors are being used for low RPM applications. The major advantage of this type of motor is that it can rotate in either direction, i.e, clockwise and counterclockwise which is apt for solar panel tracking. The steeper motor also can be used in this kind of application but tracking accuracy is a bit low.

Figure 14.6  DC gear motor.

14.2.6 Jumper Wires Figure 14.7 shows the jumper wires having male and female ports for easier connections of the circuit. If any wires other than these jumper wires are used with joints that may lead to a short circuit of the whole system sometimes. The designed hardware model with these jumper wires looks professional and neat aftercompletion of the model.

Solar Tracking System  275

Figure 14.7  Jumper wires.

14.2.7 16×2 LCD (Liquid Crystal Display) Module with I2C The 16 2 LCD got named as it contains 2 rows with 16 columns shown in Figure 14.8. The other possible combination is 81, 82, 102, and 61, and so on. However, the most commonly used one is 16 2 type LCD which is used in the proposed model. The information exchange between slaves and master takes place by I2C inter-integrated protocol. The other advantage of using I2C is that the number of required connections in the circuit can be minimized. The number of connections to be done is 12, if you use standard LCD type, whereas if I2C is used, only four connections are enough.

VSS (Ground) VDD (5V) VE (Contrast) RS (Register Select) RW (Read/Write) E (Enable) D0 D1 D2 D3 D4 D5 D6 D7 Backlight Cathode Backlight Anode

Figure 14.8  Pin configuration of 16X2 LCD with I2C.

276  Smart Grids for Smart Cities Volume 1

14.2.8 DTH11 Sensor To detect temperature and humidity in the atmosphere, the basic very low cost is the DHT11 sensor which is shown in Figure 14.9. The DTH11 sensor gives digital signals of respective temperature and humidity content in weather. Basically, it works on the capacitive transducer principle. The usage of the DTH11 sensor is very simple but must be careful by the time grabbing the required data.

Figure 14.9  DTH11 sensor.

14.2.9 Rain Drop Sensor Figure 14.10 shows a raindrop sensor is essentially a board with nickel lines coated on it. It is based on the concept of resistance. The rain sensor module enables you to measure moisture via analog output pins and it provides a digital output when the threshold of moisture is exceeded.

Figure 14.10  Rain drop sensor.

Solar Tracking System  277

14.3 Methodology This technique mail goal is to investigate the efficiency of a twin-axis solar tracing system. By taking radiant illumination as a reference, the solar panel is following the solar path [10]. The LDR and IR sensor gives input signal to the Arduino UNO which analyses the input signal and passes commands to the motor driver to make the dc motor rotate [11]. As the solar array is placed on the shaft of the DC motor, the solar plate starts moving along with the DC motor shaft in the required direction to grab the maximum radiant energy.

14.3.1 Dual Axis Solar Tracking System Working Model Figure 14.11 is the dual axis solar tracker after assembling all the parts. The LDRs and IR sensors start working when the sunrise takes place. If

Figure 14.11  The designed dual axis solar tracking system working model.

278  Smart Grids for Smart Cities Volume 1 the sensors with the tracing system are subjected to sunlight, LDR and IR sensors start to act according to the intensity of the light energy. Moreover, two LDRs or IR sensors are held on the hollow tube with the separation of a wall [12]. The specific reason behind this idea is that if both sensors in the tube can get the same intensity of light, then only the same amount of voltage signal would be generated. As the program wrote that make sure that voltage appeared in both sensors must be equal [13, 14]. If not equal, make the DC motor rotate till it ensures the same voltage of the sensors in a hollow tube. The voltages can be equal only if the solar plate is exactly perpendicular to the sun’s position. The microcontroller is an intelligent device that takes actions based on sensor input and activates the motor driver circuit as needed. This is how tracing of the sun path is Start

Assigning LDRs

Read Analog Signal from LDRs

Convert Analog Signal in to digital Signal

Calculate the average output of the LDRs- 1&2; 0&3; 2&3; 0&1

If (avgright

NOT

OR

1

OR

2

OR

3

OR

4

OR

5

OR

6

≥

0.6

7

≤

–0.6

Figure 19.6  Simulation diagram of SBC PWM technique. SBC PWM technique 1 0.5 0 –0.5 –1 1.146

1.15

1.152

1.154

1.156

1.158

Time (s)

Figure 19.7  SBC PWM technique.

that the switches can be turned when the boost network switch is on just to have boost operation.

19.4.3 Switching Pulse Generated for the Power Switches In the proposed SL-SC quasi switched boost topology seven MOSFET switches are used and the pulses given to the devices are shown in Figure 19.8.

s3

1 0.5 0

s6

s5

1 0.5 0

s7

Switching Pulses

1 0.5 0 1 0.5 0

s4

s2

s1

348  Smart Grids for Smart Cities Volume 1

1 0.5 0 1 0.5 0 1 0.5 0

1.1745

1.175

1.1755

1.176

Time (s)

1.1765

1.172

1.1775

Figure 19.8  Switching pulse for MOSFET devices.

19.4.4 Expanded Switching Pulse The waveform shown in Figure 19.9 is expanded switching pulse for showing shoot-through state. In this we took gate pulses of first leg of inverter; the upper one is for upper switch and lower one is for lower switch. We can see there is small time period for which both switches are on and that condition is called shoot-through state.

19.4.5 Input Current The waveform of input current which is around 8.1A is depicted in Figure 19.10 from the source voltage of 64V.

0.8 0.6 0.4 0.2

Switching pulse of s2

Switching pulse of s1

Expanded Switching Pulses 1

0 1 0.8 0.6 0.4 0.2 0 1.325

1.326

1.327

1.328

Time (s)

Figure 19.9  Expanded switching pulse.

1.329

1.33

1.331

Three Phase Extended Quasi SBI Structure  349 45 40

input current (A)

35 30 25 20 15 10 5 0 –5

0.075

0.08

0.085

0.09 Time (s)

0.095

0.1

0.105

Figure 19.10  Input current waveform.

19.4.6 Current in Inductor L1 The waveform of inductor current through inductor L1 is obtained around 4A and inductor value is considered as 1mH which is shown in Figure 19.11.

19.4.7 Current in Inductor L2 The inductor current L2 is obtained around 4A and inductor value is considered as 1mH, which is shown in Figure 19.12.

Inductor current IL1(A)

3.8 3.75 3.7 3.65 3.6 3.55 0.06899

0.069

0.06901

0.06902

Time (s)

Figure 19.11  Inductor current IL1.

0.06903

0.06904

0.06905

0.06906

350  Smart Grids for Smart Cities Volume 1

Inductor current IL2(A)

3.8 3.75 3.7 3.65 3.6 3.55 3.5 0.06896

0.06897

0.06898

0.06899

0.069

0.06901

0.06902

0.06903

Time (s)

Figure 19.12  Current through inductor L2.

19.4.8 Capacitor Voltage VC2 The waveform of voltage across capacitor is around 265V, which is shown in Figure 19.13.

19.4.9 DC Link Voltage The boosted dc link voltage of boost network is around 510V. This boosted voltage is given to the inverter for inversion process, which is shown in Figure 19.14. DC link voltage appears across the inverter bridge is zero during shoot-through and inverted boosted voltage appears across the load.

900

Capacity Voltage VC2

800 700 600 500 400 300 200 100 0 –100

0.095

0.1

0.105

0.11 Time (s)

Figure 19.13  Voltage waveform across capacitor.

0.115

0.12

0.125

Three Phase Extended Quasi SBI Structure  351

DC link voltage (V)

700 600 500 400 300 200 100 0 0.08176 0.08177 0.08178 0.08179 0.0818 0.08181 0.08182 0.08183 0.08184 0.08185

Time (s)

Figure 19.14  Waveform of dc link voltage.

19.4.10 Output Load Voltage The output voltage across load is achieved around 160V ac peak phase voltage as shown in Figure 19.15. It is considered as 113Vrms phase voltage.

19.4.11 Output Load Current The waveform of output current through load is obtained around 1.8A as shown in Figure 19.16.

19.5 Performance Analysis The performance of proposed inverter circuit is compared with the conventional circuits. The minimum duty ratio 0.23 at the input voltage is able to provide the boost factor of 8.125. Va

Vb

Vc

Time (s)

Figure 19.15  Output voltage across the load.

352  Smart Grids for Smart Cities Volume 1 Ia

Ib

Ic

Time (s)

Figure 19.16  Waveform of output current through load.

The plot between the shoot-through duty ratio (D) versus boost factor (B) is drawn. The graph depicted in Figure 19.17 proves that higher voltage boost is attained with the proposed topology. By graph, it is seen that in the proposed topology higher voltage boost is obtained and improves the output voltage quality. In the proposed structure, voltage gain is directly 30 ZSI,qSBI, CFSI SBI SLBI,SLZI SC-qSBI Proposed inverter

25

Boost factor (B)

20

15

10

5

0 0.05

0.1

0.15 0.2 Shoot through duty ratio (D)

Figure 19.17  Graph between (D) and (B).

0.25

0.3

Three Phase Extended Quasi SBI Structure  353 25 251,Q581CFS SL251,SLBI S81 SC-088 PROPOSED-ONE

Voltage Gain (G)

20

15

10

5

0

0.7

0.75

0.8

0.85 0.9 Modulation index (M)

0.95

1

Figure 19.18  Graph between (M) and (G).

proportional to modulation index and boost factor. With same modulation index the proposed topology has higher boost than other conventional topologies and the circuit provides boost factor of 8.125 with the duty ratio of 0.23 and the dc source of 64V. From Figure 19.18, it is clear that the proposed circuit is able to give the output of 157 V peak at the voltage gain of 2.45 with reduced modulation index and input voltage of 64V and 0.6 modulation index. Proposed topology can provide higher voltage gain compared to other conventional topology with reduced value of modulation index.

19.6 Conclusion A three-phase SL-SC based quasi switched boost inverter is proposed in this article. It is used to provide the inversion and also to get the maximum boost in single stage. The primary purpose is to get the maximum boost possible. The forbidden state of shoot-through is used to boost the dc voltage. The prime feature of this work is to get the maximum boost possible

354  Smart Grids for Smart Cities Volume 1 using shoot-through state with SL-SC combination. This proposed circuit offers a high voltage gain along with good efficiency. This inverter provides an efficiency of 94.22%. Hence this topology can be well suited for RESbased applications.

References 1. F. Blaabjerg, Z. Chen, and S. B. Kjaer, “Power electronics as efficient interface in dispersed power generation systems,” IEEE Trans. Power Electron, Vol. 19, No. 5, pp. 1184-1194, Sep. 2004. 2. Peng, F.Z. Z-source inverter. IEEE Trans. Ind. Appl. 2003, 39, 504-510. 3. Anderson, J.; Peng, F. Four quasi-Z-Source inverters. In Proceedings of the 2008 IEEE Power Electronics Specialists Conference, Rhodes, Greece, 15–19 June 2008; pp. 2743-2749. 4. M. K. Nguyen, Y. C. Lim and J. H. Choi, “Two switched-inductor quasi-Zsource inverters,” IET Power Electron., 5(7), 1019-1025 (2012). 5. M. K. Nguyen, Y. C. Lim, and Y. G. Kim, “TZ-source inverters,” IEEE Trans. Ind. Electron., 60(12),5686-5695 (2013). 6. W. Qian, F. Z. Peng, and H.Cha, “Trans-Z-source inverters,” IEEE Trans. Power Electron., 26(12), 3453-3463 (2011). 7. P. C. Loh, D. Li, and F. Blaabjerg, “Γ-Z-source inverters,” IEEE Trans. Power Electron., 28(11), 4880-4884, 2013. 8. W. Mo, P. C. Loh, and F. Blaabjerg, “Asymmetrical Γ-source inverters,” IEEE Trans. Ind. Electron., 61(2), 637-647 (2014). 9. M. K. Nguyen, Y. C. Lim, and S. J. Park, “Improved trans-Z-source inverter with continuous input current and boost inversion capability,” IEEE Trans. Power Electron., 28(10), 4500-4510 (2013). 10. Upadhyay. S., Ravindranath. A., Mishra. S., Joshi. A, 2010, A Switched-Boost Topology for Renewable Power Application, IEEE, IPEC, 758-762. 11. Nguyen, M.-K.; Le, T.-V.; Park, S.-J.; Lim, Y.C. A Class of Quasi-Switched Boost Inverters. IEEE Trans. Ind. Electron. 2015, 62, 1526-1536. 12. Chub, A.; Liivik, L.; Zakis, J.; Vinnikov, D. Improved switched-inductor quasi-switched-boost inverter with low input current ripple. In Proceedings of the 2015 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia, 14 October 2015; pp. 1-6. 13. Ravindranath. A., Avinash. J., Santanu. M. Pulse Width Modulation of ThreePhase Switched Boost Inverter. In: IEEE Conference, 2013. 14. Nguyen, M.K., Le, T.V., Park, S.J., Lim, Y.C. A class of quasi switched boost inverters. In: IEEE Trans. Ind. Electron, 62(3), 1526-1536 (2015). 15. Soumya Shubhra Nag, Santanu Mishra, “Current-Fed Switched Inverter” IEEE Transactions on Industrial Electronics, 61(9) 2014.

Three Phase Extended Quasi SBI Structure  355 16. Elias Shokati Asl, Ebrahim Babaei, Mehran Sabahi, “High voltage gain halfbridge quasi-switched boost inverter with reduced voltage stress on capacitors” IET Power Electron., Vol. 10(9), 1095-1108 (2019). 17. Minh-Khai Nguyen, Tan-Tai Tran, “A Single-Phase Single-Stage SwitchedBoost Inverter With Four Switches, IEEE Transactions on Power Electronics, 33(8), 6769-6781 (2018). 18. E., Shokati Asl, E., Hasan Babayi, M. ‘Steady-state and small-signal analysis of high voltage gain half-bridge switched-boost inverter’, IEEE Trans. Ind. Electron., 63(6), 3546-3553 (2016). 19. Duc-Tri Do, Minh-Khai Nguyen, “Three-Level Quasi-Switched Boost T-Type Inverter: Analysis, PWM Control, and Verification” IEEE transactions on industrial electronics, 65(10), 8320-8329 (2018). 20. Minh-Khai Nguyen, Tuan-Vu Le, Sung-Jun Park, Young-Cheol Lim, Ji-Yoon Yoo, “Class of high boost inverters based on switched- inductor structure”, IET Power Electron., 8(5), 750-759 (2015). 21. Minh-Khai Nguyen, Truong-Duy Duong, Young-Cheol Lim, Yi-Gon Kim, “Switched-Capacitor Quasi-Switched Boost Inverters”, IEEE Transactions on Industrial Electronics, vol. 65(6), 5105-5113 (2018). 22. Sriramalakshmi, P. and Singh, R., 2021. Single stage Boost Cascaded Multilevel Inverter Based on Switched Inductor Structure. In IOP Conference Series: Materials Science and Engineering (Vol. 1012, No. 1, p. 012056). IOP Publishing. 23. Sriramalakshmi, P. and Sreedevi, V.T., 2018, December. A single phase cascaded five level Quasi Switched Boost Inverter based on switched capacitor structure. In 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-6). IEEE. 24. Sriramalakshmi P., Sreedevi V. T. (2021) A Novel Switched Inductor Switched Capacitor-Based Quasi-Switched-Boost Inverter. In: Bose M., Modi A. (eds.) Proceedings of the 7th International Conference on Advances in Energy Research. Springer Proceedings in Energy. Springer, Singapore. 25. Sriramalakshmi, P. and Sreedevi, V.T., 2015. Modified PWM control methods of Z source inverter for drive applications. ARPN J. Eng. Appl. Sci, 10, pp. 6932-6943. 26. Loh.P.C, Vilathgamuwa. D.M, Lai.Y.S, Chua. G.T and Li. Y. W. 2005. Pulsewidth modulation of Zsource inverters. Power Electronics, IEEE Transactions on. 20: 1346-1355. 27. Palanidoss, S., Thazhathu, S.V., Bhaskar, M.S., Kannan, R. and Baboli, P.T., 2021. Comprehensive review of single stage switched boost inverter structures. IET Power Electronics, 14(12), pp.2031-2051.

20 Power Quality Improvement and Performance Enhancement of Distribution System Using D-STATCOM M. Sai Sandeep1, N. Balaji2, Muqthiar Ali1 and Suresh Srinivasan1* Department of Electrical and Electronics Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, India 2 Department of Electrical and Electronics Engineering, Muthayammal Engineering College, Namakkal, India 1

Abstract

Power distribution quality gets deteriorated in power system owing to the power electronics components and by distribution generators. Retaining the power quality is mandatory in the power system and this can be done through D-FACTs devices. In this article, it is proposed to develop the D-STATCOM using the switching level simulation model. Power electronics converter switching is carried out using high-speed insulated gate bipolar transistor (IGBT). The performance analysis D-STATCOM is carried out on a system with a single machine and with IEEE 14 bus system. The numerical findings will be illustrated on a real-world bus system with the D-STATCOM model. This proposed D-STATCOM controls the reactive power, regulates the voltage level, and mitigates the harmonics components effectively. The D-STATCOM is modelled in the Simulink platform and the output results are validated with different case studies. Keywords:  Distribution static synchronous compensator, power quality, flexible alternative current transmission

*Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 1, (357–376) © 2023 Scrivener Publishing LLC

357

358  Smart Grids for Smart Cities Volume 1

20.1 Introduction Fast advancements in power electronics and microelectronics have resulted in new alternatives for more reliable power operation in recent times. Series and shunt compensation approach, technology specifies how the line parameters might be changed to improve line performance. A FACTS technology development has been an area of interest for many research organizations. These groups saw the potential for inverter-based FACTS devices to help utilities run their power transmission with larger capacity, more efficiently, and with greater reliability. These innovative technologies have improved modern energy transmission and distribution systems’ reliability, controllability, and efficiency. Based on environmental and financial limitations, several power plants and transmission networks worldwide have been compelled to operate at almost total capacity. Hence for the better utilization of transmission lines, the thought of FACTS was introduced. It provides new ways to manage power and raise the capacity of existing transmission systems. With the increase of industrial non-linear loads and domestic smarts appliances using power electronics components, the Power Quality (PQ) has recently augmented, resulting in voltage and current deviations. In this work, different techniques and methodologies for PQ investigation are discussed [1]. A comprehensive review is made of the advance in PQ study, which is the primary research focus of electrical power systems. It is determined that PQ study still has a scope for improvement, particularly for PQ events that happen in real time [2]. This work presents an extensive survey of Signal processing and intelligent techniques for automatic sorting of PQ events, as well as the impact of noise on detection and disturbance kinds [3]. An extensive overview of the D-FACTS technology and its use in emerging utilities using renewable energy sources (RES) with power electronic converters. This D-FACTS provides solutions through a list of control techniques for dealing with non-linear loads, sensitive loads, renewable energy sources, and battery storage [4]. This work presents a comprehensive survey on numerous power quality improvement techniques in the network of distribution. The review determined that active power filters, integrated filters, UPQC and D-STATCOM have frequently utilized PQ enhancement methods [5]. Capacitor banks are used for power factor correction in the network of distribution. The amount of reactive power required depends on the connected load. This work provides a technique to regulate the capacitor bank size to attain a desired power factor correction [6]. These capacitor banks are usually linked in parallel with distribution feeder. Interaction

Power Quality Enhancement Using D-STATCOM  359 of reactance between capacitor banks and network parameters produces resonance in the feeder [7]. A detailed review is made to demonstrate guidelines for the training and analysis of the harmonic problems by case studies. This method can be used as a standard system to grow new harmonic simulation systems [8]. PQ issues and their mitigation techniques through the passive filter have been presented in numerous research papers. A comprehensive method is to suppress harmonics in distribution systems based on particular load conditions. The merits, demerits and limitations of compensators are explored in detailed case studies. Demonstrates the idea of a harmonic blocking compensator and proposed fixed-pole resonant harmonic filter [9]. Passive filters are more cost-effective, together with the drawbacks of considerable size and tuning concerns. To overcome these limits, active power filters are utilized to mitigate the PQ issues [5]. Dynamic voltage restorer is a specialized power device connected in series to solve voltage-related PQ problems. Six-switches, split-capacitor, H-bridge, and push-pull three-phase inverters topologies are analyzed and differentiated based on cost, performance and complexity [10]. There are four alternative system topologies of DVR that are analyzed based on the inverter input supply. The elementary system topologies for DVRs and evaluating them in terms of power and voltage rating. The consequence of this comparison there is no-energy storage methods with shunt converter on the load side explore the best performance, followed by the stored energy topology [11]. Voltage variations are the main problem in distribution systems due to the combination of RES and sensitive loads. DVR systems can control short voltage sags. To design and ability to govern these systems is crucial. This paper compares elementary control strategy and finds an optimized solution by combining the pre-sag and in-phase compensation techniques. During voltage disturbances, the suggested method can produce a relevant voltage without overmodulation [12]. This work proposes an internet-based device has been established to monitor PQ continuously. Measurement of PQ at the selected location is used in smart grid applications because PQ is necessary for a healthy grid [13]. The indicator and voltage variations indices were used to calculate voltage variation in the distribution system. The distinctions between the two measures illustrate a low voltage network [14]. Electrical arc furnaces (EAFs) in industrial sectors are the predominant source of voltage fluctuations and flicker at the point of common coupling in the distribution systems. EAF flicker is generated by a low-frequency modulation signal within the ranges of (5-35) Hz. These variations in load result in nonperiodical voltage distortion with considerable voltage fluctuation. The sudden changes in real and reactive power can be compensated effectively by

360  Smart Grids for Smart Cities Volume 1 proposing a non-linear controller in D-STATCOM. Thus, the required load regulation and power quality improvement are achieved [15]. A prime control approach is established on direct-current vector controller design. Close loop control assessment illustrates that a D-STATCOM operates well using the proposed control method. D-STATCOM operates within and beyond the inverter linear modulation limit without overvoltage and system oscillation [16].

20.2 Distribution Static Synchronous Compensator (D-STATCOM) The non-linear loads act as a primary source for the distortion of electrical power. Some of the significant forms of distortion are flicker, phase outage, voltage sag, voltage swell, voltage imbalance, waveform distortion, frequency variation, harmonics, and transients. Figure 20.1 shows the schematic block diagram of Shunt compensation. The power quality gets affected due to the anomalies in utility as well as customer side. Consequently, the effect on the end equipment ranges from component overheating, interference with the communication equipment to permanent damage taking place. The primary source that affects the power quality is non-linear loads, causes harmonic in the electrical network. Current harmonic causes voltage harmonics, which finally leads to voltage distortion in the distribution network. As a result of this, Power quality studies become essential PCC v= Vs -il Zn A1 iaf

Linear load

is = il =iaf + ibf + icf Zn= R+jwL

A2 ibf

Shunt Active filter

Voltage Source Vs

A3 icf

Figure 20.1  Shunt compensation.

iinj

= -i3 -i5 -i7 . . .

Linear load

Nonlinear load

Power Quality Enhancement Using D-STATCOM  361 for utilities and the consumer end. Out of listed power quality concerns, harmonics and voltage variation are the two critical issues that have devastating impacts on the distribution systems. Sometimes, these power quality anomalies may lead to permanent damage to electrical machines, malfunction of robots, loss of data, production interruption, and increased maintenance costs. It is also important to remember that the voltage-sensitive loads get tripped even for the transient distortion in power quality. Solutions are provided in the form of Distributed FACTS devices and custom power devices. Following this, the flexible AC transmission system compensates for anomalies in utility, the custom power devices are installed closer to the load, and it shall compensate for abnormalities on the customer side. Admittedly, the production interruption leads to the financial loss incurred due to tripping, substantiating the introduction of custom power devices to maintain power quality. Since disturbances appear at load, installing compensators at the load end is appropriate to solve quality problems. The mitigation techniques are classified based on the electrical loads and supply systems since both have distinct kinds of power quality issues. A set of power filters of different sorts, such as passive, active, and hybrid in shunt, series, or a combination of both configurations, can be used to assess power quality concerns in current non-linear loads. Externally, depending on the nature of the loads, voltage-fed, current-fed, or a combination of both is employed. The passive, dynamic, and combination of both are in shunt, series configuration in power filters measures the power quality problems because of the presence of non-linear loads are utilized based on the voltage and current fed loads or a combination of both. Custom power devices, such as D-STATCOM, are used for power factor correction, voltage control, mitigation of surplus neutral current, and load balancing in distribution systems with difficulties other than harmonics.

20.3 Modelling of Distribution System 20.3.1 Single Machine System There is a generation at one end of a single machine system, and at the other end, there is a load. The structure of a single machine system is shown in Figure 20.2. Under this section, we will examine the flow of electricity via a single machine that is connected to a more extensive system through a transmission network.

362  Smart Grids for Smart Cities Volume 1 Bus-2

Bus-1 G

Transmission Line Load

Figure 20.2  Schematic representation single machine system. Discrete, Ts = 5e-006 s.

A A A + B B B C C C Programmable 25 kV, 100MVA Voltage Source System

21-km Feeder

B4

a b c n1 25kV/600V

B3

A B C

B1

A B C

A B C

A B C

N

Dstatcom

C

A B

1 MW

Figure 20.3  Simulink model of single machine system with D-STATCOM.

System analysis being able to understand basic principles with such a minimal arrangement is beneficial. We will be better able to cope with substantial, complicated systems if we use basic lower-order systems. The single machine system’s Simulink model is illustrated in Figure 20.3. The synchronous generator is represented as a three-phase voltage source followed by impedance for analytical purposes. The generator’s maximum KVA rating is determined by the intrinsic impedance value. The transmission line is modelled using the pi section parameter block available in the Simulink toolbox. The power system connected to the system is modelled using the PQ load. Power flow analysis results are discussed.

20.3.2

Modeling of IEEE 14 Bus System

It has five producing units, eleven loads, and seventeen transmission lines. The system’s base real power demand is 339.26MW, while the system’s baseline reactive power demand is 79.6MVAr. Figure 20.4 shows a single-­line schematic of the IEEE 14 bus system. Table 20.1 contains the line data. Table 20.2 lists the bus information. Power flow analysis was used to investigate the dynamic behavior of the D-STATCOM on the IEEE 14

Power Quality Enhancement Using D-STATCOM  363 13

14

12 G 11

10 9

6 G

8

G

1

7

4 5

2

3

G

G

Figure 20.4  Single line diagram of IEEE 14 bus system.

bus system. The voltage level, actual power flow, and reactive power flow are measured and described in detail at various buses.

20.4 Simulation Results & Discussions Static Synchronous Compensator (STATCOM) is also referred to as Static Synchronous Condenser. It is based on a power electronic Voltage Source Converter (VSC). When a STATCOM is employed on the distribution side of the transmission network it is referred to as Distribution Static Synchronous Compensator (D-STATCOM). D-STATCOM was simulated in the Simulink environment and its performance was discussed in the following three sections.

20.4.1 Power Flow Analysis on Single Machine System Table 20.2 shows the findings of the power flow study on a single machine system. The load side demand is expected to be 5MW and 3MVAr at 600V; however, the system will only be able to supply 4.5MW and 2.7MVAr at 467V due to high reactive power consumption. As a result of this finding,

364  Smart Grids for Smart Cities Volume 1

Table 20.1  Bus data for 14 bus system.

1

Load

Generator

Type of bus

Voltage at bus (pu)

Angle (Deg)

PL (MW)

QL (MVAR)

PGen (MW)

QGen (MVAR)

Qmin (MVAR)

Qmax (MVAR)

Injected (MVAR)

1

1.126

0

0.0

0.0

173.8

0

0.0

0.0

0.0

2

2

1.12

0

23.87

13.97

49.99

0

-20

100

0.0

3

0

1.103

0

103.62

21.01

0.0

0

0.0

0.0

0.0

4

0

1.12

0

52.58

4.29

0.0

0

0.0

0.0

0.0

5

2

1.131

0

8.36

1.76

21.38

0

-15

80

0.0

6

0

1.143

0

12.32

7

0

1.133

0

0

8

2

1.143

0

0

8.25

0.0

0

0.0

0.0

0.0

0

0.0

0

0.0

0.0

0.0

0

22.63

0

-15

60

0.0

9

0

1.143

0

32.45

18.26

0.0

0

0.0

0.0

0.0

10

0

1.146

0

99

6.38

12.92

0

-6

24

19

11

2

1.148

0

3.85

1.98

0.0

0

-10

50

0.0

12

0

1.147

0

6.71

1.76

12.00

0

0.0

0.0

0.0

13

2

1.149

0

14.85

6.38

25.0

0

-15

60

0.0

14

0

1.147

0

15.51

5.5

0

0

-20

80

0.0

Power Quality Enhancement Using D-STATCOM  365 Table 20.2  Power flow analysis on single machine system. Required demand

Actual power flow

PL = 5MW

PL = 4.5MW

QL = 3MVAr

QL = 2.7MVAr

VL = 600V

VL = 467V

it can be deduced that as reactive power demand rises, the voltage profile lowers. As a result, the system needs reactive power adjustment.

20.4.2 Different Modes of Operation of D-STATCOM on Single Machine System The function of D-STATCOM is to inject current into the transmission system, as mentioned in previous sections. D-STATCOM injects current in two methods, as described below. In both situations, the voltage, the output current, and reactive power flow in the transmission line are depicted in Figures 20.5 to 20.9. Since  the majority of the loads in the power system are inductive, the system required additional reactive power, which necessitated reactive power adjustment. During the  presence of line charging admittance, the receiving end voltage is larger than the transmitting end voltage under weakly loaded or no-load conditions. When the injected current is ahead of the line voltage, the waveform is seen in Figures 20.5 and 20.6. The waveform for the fluctuation of Real and Reactive power provided by the supply in capacitive mode is shown in Figure 20.7. Accordingly, during the inductive mode, Figure 20.8 depicts the waveform of change in Real and Reactive power provided to the load. 3

Voltage Current

Voltage in P.U Current in P.U

2 1 0 –1 –2 –3 0

0.05

0.1

0.15 Time (ms)

Figure 20.5  Injected current leads the line voltage.

0.2

0.25

0.3

366  Smart Grids for Smart Cities Volume 1 1.5

Voltage Current

Voltage in P.U Current in P.U

1 0.5 0 –0.5 –1 –1.5 0

0.05

0.1

0.15

0.2

0.25 0.3 Time (Seconds)

0.35

0.4

0.45

0.5

0.45

0.5

Figure 20.6  Injected current lags the line voltage. 2 1.5 Iq, Iq ref (P.U)

1 0.5 0 –0.5 –1 –1.5 –2 0

0.05

0.1

0.15

0.2

0.35

0.4

(a)

1.5

Moducation index

0.25 0.3 Time (Seconds)

1

0.5

0 0

0.05

0.1

0.15

0.2

0.25 Time (S)

0.3

0.35

0.4

0.45

0.5

0.3

0.35

0.4

0.45

0.5

(b)

1.5

Voltage (V)

1 0.5 0

–0.5 –1 –1.5 0

0.05

0.1

0.15

0.2

0.25 Time (S)

(c)

Figure 20.7  D-STATCOM dynamic performance curves. (a) Comparison of Iq with Iq reference (P.U). (b) Modulation index. (c) Source voltage in (P.U).

Power Quality Enhancement Using D-STATCOM  367

Real Power (MW) Reactive Power (MVAr)

8

× 106

7 6 5 4 3 2 1 0

0.2

0.4

0.6

0.8

1 1.2 Time (Seconds)

1.4

1.6

1.8

2

1.4

1.6

1.8

2

Figure 20.8  Capacitive mode operation of D-STATCOM.

Real Power (MW) Reactive Power (MVAr)

5.5

× 106

5 4.5 4 3.5 3 2.5 2 1.5 0

0.2

0.4

0.6

0.8

1 1.2 Time (Seconds)

Figure 20.9  Inductive mode operation of D-STATCOM.

The converter is working in a capacitive mode in Figure 20.7 for the time period (0.5-1.5) sec. The converter injects reactive power into the system during this time period, increasing the power flow on the transmission line. Figure 20.8 shows the converter working in inductive mode between (0.5-1.5) sec, monitoring the reactive power from the transmission system, resulting in a decrease in power flow on the transmission line, and vice versa in Figure 20.9. Table 20.3 shows the power flow results on a single machine bus system with and without D-STATCOM connected. The dynamic behaviour of D-STATCOM is demonstrated in Figure 20.7 for a step-change in source voltage. The source voltage is initially maintained at 1 p.u. which lasts for 0.1S. At t = 0.2 sec, the source voltage has been raised by 6% to 1.06 p.u. Iq becomes negative as a result of this, and the D-STATCOM adjusts by receiving reactive power from the network. The source voltage is lowered by 6% from the starting value to 0.94 p.u. at t = 0.3sec. As a result, Iq becomes positive, and the D-STATCOM, which gives reactive power to the network, compensates for the voltage drop.

368  Smart Grids for Smart Cities Volume 1 Table 20.3  Load flow analysis on single machine bus system.

Demand on single machine bus system

Power flow on the single machine bus system without D-STATCOM

Power flow on the single machine bus system with D-STATCOM Case I

Case II

PL = 4.5MW

PL = 4.5MW

PL = 5.134MW

PL = 4.03MW

QL = 3 MVAr

QL = 2.7MVAr

QL = 3.08MVAr

QL = 2.4MVAr

VL = 600V

VL = 467V

VL = 607.8V

VL = 413V

The modulation index improves from 0.58 to 0.88 when D-STATCOM switches from inductive to capacitive operation, which correlates to a substantial rise in VSC voltage. The real and reactive power transitions for the D-STATCOM operating in inductive and capacitive modes are illustrated in Figures 20.8 and 20.9.

20.4.3 Step Change in Reference Value of DC Link Voltage We must maintain the DC link voltage at various levels depending on whether we are operating at base load, increased load, or fault condition. The standard D.C link voltage is retained at V (dc ref) = 2.4 KV during the time interval 0