Energy Conversion: Methods, Technology and Future Directions

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Energy Conversion: Methods, Technology and Future Directions

Table of contents :
Contents
Preface
Acknowledgments
List of Reviewers
Chapter 1
Photovoltaic Generators: Development, Simulation and Perspectives
Abstract
Introduction
Principle of Photo-Electric Conversion
Photovoltaic Conversion
Different Photovoltaic Technologies
Crystalline Silicon-Based Photovoltaic Modules
High Efficiency Multi-Junction Cells
New Photovoltaic Technologies
Thin Film Technology
Thin Film Silicon
Non-Silicon Materials
Thin Film Multi-Junctions
Fundamental and Technological Losses in GPV
Current Architecture of Commercial GPV
Fill Factor of GPV
Modeling and Simulation of GPV
Ideal Model
Two Parameter Models
Five Parameter Models
Two Diode Models
Empirical SNL Model
Thermal Model of GPV
Conclusion
References
Chapter 2
Performance Analysis of Solar Energy Conversion Technology
Abstract
Nomenclature
Introduction
Solar Thermal System
Non-Concentrating Collectors
Flat Plate Collector (FPC)
Evacuated Tube Collector (ETC)
Concentrating Collectors
Linear Fresnel Reflector (LFR)
Parabolic Trough Collector (PTC)
Compound Parabolic Collector (CPC)
Central Receiver (CR)
Parabolic Dish Collector (PDC)
Photovoltaic System
Hybrid Solar System
PV Air Collector
System Description
Thermal Modeling
Opaque Type Photovoltaic-Thermoelectric Cooler with Air Duct
PV Module of Opaque Type
Tedlar
TEC
Duct
Result and Discussion
Conclusion
Appendix
References
Chapter 3
An Extended Study of Frequency-Supported Wind Energy Conversion Systems
Abstract
Nomenclature
Introduction
Literature Review
Wind Energy Conversion System (WECS)
Dynamics of WTG
Operating Regions and MPPT Used for WECS
Tip-Speed-Ratio (TSR) Algorithm
Result and Discussion
Scenario 1
Scenario 2
Conclusion
Acknowledgments
References
Chapter 4
RERNN-BCMO-Based Load Frequency Control in Multi-Area Power Systems Using Hybrid Renewable Energy Sources
Abstract
Introduction
Multi-Area Power System for LFC
Problem Formulation
Proposed Approach
Recalling-Enhanced Recurrent Neural Network (RERNN)
Step 1: Initialization
Step 2: Random Generation
Step 3: Fitness Function
Step 4: Check the Iteration
Step 5: Find the Learning Rate
Step 6: Calculation of New Weight
Step 7: Calculate the Direction
Step 8: Termination
Processing Steps of Balancing Composite Motion Optimization (BCMO)
Step 1: Initialization
Step 2: Random Generation
Step 3: Fitness Function
Step 4: Finding Instant Global Point
Step 5: Selection
Step 6: Updation
Step 7: Termination
Result and Discussion
Conclusion
References
Chapter 5
A Review on State-of-the-Art Wind Energy Conversion Systems and Associated Control Strategies for Normal and Fault Conditions
Abstract
Introduction
State-of-the-Art WECS
WECS Control Aspects
DFIG-Based WECS Control
PMSG-Based WECS Control
SCIG-Based WECS and Associated Controls
Fault Ride-Through (FRT)
DFIG-Based WECS with Partially Rated Converters
Variable Speed WECS with Fully Rated Converters
Findings and Research Gaps
Conclusion
References
Chapter 6
Simulation and Analysis of Three-Phase and Five-Phase Variable Speed PMSMs under Open Phase Fault Conditions
Abstract
Introduction
Simulation of Three-Phase PMSM
Analysis of Three-Phase PMSM
Simulation of Five-Phase PMSM
Analysis of Five-Phase PMSM
Conclusion
References
Chapter 7
Investigation and Mitigation of Distribution-Side Power Quality Issues
Abstract
Introduction
Classification of Power Quality Problems
Power Quality Standards
Proposed Solutions to Power Quality Problems
Power Quality Enhancement
Active Power Filters for Mitigation of Distribution-Side Power Quality Problems
Waveform Compensation
Filter Based Method
Heterodyne Method
Pattern Learning and Identification
Instantaneous Power Compensation
Artificial Intelligence Based Control Algorithm
Light Flicker Mitigation through STATCOM
Conclusion
References
Chapter 8
Enhancement of Power Quality in Microgrid Using Optimized PV-Based DSTATCOM
Abstract
Introduction
System Modeling
DSTATCOM
PV Cell
Control Technique Used
Optimization Technique Used
Particle Swarm Optimization
Dragonfly Algorithm
Simulation and Result
Case A: Role of DSTATCOM in Mitigation of Harmonics and Maintaining the Power Quality
Case B: Role of DSTATCOM in Maintaining Voltage Profile
Conclusion
Appendix
References
Chapter 9
Role of Machine Learning in Forecasting Solar and Wind Power Generation
Abstract
Introduction
Machine Learning
Overview
Classification
Regression
Time Series Forecasting
Time Series Forecasting Framework
Solar PV Power Forecasting
Integration Challenges and Importance of Solar PV Power Forecasting
Machine Learning-Based Solar PV Power Prediction
Wind Power Forecasting
Integration Challenges and Importance of Wind Power Forecasting
Machine Learning Based Wind Power Prediction
Power Generation Forecasting Horizons
Forecasting Horizons
Very-Short-Term Forecasting
Short-Term Forecasting
Medium-Term Forecasting
Long-Term Forecasting
Very-Long-Term Forecasting
Forecasting Methodologies
Physical Method
Statistical Method
Hybrid Method
Demonstration of Forecasting Framework
Data Visualization
Testing Stationary
Grid Search
Validating Model Predictions
Result and Discussion
Conclusion
References
Chapter 10
Technological and Communicational Advancements in the Energy Grid: A Review
Abstract
Introduction
Technological Advancement and Energy Grid System
Communicational Advancements in Energy Grid System
Conclusion
References
Chapter 11
Renewable Energy and Energy Storage Systems
Abstract
Introduction
Renewable Energy and Its Prospects
Energy Storage Systems
Roles of Energy Storage (ES) Technologies
Critical Parameters of an Energy Storage Device
Classification of Electrical Energy Storage Technology
Benefits of Energy Storage System
Key Grid Energy Storage Technologies
Battery Energy Storage System (BESS)
Applications of Energy Storage System
Discussion
Conclusion
References
Chapter 12
Review of Energy Storage System Technologies in Microgrid Applications: Characteristics, Issues and Challenges
Abstract
Introduction
Status, Characteristics and Applications of Energy Storage Systems
Energy Storage Technologies
Mechanical Storage
Pumped Hydroelectric Energy Storage
Compressed Air Energy Storage (CAES)
Flywheel Energy Storage System (FESS)
Electrochemical/Battery Energy Storage (BES)
Super-Capacitor (SC)/Ultra-Capacitor (UC)
Electromagnetic/Superconducting Magnetic Energy Storage (SMES)
Hybrid Energy Storage System (HESS)
Conclusion
References
Chapter 13
Determination of Optimal Size for Battery Energy Storage System in Distribution Networks
Abstract
Introduction
Overview of Battery Energy Storage System
BESS Simulation
Problem Formulation
Objective Functions
Constraints
Equality Constraints
Power Balance
BESS Charging
BESS Discharging
Charge Balance
Inequality Constraints
Voltage
Operation Constraints of the Battery
Optimal Size of BESS
Result and Discussion
Conclusion
References
About the Editors
Index
Blank Page

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Energy Science, Engineering and Technology

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Energy Science, Engineering and Technology The Future of Biodiesel Michael F. Simpson (Editor) 2022. ISBN: 978-1-60741-364-6 (Softcover) 2022. ISBN: 979-8-88697-172-9 (eBook) Nanotechnology Applications in Green Energy Systems Rajan Kumar, PhD and Tangellapalli Srinivas, PhD (Editors) 2021. ISBN: 978-1-68507-451-7 (Hardcover) 2022. ISBN: 978-1-68507-479-1 (eBook) Energy Conversion Systems: An Overview Saurabh Mani Tripathi, PhD, and Sanjeevikumar Padmanaban, PhD (Editors) 2021. ISBN: 978-1-53619-131-8 (Hardcover) 2021. ISBN: 978-1-53619-200-1 (eBook) Silicon Carbide Radiation Detectors Marzio De Napoli 2013. ISBN: 978-1-61209-600-1 (Hardcover) 2013. ISBN: 978-1-53619-011-3 (eBook) Power Systems Applications of Graph Theory Jizhong Zhu 2011. ISBN: 978-1-60741-364-6 (Hardcover) 2011. ISBN: 978-1-61728-566-0 (eBook)

More information about this series can be found at https://novapublishers.com/product-category/series/energy-science-engineeringand-technology/

Saurabh Mani Tripathi, PhD Asheesh Kumar Singh, PhD Editors

Energy Conversion Methods, Technology and Future Directions

Copyright © 2023 by Nova Science Publishers, Inc. DOI: https://doi.org/10.52305/VXCB5652

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Published by Nova Science Publishers, Inc. † New York

Contents

Preface

.......................................................................................... vii

Acknowledgments ....................................................................................... xi List of Reviewers ....................................................................................... xiii Chapter 1

Photovoltaic Generators: Development, Simulation and Perspectives .....................1 Hocine Belmili

Chapter 2

Performance Analysis of Solar Energy Conversion Technology ...................................................45 Ajay Pratap Singh, Sumit Tiwar, Harender, Prabhakar Tiwari and S. N. Singh

Chapter 3

An Extended Study of Frequency-Supported Wind Energy Conversion Systems .................................73 Maloth Ramesh, Anil Kumar Yadav, Rajan Kumar and Pawan Kumar Pathak

Chapter 4

RERNN-BCMO-Based Load Frequency Control in Multi-Area Power Systems Using Hybrid Renewable Energy Sources .....................95 Arshad Mohammed and R. Srinu Naik

Chapter 5

A Review on State-of-the-Art Wind Energy Conversion Systems and Associated Control Strategies for Normal and Fault Conditions ...............117 Arika Singh, Kirti Pal and Hemant Ahuja

Chapter 6

Simulation and Analysis of Three-Phase and Five-Phase Variable Speed PMSMs under Open Phase Fault Conditions ............................151 Khadim Moin Siddiqui, Farhad Ilahi Bakhsh, Bhavesh Kumar Chauhan and Arif Iqbal

vi

Contents

Chapter 7

Investigation and Mitigation of DistributionSide Power Quality Issues .............................................165 A. Sharma and B. S. Rajpurohit

Chapter 8

Enhancement of Power Quality in Microgrid Using Optimized PV-Based DSTATCOM...................193 Alok K. Mishra, Suvendu M. Baral, Somya R. Das, Prakash K. Ray, Tapas K. Panigrahi, Asit Mohanty and Akshaya K. Patra

Chapter 9

Role of Machine Learning in Forecasting Solar and Wind Power Generation ..............................211 Rachna Vaish and U. D. Dwivedi

Chapter 10

Technological and Communicational Advancements in the Energy Grid: A Review .........................................................................243 Sandhya Shrivastava, Anam Tariq, Kirti Verma and Ankit Kushwaha

Chapter 11

Renewable Energy and Energy Storage Systems .............................................................269 Hemlal Bhattarai

Chapter 12

Review of Energy Storage System Technologies in Microgrid Applications: Characteristics, Issues and Challenges ........................291 Hannan Ahmad Khan, Mohd. Zuhaib, Mohd. Rihan and Anil Kumar

Chapter 13

Determination of Optimal Size for Battery Energy Storage System in Distribution Networks...............................................313 Soumyabrata Das and Ashutosh Kumar Singh

About the Editors ......................................................................................333 Index

.........................................................................................335

Preface

Energy conversion technology refers to a process that converts energy from one form to another. Energy conversion systems are very complex, especially those that convert raw energy from fossil fuels and nuclear fuels into electrical energy. The increasing day-to-day electricity demand around the world, continuous consumption of conventional energy resources, environmental damage and ever-increasing fuel prices have made it necessary to further research and develop other alternative systems such as photovoltaic (PV) systems, wind turbines, hydro-power systems, battery energy conversion systems, etc. This edited book is intended to serve as a resource for engineers, researchers, scientists and experts wishing to become familiar with energy conversion technologies. This edited volume contains thirteen selected chapters that deal with cutting-edge studies on energy conversion and storage technologies, as described below: In the study of various photovoltaic technologies, it is very interesting to look at the electrical models of photovoltaic generators to better understand their functioning. In Chapter 1, Hocine Belmili presents the most developed photovoltaic technologies, a synthesis of the current state-of-the-art on various materials by specifying the parameters and disadvantages that affect the performance of photovoltaic generators. In Chapter 2, Ajay Pratap Singh, Sumit Tiwari, Harender, Prabhakar Tiwari and S.N. Singh elaborated on the thermoelectric cooler (TEC) incorporated at the base of a photovoltaic (PV) module to improve the overall electrical efficiency of the system. The intermittent nature and variability of wind speed in wind turbine generator (WTG) systems can cause a number of issues, such as severe fluctuations in system frequency. In Chapter 3, Maloth Ramesh, Anil Kumar Yadav, Rajan Kumar and Pawan Kumar Pathak present a detailed review on the support of WTG systems in the design of inertial control, primary frequency control, and secondary controllers. The authors point out that the wind-assisted load frequency control (LFC) scheme significantly improved the system's transient response and resulted in a cost-effective solution. In

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Chapter 4, Arshad Mohammed and R. Srinu Naik applied a new hybrid method to control the load frequency of multi-area power systems consisting of renewable energy sources. The authors claim that the obtained results are promising and effective compared to many other existing methods in all key aspects. In Chapter 5, Arika Singh, Kirti Pal and Hemant Ahuja present an exhaustive review on the overall control aspects of grid-connected wind energy conversion systems (WECSs) based on doubly fed induction generator (DFIG), permanent magnet synchronous generator (PMSG) and squirrel-cage induction generator (SCIG). Nowadays, permanent magnet synchronous machines (PMSMs) are gaining popularity in wind power applications due to their greater efficiency and better performance. In Chapter 6, Khadim Moin Siddiqui, Farhad Ilahi Bakhsh, Bhavesh Kumar Chauhan and Arif Iqbal present a simulation analysis of three-phase and five-stage PMSM drives to demonstrate their fault-tolerant capability by comparing their performance under healthy as well as open phase fault conditions. Power quality (PQ) is becoming one of the major concerns in the power distribution system. In Chapter 7, A. Sharma and B. S. Rajpurohit thoroughly discuss the problem of PQ present in the distribution side of the power system. In addition, the authors also emphasize the state-of-the-art control techniques used for shunt active filters used for PQ mitigation. In Chapter 8, Alok K. Mishra, Suvendu M. Baral, Somya R. Das, Prakash K. Ray, Tapas K. Panigrahi, Asit Mohanty and Akshaya K. Patra present a photovoltaic-based distribution static compensator (PV-DSTATCOM) to mitigate voltage sag and total harmonic distortion (THD) in microgrid application through a metaheuristic dragonfly algorithm to determine the optimized values of controller gains. In Chapter 9, Rachna Vaish and U. D. Dwivedi present a comprehensive assessment of issues in renewable energy integration with emphasis on solar and wind power generation and their integration challenges. They also present a brief state-of-the-art review for forecasting power generation from solar photovoltaic and wind plants. In addition, the authors present a case study of wind power forecasting using the seasonal autoregressive integrated moving average (SARIMA) model to demonstrate the time series forecasting framework. In Chapter 10, Sandhya Shrivastava, Anam Tariq, Kirti Verma and Ankit Kushwaha present a detailed study of the technological and communication-related advancements made in hybrid grid and smart grid systems with the incorporation of Internet of Things (IoT), Machine Learning

Preface

ix

(ML), Deep Learning, Artificial Intelligence (AI) and various communication techniques. There is a need to focus on development and advancement of energy storage systems to realize the practical use of renewable energy for standalone as well as grid-connected applications. In Chapter 11, Hemlal Bhattarai points to the development of various energy storage technologies, with the battery energy storage system as one of the most commonly used options for energy storage. In Chapter 12, Hannan Ahmad Khan, Mohd. Zuhaib, Mohd. Rihan and Anil Kumar present an in-depth review of energy storage systems considering state-of-the-art technology, features, challenges, applications, global situation and economic analysis. In Chapter 13, Soumyabrata Das and Ashutosh Kumar Singh use four different optimization algorithms to meet operational constraints for the installation of battery energy storage system (BESS) in an IEEE 33-bus distribution network with renewable energy sources, considering various technical aspects. We trust that this published book has produced a comprehensive collection of relevant topics on the subject area. Readers are expected to find all the chapters inspiring and very useful while doing their research in the subject area.

Saurabh Mani Tripathi, PhD Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, India Asheesh Kumar Singh, PhD Department of Electrical Engineering Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

Acknowledgments

The editors thank all the authors who have made valuable contributions to this edited book and are grateful to all the reviewers who have generously given their time to review the chapter manuscripts. They also thank the staff of Nova Science Publishers, NY for their continued support during the press production process of this edited book.

Saurabh Mani Tripathi, PhD Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, India Asheesh Kumar Singh, PhD Department of Electrical Engineering Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

List of Reviewers

Ankit Kumar Srivastava

Institute of Engineering and Technology, Ayodhya, India

Anuradha Tomar

Netaji Subhas University of Technology, New Delhi, India

Anurag Dwivedi

Bansal Institute of Engineering Technology, Lucknow, India

Awadhesh Kumar

Madan Mohan Malaviya University of Technology, Gorakhpur, India

Belmili Hocine

Unité de Développement des Equipements Solaires, Algérie

Fatemeh Nasr Esfahani

Lancaster University, United Kingdom

Hemlal Bhattarai

Jigme Namgyel Engineering Dewathang, Bhutan

Kanchan Pawani

Sant Longowal Institute of Engineering and Technology, Longowal, India

Khadim Moin Siddiqui

Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, India

Kirti Pal

Gautam Buddha University, Greater Noida, India

Mahamad Alam

National Institute of Technology, Warangal, India

Mohit Bajaj

National Institute of Technology, Delhi, India

Natwar Singh Rathore

KIET Group of Institutions, Ghaziabad, India

and

College,

xiv

Navdeep Singh Rachna Vaish Saifullah Khalid Shailendra Dwivedi

List of Reviewers

Madan Mohan Malaviya University of Technology, Gorakhpur, India Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, India Airports Authority of India, Lucknow, India

Shashwati Ray

Sardar Vallabhbhai National Institute of Technology Surat, India Bhilai Institute of Technology, Durg, India

Shruti Pandey

KIET Group of Institutions, Ghaziabad, India

Suprava Chakraborty

Vellore Institute of Technology, Vellore, India

Vipin Chandra Pal

National Institute of Technology, Silchar, India Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

Vipin Das

Chapter 1

Photovoltaic Generators: Development, Simulation and Perspectives Hocine Belmili* Unité de Développement des Equipements Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail, W. Tipaza, Algérie

Abstract This chapter exposes the most developed photovoltaic technologies, in order to understand better their functioning, the advances, the perspectives to come, and the synthesis of the current state of the art on the various materials by specifying parameters and type of losses influencing the photovoltaic generator (GPV) performance. In addition, it describes some models commonly used to model the GPV. These models are simulated under MATLAB/Simulink environment. A library of these studied models is created under Simulink with a comparative study between models. The influence of different external (weather) and internal (contact resistance and leakage currents) parameters on the operation of the GPV has also been discussed.

Keywords: photovoltaic generators (GPV), photovoltaic library, photovoltaic models, silicon solar cells, solar cells, thin films, models parameters

Introduction The most widely used photovoltaic technology since the creation of the first cells is crystalline silicon, which currently represents 90% of the world *

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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production for terrestrial applications. This can be explained by the fact that the photovoltaic industry regularly benefits from the development of the semiconductor industry, which is able to provide excellent quality raw material for the photovoltaic modules as well as fully controlled manufacturing processes. Depending on the quality of the silicon, we recall the performance of these devices. We then present the manufacturing principle of high-efficiency multi-junction cells, whose development was motivated primarily by space applications where the cell’s performance takes precedence over manufacturing costs. We will then discuss one of the new generations of solar cells, which use new organic materials. These new compounds, in particular polymers, could revolutionize the PV market due to their low manufacturing cost and ease of use (flexibility, lightness). Research in this area has been extremely active for many years and progress is being made rapidly. We also address the broader field of thin film PV cells, which some refer to as second generation cells because they have historically adhered to crystalline silicon cells. Their main advantage is the small amount of material needed to manufacture a cell compared to the conventional cells (first generation). The most developed thin-film cells use Amorphous Silicon, Copper Indium Gallium Selenide (CIGS), cadmium telluride (CdTe) as a base material, and there are more and more multi-junction cells, which improve the performance of this sector. In the context of improving the performance of photovoltaic systems, we thought it would be interesting to start with an in-depth study of the operating principle of photovoltaic generators (GPV), using electrical models to describe their behavior.

Principle of Photo-Electric Conversion Solar radiation consists of photons with wavelengths ranging from the ultraviolet (0.2μm) to the far infrared (2.5μm). We use the notion AM for Air Mass to characterize the solar spectrum in terms of emitted energy [1]. The total energy carried by solar radiation over a sun-earth distance is of the order of 1350 W/m² (AM0) in space outside the Earth’s atmosphere (Figure 1.1). When the solar radiation crosses the atmosphere, it undergoes an attenuation and a modification of its spectrum, following phenomena of absorption and diffusion in gases, water and dust. Thus, the ozone layer absorbs part of the light spectrum coming from the sun, and in particular part of the ultraviolet radiation that is dangerous for health. The direct solar radiation received at

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3

ground level (at 90° inclination) reaches 1000 W/m² due to the absorption in the atmosphere (AM1). This value changes depending on the inclination of the light rays to the ground. The smaller the angle of penetration ‘θ’ is, greater the atmospheric thickness that the rays will have to cross, hence a consequent loss of energy. For example, the direct energy transported by the solar radiation reaching the ground with an angle of 48° is about 833 W/m² (AM1.5). To know the total radiation received on the ground, it is necessary to add the diffuse radiation. The diffuse radiation is all the radiation whose path between the sun and the point of observation is not geometrically rectilinear and is scattered or reflected by the atmosphere or the ground. Considering this, we obtain a reference of the global spectrum noted AM1.5 with a power of 1000 W/m². The French scientist, Edmond Becquerel, was the first to discover the photovoltaic effect in 1839 [2]. He found that certain materials could produce a small amount of current when exposed to light. Later, Albert Einstein discovered, while working on the photoelectric effect, that light was not only wave-like, but that its energy was carried by particles, the photons. The energy of a photon is given by the relation:

E = (h.c) / 

(1.1)

Where ‘h’ is the constant of Planck; ‘c’ is the speed of the light and ‘λ’ is its wavelength. Thus, shorter the wavelength, greater is the energy of the photon. This discovery was worthy of the Nobel Prize to Albert Einstein in 1905.

Figure 1.1. Standards for measuring the spectrum of light energy emitted by the sun, concept of the AM convention.

The wavelengths of terrestrial solar radiation range from 0.2μm (ultraviolet) to 4μm (infrared) with a maximum energy for 0.5μm. About

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97.5% of the solar energy is between 0.2μm and 2.5μm. From 0.4μm to 0.78μm, the spectrum corresponds to the visible range. Therefore, solar energy collectors must be compatible with these wavelengths in order to trap photons and release them as heat or electrons. Figure 1.2 depicts the solar spectra surveyed under several conditions according to the AM convention. Table 1.1 gives the characteristic energy values of photons for various wavelengths, along with the corresponding areas of the light spectrum.

Figure 1.2. Solar spectra surveyed under several conditions according to the AM convention.

Table 1.1. Energy values of photons from the solar spectrum λ (μm) 0.2 0.4 0.5 0.78 1 2 4

Eph (ev) 6.2 3.1 2.48 1.59 1.24 0.62 0.31

Zone Ultraviolet Visible blue Visible yellow green Visible red Infrared Infrared Infrared

Photovoltaic Conversion Photovoltaic conversion, now widely used, can be defined as the transformation of photon energy into electrical energy through the process of light absorption by matter. When a photon is absorbed by the material, it

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spends part of its energy by colliding with an electron, literally tearing it from the material. This electron being previously at a lower energy level where it was in a stable state then moves to a higher energy level, creating an electrical imbalance within the material resulting in an electron-hole pair, of the same electrical energy. Generally, the electron-hole pair quickly returns to equilibrium by transforming its electrical energy into thermal energy. In the same way, all the energy of the photons not being able to be transformed into the electricity is absorbed by the material in thermal form. The material constituting the PV collectors has an internal temperature that increases proportionally to the solar energy received. The photon-electron conversion rate is low because a number of conditions must be met for this phenomenon to occur. The thermal effect is therefore the main one on most sensors, making their performance worse [3]. Even though the electrical phenomenon is secondary to the thermal phenomenon, recovering all or part of the electrical energy is the primary objective of photovoltaic sensors in the form of cells or generators. This is possible, for example, thanks to solar cells made by associating an N-doped semiconductor material with another Pdoped semiconductor (Figure 1.3).

Figure 1.3. Schematic diagram of photovoltaic conversion.

The energy produced by the absorption of a photon in a material is translated from the electrical point of view by the creation of an electron-hole pair. This reaction leads to a difference in charge distribution, thus creating a difference in electrical potential, which is the photovoltaic effect. The fact of having associated two types of materials to create a junction makes it possible to recover the charges before these last ones are recombined in the material which becomes then neutral. The presence of the PN junction thus allows to

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maintain a current flow to its terminals. The number of photons per unit of wavelength is a data to know for photovoltaic applications to estimate the total energy available. The wavelength corresponding to the maximum number of photons is around 650-670 nm. Most photovoltaic cells use semiconductors to collect the electron-hole pairs created by the collision of photons in the material. However, depending on the material used, the number of useful photons (which can be absorbed) differs. Indeed, each material has its own energy gap (forbidden energy band). Any photon with an energy lower than this gap and arriving at the surface of the material will not have enough energy to pull an electron from the material even if it collides with one. The current produced by a PV sensor is therefore much lower than the quantity of photons arriving on the material because several conditions must be met for the energy of a photon to actually translate into current (compatibility of the material with the wavelengths of the solar spectrum, energy of the photons when they arrive on the material, probability of encountering a photon with an electron, incidence of radiation, thickness of the material, etc.). In addition, another trade-off must be made by the PV collector designer. If the gap of the material is large, few photons will have enough energy to create current but at the terminals of the cell, the open circuit voltage will be large and will facilitate the exploitation of electrical energy. Conversely, a material with a small gap absorbs more photons but presents a lower voltage at its terminals. This trade-off has been quantified by Shockley and Quessier [4]. For example, with a single material, the theoretical maximum conversion efficiency is 31% for an energy gap of about 1.4eV. By comparison, the gap of silicon, which is today the most used material for cells in terrestrial PV collectors, is not very far from this optimum with 1.12eV. Thus, the theoretical maximum for a simple Si junction is about 24% [5]. The potential difference across a PN junction subjected to illumination is also measurable between the terminals of the PV cell. Typically, the maximum voltage of a cell (PN) is about 0.5 to 0.8V. It can be directly measured at its terminals without load (open circuit). This voltage is called open circuit voltage (Vco). When the terminals of a cell are short-circuited, we can measure the maximum current produced by the PV cell and it is commonly called short circuit current (Icc). These values can vary greatly depending on the material used, temperature and solar radiation. Figure 1.4 shows the typical measurable characteristics Icell = f(Vcell) of a PN junction subjected to a constant light flux and in darkness [6].

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Figure 1.4. I(V) characteristics of a photovoltaic cell subjected to different illuminations.

We often speak of conversion efficiency for photovoltaic cells. This term corresponds to the capacity of the cell to transform the energy of the photons that strike it. These measurements are now standardized. Solar cells are therefore tested by manufacturers under an artificial light spectrum corresponding to a typical solar spectrum AM1.5 (or the total irradiance received on the ground at an altitude of 0° with an angle of 48°) under a fixed temperature of 25°C. For simplicity, this convention was used to normalize the efficiencies given in the collector manuals so that they could be compared. The average total power received during the tests by the cells assembled in PV module is 1000W/m² under 25°C. Many scientists are working on increasing the conversion efficiency of photovoltaic cells. Currently, most commercial panels have an efficiency of about 14%. This can translate into the production of 140 peak Watts for a PV module that receives 1000W/m2. Research on the materials that make up the cells is in full swing, as is research on the optimization of the manufacturing of PV cells and panels. To better situate our work on the conversion systems just outside the cells and panels, it seemed important to us to make a synthesis of the current advances made on the production of PV energy. The following sections of this chapter thus outline the major advances and innovations in photovoltaic materials in the race for energy efficiency.

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Different Photovoltaic Technologies Crystalline Silicon-Based Photovoltaic Modules Photovoltaic modules based on crystalline silicon cell (Figure 1.5) have always dominated the market with more than 90% of sales. Cells based on crystalline silicon wafers (c-Si) are divided into two distinct categories, those based on monocrystalline silicon (mc-Si) and those based on polycrystalline silicon (pc-Si). Monocrystalline silicon is more expensive than multicrystalline silicon but allows to obtain a higher efficiency, with nearly 24.7% against 19.8% of record efficiency on small cell in laboratory [5].

Figure 1.5. Schematic diagram of a crystalline silicon cell.

Figure 1.6. Example of the silicon photovoltaic cell production chain.

Crystalline silicon modules are manufactured industrially. All steps of the manufacturing process (Figure 1.6) are continuously improved in order to achieve the theoretical yields as much as possible without increasing the price of the modules. There is still a lot of potential for optimization. The first step

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is the production of ingots from pure silicon for the various silicon melting and crystallization processes. The top material is single-crystal silicon, typically produced by the Czochralski method of introducing a single-crystal preformed seed into the silicon melt ingot [7]. The silicon solidifies on this seed and maintains the same crystalline organization as the seed. The lower material is polycrystalline silicon, which is produced in ingots through different silicon melting and solidification processes. Its crystallization is ensured by a drastic control of the solidification temperature. Over time, the size of the ingots has evolved with the technology from 30kg to 100kg for monocrystalline and from 150kg to 250kg for multicrystalline. Improvements have also been made to the automation of processes and the management of energy consumption. A problem remains, however, concerning the cutting of ingots into wafers, which leads to a significant loss of material. Indeed, after solidification, the ingots are cut into thin layers of about 300μm thickness thanks to an abrasive diamond wire of 150μm diameter [8]. Currently, the wafers have a size of 125 x 125mm2 with a thickness of 330μm. Today, in addition to obtaining thinner wafers, the objective is to reduce sawing losses in order to save Silicon. Photovoltaic manufacturers are developing new generation wafers of 210 x 210mm² and even wider, while reducing their thickness to reach a target of 100μm, (Figure 1.7) [9]. Increasing the size of these cells therefore implies an increase in the power produced and thus the current. The most efficient cells can already provide currents of more than 10A at a voltage of 0.6V.

Figure 1.7. Evolution of the size of silicon photovoltaic cells in recent years.

A third technology directly uses the drawing of ribbons from molten baths [10]. A ribbon serving as a substrate passes through a bath of molten silicon, a thin layer of silicon is then deposited on the substrate. This technique allows for thinner wafers (150μm) and avoids sawing-related losses. Despite the slow deposition rate (a few cm/min), ribbon technology is a promising candidate for reducing the price of photovoltaic peak wattage.

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The wafers are then processed to be doped by diffusion into the material itself to form PN junctions. The addition of an anti-reflection layer and the creation of electrical contacts on the back and front sides (grooving to facilitate the collection of carriers) complete the cell manufacturing process. The finished photovoltaic cells are assembled together (in series and/or in parallel) and encapsulated to become a solar photovoltaic module that can operate for over 20 years. Depending on the arrangement of the cells in the module, the desired power is obtained for an optimal output voltage corresponding to the maximum power point of the assembly (12, 24, 48 V). The great majority of the current panels can deliver a power from 50 to 200Wp. The most recent analyses estimate that a solar photovoltaic system pays back the energy required for its manufacture and installation within a few years. According to a study by the International Energy Agency, the energy payback time of a photovoltaic system is about 1.5 to 5 years, depending on the solar irradiation as well as the site and orientation. Currently, there is no law requiring manufacturers to recover or process solar panels at the end of their life. However, with the rapid growth of the market, some manufacturers and research organizations have joined forces to create an association called “PV Cycle” whose objective is to recycle waste related to photovoltaics. The process of dismantling the modules first involves a thermal treatment, which separates the glass from the cells. Once these elements are detached, the cells are chemically stripped to remove the contacts, the anti-reflection layer and the doping layer. Once these operations are completed, the aluminum, glass and metals can easily be recycled while the intact wafers can be reused inside a module as new wafers. Indeed, even after 20 to 30 years of service, the quality of a silicon wafer remains the same. However, broken wafers can be re-melted to produce new silicon ingots, which can be used to manufacture new modules. The silicon industry initially experienced a great boom thanks to the experience of the semiconductor industry, which has long been using the electrical properties of silicon. It is also interesting to note that the photovoltaic industry, which used to use waste from the electronics industry as a source of silicon, is now facing a growing shortage of solar-grade silicon raw material due to the increasing demand. This situation has led to an industrial transformation with the gradual establishment of new specific production channels for photovoltaic silicon, marking a new phase of development. Today, commercial photovoltaic modules claim a yield of 15 to

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19.6% for the most efficient cells. In the long term, silicon technology still has a major role to play in photovoltaic development. However, it will not be the only technology used. Indeed, to lower prices and make this energy affordable, several attempts at new technological developments are currently underway. In the following, we will present those that seem the most promising.

High Efficiency Multi-Junction Cells Today, most inorganic photovoltaic cells consist of a single PN junction. In this junction, only photons whose energy is equal to or higher than the band gap of the material (noted Eg in eV) are able to create electron-hole pairs. In other words, the photovoltaic response of a single junction cell is limited. Only the proportion of the solar spectrum whose photon energy is greater than the absorption gap of the material is useful, so the energy of the lower photons is not usable. On the other hand, even if the energy of photons is sufficient, the probability of encountering an electron is low. Thus, most photons pass through the material without having transferred their energy. A first answer to limit the losses is known for a long time from the technological point of view, it is enough to use multilevel systems, by stacking junctions with decreasing gaps (Figure 1.8). Thus it is possible to exploit the solar spectrum in its quasitotality with very important conversion yields.

Figure 1.8. Principle of the heterojunction cell.

Multi-junction PV cells based on ΙII-V semiconductor material associations (GaAs, AlGaAs, InGaAs, etc.) have thus been developed since the 1960s presenting very high efficiencies (sometimes over 40%). They are not known to the general public due to their manufacturing cost, which is

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currently the highest (ultra vacuum manufacturing, very slow growth, problem of breakage and defects at the interfaces). The development of high efficiency cells has been motivated primarily by space applications. It is therefore understandable that the most efficient photovoltaic technologies are used in order to optimize the weight of the unit and to ensure that it is autonomous as long as possible. A final advantage to be noted for these cells is their robustness against radiation and impacts. Over time, these cells have demonstrated that it is possible to have energy generators that age very well and can produce energy even with some deterioration. The efficiency of commercialized modules containing multi-junction cells is currently around 30% for an AM0 spectrum. Some research is focused on improving manufacturing technologies to lower the cost and adapt them to terrestrial needs. They are mainly dealing with the problems of interfaces and the transition from small manufacturing volumes to large quantities. Today, land-based racing vehicles or boats use them to ensure their autonomy. But these cells are still too expensive for domestic applications. By using solar concentrators, some people think they can lower the price (less cell surface used) and take a place in the conventional terrestrial market. For example, if we compare the best efficiency without concentrator of a triple junction GaInP/GaAs/Ge reaching 32%, this same cell would reach 40.7% with concentrator [11]. Even higher efficiencies can be achieved with complex cells with 4 to 6 junctions [12]. The basic idea of the photovoltaic concentrator (CPV) is to reduce the cost price of a PV system by focusing the light rays on a solar cell of surface Fc through an optical lens of surface Fo. The concentration ratio C is approximately C=Fo/Fc as shown in Figure 1.9.

Figure 1.9. Schematic diagram of a photovoltaic concentrator.

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The reduction in cell area allows the use of more efficient cells that were previously too expensive to be used on large PV module arrays. In practice, the biggest difference between a photovoltaic field composed of conventional PV modules and a CPV is the need to adjust the orientation of the system to follow the path of the sun. Indeed, a bad angle of light penetration could completely deprive the cells of light, especially since these sensors can only use the direct radiation of the solar spectrum. This function, performed by a tracking system, must be taken into account in the overall price of the CPV and represents a significant part of the investment, making this system ultimately too expensive for the general public. However, the number of examples of concentrated solar power plants clearly shows the interest of these systems and their viability [12]. Today, they are intended for very large power plants exceeding 100 kW. Thermal problems related to the concentration of the rays on the cells require an efficient heat dissipation device. An interesting technique allows to combine photovoltaic solar production with thermal solar production, by recovering the heat emitted by means of a heat transfer fluid, and thus also create a solar water heater.

New Photovoltaic Technologies Organic materials are increasingly used in the field of optoelectronics, with prospects for organic or even molecular electronics, for lighting using organic light-emitting diodes (OLEDs). Although the materials to be used are not optimized in the same way, the photovoltaic field has benefited from technological advances in optoelectronics for several years. So, although this field is really recent, the annual progress is spectacular. Organic, molecular or polymeric materials, based on carbon, hydrogen and nitrogen, are particularly interesting in terms of abundance, cost, weight and implementation. Like semiconductors, they have energy levels that can absorb photons by creating electron-hole pairs that can be used thanks to transitions between the so-called HOMO (hightest occupied molecular orbital) levels and the so-called LUMO (lowest unoccupied molecular orbital) levels. In the absence of separation, the electron-hole pairs can recombine by emitting photons of the corresponding wavelength (fluorescence, luminescence) rather than being converted into heat as seen previously for inorganic materials. The separation of the charges remains today one of the blocking points to be carried out effectively. If they can be separated and transported to an external circuit by means of two

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adjacent phases, then we obtain photovoltaic cells of a new type such as the one presented in Figure 1.10 [13].

Figure 1.10. Schematic diagram of an organic cell.

In order to make full-fledged organic PV cells in the future, it is necessary to improve the properties of electron-hole pair creation in organic molecules or polymers, but also to develop methods of pair separation by associating an acceptor material and a donor material, thanks to different positions of the energy bands. In this context, we speak of LUMO and HOMO materials. One of the most difficult points is the separation of these two phases which tend to mix because the materials are soluble with each other. However, an important advance has made it possible to spontaneously separate the donors and acceptors by making the materials non-soluble [13]. In the case of polymers, it is as if there are two classes of fibers intimately mixed, one conducting electrons and the other conducting holes [14]. Other research has been directed towards a model of photovoltaic cell that is both organic and inorganic (hybrid cells), which offers many advantages over traditional cells. For example, a nanocrystalline cell has been developed that mimics plant photosynthesis. The effective use of organic dye molecules in photovoltaics was discovered in 1991 by Michael Graetzel and has been used for a long time in dye lasers and optical materials. Pigmented organic molecules (dyes) absorb light and release electrons. The electrons are transported to the anode by a porous layer of titanium dioxide (TiO2), an inorganic semiconductor material. At the anode, the electrons are directed to an external circuit where their passage produces electrical energy [15]. The efficiency of these organic solar cells is still lower than 3% because of the nature of the mixture and the problems of electronic recombination [16]. The oxidation of the cell is yet another problem that needs to be addressed in research to find viable long-term solutions. Despite all these drawbacks, the development of these cells is likely to persist in the future as they have a high

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ease of fabrication. Indeed, the active materials can be spread over large surfaces. As these cells can easily grow on flexible materials, their almost natural flexibility is also an advantage. The cost of these organic cells is lower than that of dye cells and they can even be biodegradable. Organic solar cells with their low raw material costs, low production energy requirements, and ability to be manufactured on a large scale are serious candidates with high potential for long-term photovoltaic development.

Thin Film Technology Thin-film PV cells are what some people call second-generation cells because they historically follow the relatively thick crystalline silicon cells. The advantage of thin-film technology is the small amount of material required to manufacture a cell compared to the conventional cells. In contrast to the first generation of crystalline silicon cells, only the amount of photosensitive material that is effective in absorbing most of the solar radiation is deposited (a few microns thick is sufficient). In addition, less expensive methods of manufacturing the cells are used, allowing for full integration. The three emerging technologies today are: • • •

Amorphous and microcrystalline silicon noted TFSi (Thin-Film Silicon in English). The poly-crystalline semiconductor CdTe (Cadmium Telluride). The Cu(In,Ga)Se2 alloy (Copper-Indium/Gallium-Selenium) noted in the literature CIGS.

Thin Film Silicon In TFSi technology based on non-crystallized silicon, amorphous silicon (denoted a-Si) can be directly deposited on a glass substrate at low temperature by a plasma-enhanced chemical vapor deposition (PECVD) process [17]. First, a 0.5 μm thick transparent conductive oxide layer (TCO) is deposited on the glass. This is followed by the following deposition steps: an N-type a-Si layer, then a semi-insulating a-Si layer (1μm total) and finally a P-doped a-Si layer (Figure 1.11). A final silver-based back metal layer provides connectivity. The manufacturing process of these cells allows to significantly lower the production costs. Indeed, the production cycle requires very little energy and the process can generate large unit areas, of the order of one square meter, in one piece. To manufacture a module, it is sufficient to add to the process a deposition of conductive material between the cells serving as interconnections between them. Thus, thanks to this manufacturing flexibility,

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it is possible to simply manufacture panels of any power and voltage depending on the demand and the application.

Figure 1.11. Schematic diagram of an amorphous silicon cell and comparison with a crystalline silicon cell.

Despite low conversion yields of 6% to 7% (limited by the very high recombination rate), this field tends to develop because the price is derisory compared to the current cost of first generation Si cells. Amorphous silicon, having a high absorption coefficient, only needs a very small thickness of silicon, about one micron, thus reducing considerably the costs of raw material and the risk of shortage due to lack of silicon. Several possibilities exist to improve the efficiency of these so-called single-junction cells. Nowadays, materials constituting microcrystalline junctions (μc-Si) can be added or SiGe-based alloys can be created. The introduction of these innovative materials holds long-term promise for TFSi technology. However, these technologies are still in the research stage and will need to be modified in several ways in order to be properly adapted to largescale industrial production. It also remains to be seen how these cells will perform as they age. The most technologically advanced ones have a lifespan of less than 10 years and therefore cannot be used on roofs or in difficult access areas from a maintenance point of view. A study [18] states that an a-Si module produces more energy over a year than a crystalline silicon module (for the same installed peak power) over the same period and on the same site. This is due to a physical phenomenon related to temperature. Indeed, crystalline silicon loses its production capacity as the temperature inside the module increases. This dependence strongly reduces the yield of the modules in summer, for example, at the peak of the solar energy supply. A crystalline silicon module loses about 0.45% of its power when its temperature increases by one degree Celsius (from standard

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conditions 25°C, AM1.5) while, in contrast, a-Si shows a gain of 0.175% per °C [19]. Moreover, amorphous silicon remains relatively stable and can produce energy even under diffuse sunlight and low (and artificial) illumination. Besides these advantages, amorphous silicon modules have a shorter lifetime compared to crystalline silicon modules (>10 years versus >20 years). Indeed, their degradation rate is almost three times higher than that of crystalline silicon panels, especially during the first six months of operation [20]. However, research in this field has been extremely active for several years and progress is rapid. Among the research being carried out is the study of the metastability of amorphous silicon [21]. The aim is to understand the Straebler-Wronski effect relating to the degradation of cell performance under the effect of light. A new cell sees its performance drop by about 10% before stabilizing. The mechanisms involved are still poorly understood. Table 1.2. Performance of the single-junction silicon die Technology Monocrystalline Polycrystalline Amorphous

Typical Yield 12 to 16% 11 to 14% 6 to 7%

Influence Temperature –0.442% per °C –0.416% per °C + 0.175% per °C

Degradation rate –0.38% per year –0.35% per year –1.15% per year

Table 1.2 shows a summary of the different types of silicon currently on the market. The silicon industry, both crystalline and amorphous, still has a major role to play in the photovoltaic development of the future.

Non-Silicon Materials Current research on PV materials also focuses on materials other than silicon, which are better suited to use in thin films and deliver high yields at relatively low costs. Two types of materials seem to be progressively imposed, one based on cadmium telluride (CdTe) and the other on copper-indium/galliumselenium alloys (CIGS). The CGIS cells (Figure 1.12) are constituted by stacking. First, a 0.5μm thick molybdenum metal layer is deposited on window glass to make the back contact. Then a P-type CIGS layer of about 1.5μm is deposited. This is followed by a CdS or ZnS (for Cadmium Sulfide and Zinc Sulfide) layer of about 50nm, and finally a 1μm N-type ZnO (Zinc Oxide) layer serving as the conductive and transparent front contact. Finally, the cell is encapsulated by covering with a glass pane [22, 23]. In the case of CdTe, an N-type CdS layer

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is deposited on glass, followed by a P-type CdTe layer, about 2μm, and a conductive carbon layer serving as the second contact.

Figure 1.12. Schematic diagram of a cell based on CIGS.

CIGS technology currently exhibits the best production efficiency for a cell and for modules compared to all inorganic thin film technologies with cells as high as 19.9% in the laboratory [24] and commercial modules of 12%. However, there are still a number of areas for improvement in order to reduce the price of these cells. The main challenge of thin film CIGS technology is the reduction of the material price. Various tracks exist to try to replace expensive materials like InGa by Al. Moreover, it is also necessary to find solutions to reduce the waste of active raw material during the manufacturing process. A last track consists in simply reducing the thickness of the active layer. The chemical simplicity of cadmium telluride (CdTe) material and its stability make it an attractive material. Its thermo-physical properties and its chemical characteristics make it possible to manufacture cells simply and at low cost. The efficiency of CdTe cells depends strongly on the way the active layers are deposited (deposition temperature, speed and nature of the substrate). Compared to other thin film technologies, CdTe is easier to deposit and therefore more suitable for the production of large area PV modules. The current major drawback is based on the recognized toxicity of Cadmium although it has been demonstrated that the environmental risks associated with CdTe PV cells are minimal [25]. Indeed, CdTe modules would not present any health and environmental risks, and a simple recycling of the modules at the end of their life would definitely solve the pollution problem. The use of CdTe in photovoltaic modules is in fact much less worrying than other uses of

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cadmium, such as in Ni-Cd batteries. Unlike silicon wafers, cells cannot be extracted and reused in almost the same way. They have to go through a metallurgical process. From an energy efficiency point of view, CdTe cells have reached record efficiencies of 16.5% in the laboratory and commercial modules have reached nearly 10.7% [26]. Europe and the USA are already producing CdTe thin film panels. Their efficiency is around 9% and the manufacturing costs seem to be competitive with the c-Si process.

Thin Film Multi-Junctions To improve the performance of thin film cells, double and triple junction architectures have been developed [27]. They are inspired by the multijunction cells initially developed for the space industry in order to benefit from the solar spectrum. However, they are simpler to implement and are better mastered today for the terrestrial market. For example, we can mention the tandem cells based on both amorphous and polycrystalline silicon (a-Si/μc-Si) [27], (Figure 1.13). The best stabilized efficiencies in the laboratory are around 12% for these cells. It is a question, as for the multi-junctions, of stacking two photovoltaic cells which absorb photons of different wavelengths. By this mechanism, the tandem set composed of the association of two cells, can absorb a broader spectrum of light and thus produce more energy than a single junction cell. The first layer of amorphous silicon absorbs part of the spectrum while the other part, which passes through it, is absorbed by the second layer of microcrystalline silicon. The manufacturing process is the same as for thin films, the different layers that make up the cell are deposited by plasma (PECVD) on a glass substrate.

Figure 1.13. Tandem a-Si/μc-Si cell and solar spectrum and absorption spectra.

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A particularly promising amorphous silicon-based triple junction has been developed at the National Renewable Energy Laboratory (NREL) in Colorado, in association with United Solar Systems Corp. This new structure consists of three layers that capture different wavelengths, doubling the efficiencies compared to the theoretical limit of single junctions [28]. The first cell, which captures blue photons, uses an a-Si material with a gap of 1.8eV. The central cell, made of amorphous silicon and germanium (10 to 15% Ge concentration), has a gap of 1.6eV which allows it to absorb green photons. The back cell, in aSi-Ge (40 to 50% of Ge), captures red and infrared photons (Gap of 1.4eV). Commercial triple-junction modules in flexible support are already available with yields around 7% for 1m² solar films. The best efficiency of a-Si/a-SiGe/a-SiGe triple-junction cell is 13% in laboratory. Now, scaling up to industrial scale and producing efficient multi-junction modules are the main challenges facing this technology in order to have a full development. The constituents of the tandem are hydrogenated polymorphic silicon (pm-Si:H) PIN front cells and hydrogenated microcrystalline silicon (μc-Si:H) back cells. It has improved electronic properties compared to a-Si:H and is less susceptible to degradation under light. This material also contains more hydrogen than a-Si:H and has a larger gap (1.8 to 1.9eV instead of 1.7eV). These elements make pm-Si:H a better candidate for the front cell of a tandem structure than the traditional a-Si:H. Hydrogenated microcrystalline silicon (μc-Si:H) has a significantly smaller gap than pm-Si:H, between 1.1 and 1.5eV depending on the crystal fraction of the material. It is therefore a suitable material for the realization of the back cell in a tandem type combination with a pm-Si:H cell. To improve these two materials, known for their better stability compared to amorphous silicon, work has focused on the efficiency of doping and especially on increasing the deposition rate, which is one of the current brakes on industrial development. A particular effort has been dedicated to the back cell in microcrystalline silicon, which is thicker than the front cell, and therefore constitutes the limiting element from the point of view of industrial realization. In this respect, a new way of depositing thin films has been explored, using plasma assisted by electronic resonance, with the objective of reaching deposition speeds of 150 nanometers per minute [29]. Figure 1.14, shows the I(V) characteristic of a pm-Si:H module developed by LPICM with a deposition rate of 48nm/min. The modules fabricated from these two types of cells are then combined from isolated intermediate layers (Figure 1.15). The advantage of this association is to be able to access each

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type of cells independently. Thus, it allows to free from the usual constraint of the tandem cells, namely the adjustment of the currents in each of the cells depending on the cells producing the least current and the realization of an intermediate junction of tunnel type.

Figure 1.14. I(V) characteristic of a pm-Si:H photovoltaic module developed by the LPICM. Association of 8 cells in series.

Figure 1.15. Tandem cell with intermediate electrode for independent management of each cell.

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The objective of this dissociation is to provide more flexibility for an optimal management of the available output powers which, like the currents, are not maximal under the same conditions. This is conditioned by the design of efficient adaptation stages fully adapted to the characteristics of each material layer [30]. Two types of structures can then be studied. On the one hand, a structure, called 4-electrode, for which the polymorphic and microcrystalline cells are electrically and physically separated, either by independent preparation followed by assembly by means of a layer of silicone elastomer, or by successive deposits with the installation of a layer of electrically insulating but totally transparent silicone resin (Figure 1.15). The four-electrode structures can be manufactured by post assembly of polymorphic and microcrystalline cells or modules manufactured separately.

Source: NREL (National renewable energy laboratory) [31]. Figure 1.16. Record yields of photovoltaic cells obtained in the laboratory.

In addition to the four-electrode structure from two assembled modules, other solutions for monolithic three- or four-wire tandem modules can be considered. For example, a so-called three-electrode structure can be formed by successively depositing the two cells and electrically coupling them through an intermediate layer of OCT, acting both as an electrical contact and

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as a reflector for the polymorphic cell. It is also possible to realize a monolithic structure with four electrodes by interposing a resin-based dielectric layer between two layers of TCO. In any case, since the intrinsic characteristics of the two types of materials respond differently to the wavelengths of the solar spectrum, each type of cell combination will not have the same I(V) characteristic and therefore will not have the same optimal power point. Figure 1.16 shows the evolution of the record yields of the main current photovoltaic technologies and Figure 1.17 shows the simulation of the I(V) characteristics of the cells of the different photovoltaic technologies respectively. They include monocrystalline and polycrystalline silicon cells, amorphous silicon cells, cells made of copper indium gallium selenide (CIGS), cadmium telluride (CdTe), but also cells based on III-V compounds that belong to the category of multi-junction cells. Beside the well established fields, new fields have appeared, based on the use of dyes or organic materials, which are only in their infancy.

Figure 1.17. The simulated I(V) characteristic of cells for different photovoltaic technologies [31].

Fundamental and Technological Losses in GPV The photovoltaic conversion efficiency can be expressed by taking into account the loss factors [32]:

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g

o P( )d E g o N ( ) qVOC Af = . . .FF.(1 − R). . d . col P( )d Eg At  P( )d

(1.2)

o

The voltage factor is the ratio of the maximum voltage developed by the cell (Voc) and the voltage corresponding to the gap (Eg/q).

Reflecting Front panel contact coverage

A photon generates only one electron-hole pair. The rest of the energy, higher than the forbidden bandwidth, is mainly dissipated in heat

Incomplete absorption due to limited cell thickness

Fundamental Losses Photons whose energy is lower than Eg-Ephoton cannot contribute to the creation of electron-hole pairs.

Collection yield

Voltage factors

Losses by excess energy Losses by long wavelength of photons photons of the solar spectrum

Table 1.3. Losses limiting the conversion efficiency of GPVs Technological Losses Part of the incident energy is reflected by the non-metallized surface of the cell. Reflection losses are considered as a technological problem that can be solved by the use of special surface treatments and anti-reflection layers.

Af is the area of the front surface not covered by the metal contact and At is the total area. This is a technological limit generated by the coverage ratio 1–Af/At. The coverage ratio is a compromise between the power losses due to the “shadow” of the contacts and the FF losses caused by the series resistance. Special techniques can significantly increase the absorption even in very thin cells

Not all photogenerated carriers are collected. Some recombine in the material or on the surface. Silicon growth and cell manufacturing processes can reduce these recombinations to a fundamental minimum.

Some factors have fundamental limits on which we cannot intervene. Other factors are technological and can be optimized. Among the technological losses (explanation Table 1.3), the reflection in front face is a problem related to the treatment of the surface and the anti-reflection layer

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deposited; the rate of shadow is induced by the grid pattern. This grid has already been the subject of some optimization work in laboratories. There are several ways to improve the collection efficiency: improvement of the material, optimization of contacts and anti-reflection layer, etc.

Current Architecture of Commercial GPV An elementary photovoltaic cell constitutes an electrical generator of low power, insufficient as such for most domestic or industrial applications. Photovoltaic generators are therefore made by associating, in series and/or in parallel, a large number of elementary cells. An association of Ns cells in series allows to increase the voltage of the photovoltaic generator (GPV). On the other hand, a parallel association of Np cells is possible and allows to increase the output current of the generator thus created. The overall electrical current/voltage characteristic of a photovoltaic generator is therefore theoretically deduced from the combination of the characteristics of the Ns*Np elementary cells, which are assumed to be identical and which make up the generator, by means of two affinities with a ratio of Ns parallel to the voltage axis and of Np parallel to the current axis (Figure 1.18).

Figure 1.18. Current GPV architecture.

Under certain conditions of non-uniform illumination and operation close to the short circuit, a cell of the series grouping can even be subjected to the voltage of the (Ns – l) other cells applied in reverse and thus operate as a receiver by dissipating a significant power which can destroy it if the thermal

26

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stress is too strong or if the avalanche voltage is exceeded. To avoid this, a Dp diode must be connected in parallel, called “by-pass diode”. The spontaneous firing of this parallel diode, as soon as a reverse voltage appears at the terminals of the grouping, limits the latter to the value Vd of the direct conduction voltage of the chosen diode and the power dissipated to Vd*Is. Commercially available modules now include these parallel reverse voltage protection diodes. In a dual way, an “anti-reverse” diode Dr, must protect channels in parallel from reverse currents. Figure 1.18 shows the typical wiring of a photovoltaic cell or module array equipped with these protective diodes [33]. It is therefore clear that the control of this photovoltaic energy is not as simple as it seems. The current production in a commercial photovoltaic module is limited by the weakest cell in the assembly. Generally, when assembling the cells, it is necessary to sort them according to their electrical characteristics in order to obtain a homogeneous association and thus avoid limiting the power supplied by a panel because of a dispersion of the cells. Moreover, the aging of the cells can produce a dispersion of the characteristics. The weather conditions to which the modules are subjected (inhomogeneous sunshine, temperature, dirt, snow, rain, deposit of dead leaves, etc.) makes photovoltaic energy a resource that is difficult to control. Currently, in order to make it a proper energy source, a large number of researchers from all over the world are working to make it more affordable in terms of flexibility, efficiency and costs.

Fill Factor of GPV An important parameter is often used from the I(V) characteristic to qualify the quality of a PV cell or generator i.e., fill factor (FF). It is illustrated in Figure 1.19. This coefficient represents the ratio between the maximum power that the cell can deliver, noted Pmax, and the power formed by the rectangle Icc*Vco. The greater the value of this factor, the greater the power that can be used. The best cells will therefore have been subject to technological compromises in order to achieve the ideal characteristics as much as possible [34]. FF =

Pmax I cc .Vco

(1.3)

Photovoltaic Generators

27

Figure 1.19. Notion of form factor FF for a photovoltaic generator.

Modeling and Simulation of GPV In the literature there are several electrical models that describe the operation of GPVs, among others the ideal model, two-parameter, four-parameter, fiveparameter, two-exponential, etc. [35]. In addition, other empirical models have been proposed, such as those described by SNL (Sandia national laboratory, USA) [36]. In this section a simulation under MATLAB/Simulink of some models has been performed.

Ideal Model The equivalent diagram of the ideal GPV model (Figure 1.20), includes a current generator which models the illumination, in parallel with a diode which models the PN junction. This model can be applied to any type of single junction GPV in an idealized manner.

Figure 1.20. Ideal GPV model.

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The equation giving the GPV current is written as follows:

IG = I ph,G - I d ,G

(1.4)

 I G = nP I   I d ,G = nP I d V = n V S  G

(1.5)

with

By substitution, the equation for the I-V characteristic of the GPV becomes:

I =I G

ph , G

−I

exp  q(VG ) −1 s   n ns kT    

 

(1.6)

The ideal model is developed in Simulink as shown in Figure 1.21.

Figure 1.21. Ideal model of the GPV under Simulink.

Two Parameter Models The ideal model does not represent single-junction GPVs well because of losses due to leakage currents and metal contacts. The so-called two-parameter model models GPVs as a current source that always models illumination, a diode that models the PN junction, and a series resistor Rs, G that models losses at the electron-hole pair harvesting contacts and connection losses. This

Photovoltaic Generators

29

model does not take into account the leakage and recombination currents at the materials and the PN junction (Figure 1.22).

Figure 1.22. Two parameter model of the GPV.

The current generated by the GPV is:

IG = I ph,G - I d ,G

(1.7)

 I G = nP I   I d ,G = nP I d I  ph,G = nP I ph

(1.8)

with

Using the following notations:

 I G = nP I   I d ,G = nP I d I  ph,G = nP I ph

(1.9)

We obtain the final expression of the I-V characteristic of the GPV as follows:

I =I G

ph, G

−I

exp q(V + Rs  I  s   n ns kT G

,G

G

)  −1

   

(1.10)

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The two-parameter model is developed in Simulink as shown in Figure 1.23.

Figure 1.23. Two parameter model of GPV in Simulink.

Five Parameter Models The five-parameter model, also known as the one-exponential or one-diode model, takes into account all types of energy loss, both at the level of the connection contacts and at the level of the semiconductor materials making up the PN junction. This model is a form of schematic representation of a GPV where it consider that the latter is a current generator with its various electrical elements placed in a circuit thus characterizing the leakage phenomena. The exponential represents the Shockley diffusion current. Note that the series resistance Rs,G models the losses due to metal contacts and connections and the shunt resistance Rsh,G models the leakage currents, recombination currents directly subtracted from the photogenerated currents and diffusion in the PN junction. Figure 1.24 shows the five-parameter electrical model of the GPV.

Figure 1.24. Model with one exponential of the GPV.

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31

For this model the GPV current is given by:

IG = I ph,G - I d ,G - I Rsh,G

(1.11)

 I G = nP I   I d ,G = nP I d  I ph,G = nP I ph I  Rsh,G = nP I Rsh

(1.12)

with

Using the following notations

  VG = n S V  nS RS  R S ,G = n P   n  RSh,G = S RSh nP 

Figure 1.25. Five-parameter model of the GPV in Simulink.

(1.13)

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We obtain the final expression of the I-V characteristic of the GPV as follows:

I =I G

(

ph, G

  q VG + Rs, G  I G − I exp s n ns kT  

)

 V + R I G s, G G −1 − R sh , G  

(1.14)

This model is considered to be the closest to the reality of single-junction PV cells. In Simulink this model is developed as shown in Figure 1.25.

Two Diode Models The two-diode model, also known as the two-exponential model, has a more complete description than that given by the one-exponential model. The diode in the one-exponential model only models the Shockley scattering current with, ideally, a quality factor of unity. Experimentally, for a good description, it is necessary to consider a quality factor different from unity. This makes the two-exponential model that represents separately the Shockley scattering current and the current due to recombination by trap centers in the space charge zone more accurate. Thus the two diodes symbolize the recombination of minority carriers at the surface of the material on the one hand and in the volume of the material on the other. The equivalent diagram of the GPV according to this model, is represented in Figure 1.26.

Figure 1.26. Two exponential model of the GPV.

The GPV current is:

IG = I ph,G - I d1,G - I d 2,G - I Rsh,G

(1.15)

Photovoltaic Generators

33

with

 I G = nP I   I d 1,G = nP I d 1   I d 2 ,G = nP I d 2 I  ph,G = nP I ph  I Rsh,G = nP I Rsh 

(1.16)

Using the following notations:

  VG = nSV  nS  RS  RS ,G = nP   n  RSh,G = S RSh  n P 

Figure 1.27. Two diode model of GPV in Simulink.

(1.17)

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We obtain the expression of the I-V characteristic of the GPV as follows: I =I G

ph, G

−I

exp q(V + Rs I   n ns kT G

s1

,G

1

G

)−1 − I   

(

s2

  q VG + Rs, G  I G exp n2 ns kT  

)−1 − V    

G

+ Rs, G  I G Rsh, G

(1.18) The two-exponential model as modeled in Simulink is shown in Figure 1.27.

Empirical SNL Model Sandia National Laboratory (Albuquerque, USA) has developed a PV collector model, allowing both testing of collectors as well as estimating their productivity. The main advantages of the model are as follows: •





The variation of the solar spectrum according to the position of the sun in the sky, and its influence on the photovoltaic yield, is taken into account; The model also takes into account the angle of incidence of direct radiation on the GPV. The front of the PV module is usually made of a glass pane, which does not let the radiation through in the same way depending on the angle of incidence of this radiation. This phenomenon appears as soon as the angle of incidence (with respect to the normal to the PV module) exceeds 60°; The method allows to take into account the fact that the STC (Standard Test Conditions), i.e., the reference conditions according to which the quantities representing the module are measured by the manufacturers, are quite different from the normal operating conditions. In these reference conditions, the operating temperature of the PV cells is equal to 25°C, which corresponds to an ambient temperature around 0°C, which is rarely the case in practice;

The performance of the SNL model is based on empirical equations. However, it achieves its versatility and accuracy due to the fact that the individual equations used in the model are derived from the different GPV channels. This model takes into account electrical, thermal, solar and optical effects for solar cells [37]. The performance modeling approach has been well

Photovoltaic Generators

35

validated by extensive outdoor testing of PV modules, and inter-comparison studies with other laboratories and testing organizations. The form of the model is given by equations used when calculating the expected power and energy produced by the GPV, assuming that predetermined module performance coefficients and solar resource information are available.

Figure 1.28. SNL model of the GPV under Simulink.

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The SNL model is described by the following set of equations:

E   I cc ( E , Tc , AM a , AOI ) = ( E ) f 1 ( AM a ). f 2 ( AOI )1 +  Icc .(Tc − T0 ) 0   Ee = I ( E , Tc = T0 , AM a , AOI ) / I cc 0  Voc ( E e , Tc ) = Voc0 + C 3 . ln( E e ) +  Voc .(Tc − T0 )  2 Vm ( E e , Tc ) = Vm 0 + C 4 . ln( E e ) + C 5 .ln( E e ) +  Vm .(Tc − T0 )   I cc ( E e , Tc ) = C1 + Ee C 2 +  I mp .(Tc − T0 )  P = I .V m m  m  FF = Pmp /( I cc .Voc )   (1.19)





where C1, C2, C3, C4, C5 are semi-empirical coefficients. This model requires measurements to be made on the GPV once installed. Under Simulink this model is modeled as shown in Figure 1.28.

Thermal Model of GPV Temperature has an important influence on the photovoltaic phenomenon. It very intersting to develope a model that allows us to take into consideration the thermal part, based on the fundamental equations of the GPV operation according to the one-diode model and the cell temperature model (the set of equations 1.20). Furthermore, Figure 1.29 describes this model in Simulink.   q (VG + Rs , G  I G )   VG + Rs , G  I G I G = I ph, G − I s exp    −1 − Rsh, G n n s kT        E  I (T ) = I ph (T = 298 K ) 1+ (T − 298 K )510 − 4   E0   ph, G  E 3 − g  KT I = K T e  s   NOCT − 20   .E + Ta Tc =  800  

(1.20)

Figure 1.29. Thermal model of the GPV under Simulink.

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Where, NOCT: The normal operating temperature of the PV cells (°C), Ta: Ambient temperature (°C), E: Sunshine (W/m2). Table 1.4 gathers all the previous simulations of the studied GPV models in the form of a library under MATLAB/Simulink. Table 1.4. Library of studied GPV models in MATLAB/Simulink

Ideal model

Five-parameter model Two diode model Thermal model

Type of model

Two parameter model

Simulink GPV Block

Model SNL

Type of model

Simulink GPV Block

Photovoltaic Generators

39

Figure 1.30. Creation of a library of GPV models in Simulink.

Figure 1.30 shows the creation of a library of GPV models “Photovoltaic Library” in Simulink. According to the current/voltage characteristic, a comparative study of the different models studied has been evaluated (Figure 1.31). One can note that the two-diode model, the five-parameter model and the one based on the cell temperature model are relatively close. They express well the operation of the GPV. In addition, these models are easy to implement in the sizing and simulation of photovoltaic systems. The two-parameter model is close to the ideal model. Both models lack accuracy in expressing the operation of the GPV. The SNL model has a characteristic that is a little further from the onediode and two-diode models. This model is more accurate than the other models because it takes into account several factors such as the variation of the solar spectrum and the angle of incidence of the radiation. Otherwise it is based on empirical equations, with predetermined coefficients of performance and availability of solar resources. All these factors make this model difficult to use for the characterization of GPVs as well as for the sizing and simulation of photovoltaic conversion chains.

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Figure 1.31. Comparison between the I(V) characteristics of the studied models.

Conclusion The world of photovoltaic conversion, both at the research and industrial levels, has been undergoing a profound change in recent years associated with the growing interest in photovoltaic energy. This chapter has allowed us to explore the principle of photovoltaic conversion as well as the different technologies used to achieve it. Both technological and electrical aspects have been discussed in order to better understand the whole photovoltaic conversion mechanism. A key element in photovoltaic generators is the conversion capacity, which is the achievement of very high yields. These researches are based on the theoretical analysis of the photon-electron conversion adapted to the whole solar spectrum. The distance to be covered in relation to the current values leaves a potential of at least 30% improvement in relation to conventional cells. It can be conclude also that, whatever the photosensitive material used, a photovoltaic cell subjected to sunlight is a non-linear power generator. The elementary cell therefore remains a low power generator that requires different architectures of associations and management to meet the energy needs.

Photovoltaic Generators

41

In the studies of different photovoltaic technologies, it is very interesting to study electrical models of photovoltaic generators. This approach allows to understand and visualize the evolution of the electrical characteristics of these photovoltaic generators. These data will be used to feed the circuit simulators with a behavior of the GPV as close as possible to reality in order to optimize the adaptation stages and the management necessary to further increase the efficiency of the photovoltaic systems. The characterization of the photovoltaic generators remains a very important discipline, at the time these last ones are exposed to the various real meteorological factors. The developed library is very interesting and beneficial to the researcher, which can be used to directly simulate solar and temperature data for GPV emulators, or through actual data tables injected directly into the model.

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Hocine Belmili Muller, A., Reinecke, M., & Bachmann, A. Towards larger and thinner wafers used in photovoltaic. Thirty-first IEEE Photovoltaic Specialists Conference, 2005, 3-7 Jan. pp: 1019–1022. Hahn, G., Seren, S., Kaes, M., & Schonecker, A. Review on Ribbon Silicon Techniques for Cost Reduction in PV Photovoltaic Energy Conversion. IEEE 4th World Conference in Photovoltaic Energy Conversion, Vol. 1, May 2006, pp. 972– 975. Petibon Stéphane, M. Nouvelles architectures distribuées de gestion et de conversion de l’énergie pour les applications photovoltaïques. Thèse d’état, France, 2009. [New distributed energy management and conversion architectures for photovoltaic applications. State thesis, France, 2009]. Jansen, K. W., Kadam, S. B., & Groelinger, J. F. The Advantages of Amorphous Silicon Photovoltaic Modules in Grid-Tied Systems. IEEE 4th World Conference in Photovoltaic Energy Conversion., Vol. 2, May 2006, pp. 2363 – 2366. Kippelen, B. Organic Photovoltaics. Lasers and Electro-Optics, 2007. CLEO Conference on 6-11 May 2007, pp. 1-2. Puigdollers, J., Voz, C., & Sporer, C. Organic photovoltaic solar cells based on MEH-PPV / PCBM blend. Electron Devices, 2005 Spanish Conference on 2-4 Feb. pp: 279 – 281. del Cueto, J. A. Comparison of energy production and performance from flatplate photovoltaic module technologies deployed at fixed tilt. IEEE Photovoltaic Specialists Conference, 2002., 19-24 May 2002, pp. 1523–1526. Tina, G. M., & Abate, R. Experimental verification of thermal behaviour of photovoltaic modules. IEEE Mediterranean, 5-7 May 2008, pp. 579–584. Laure Marandet, La deuxième vie des modules et Systèmes Solaires. Journal des énergies renouvelables, Mars-Avril 2008, n°184 [The second life of solar modules and systems. Journal of renewable energies, March-April 2008, n°184]. Working group Science, Technology and Applications of the EU PV Technologiy Platform, A Strategic Research Agenda (SRA) for Photovoltaic Solar Energy Technology. June 2007. disponible sur: www.solarserver.de/solarmagazin/ solarreport_1107_e.html. King, R. R., Law, D. C., Edmondson, K. M., & Fetzer, C. M. 40% efficient metamorphic GaInP/GaInAs/Ge multijunction solar cells. Applied Physics Letters, Vol. 90, Issue 18, id. 18351, 3 pages (2007). Barnett, A., Honsberg, C., & Kirkpatrick, D. 50% Efficient Solar Cell Architectures and Designs. Photovoltaic Energy Conversion, IEEE, 4th Vol. 2, May 2006 pp. 2560–2564. Entreprises Concentrix Solar et SolFocus projet de 3MW CPV en Espagne. Déjà 500kW installé sur les sites de Puertollano et Almoguera, disponible sur: [Companies Concentrix Solar and SolFocus 3MW CPV project in Spain. Already 500kW installed at the Puertollano and Almoguera sites, available at:] www.concentrixsolar.de & www.solarfocus.com. Ki Hwan Kim, & Min Sik Kim. Improvement of CIGS microstructure and its effect on the conversion efficiency of CIGS solar cells. Photovoltaic Energy Conversion Conference, Vol. 1, May 2006 pp. 575–578.

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Belmili, H. Etude photoacoustique et photoconductivité du compose CIS. Mémoire de magistère, Université Ferhat Abbes, Sétif 2004 [Photoacoustic and photoconductivity study of the CIS compound. Master's thesis, Ferhat Abbes University, Sétif 2004]. Seigo Ito, Takurou N., & Murakami, Pascal Comte. Fabrication of thin film dye sensitized solar cells with solar to electric power conversion efficiency over 10%. Thin Solid Films, Vol. 516, Issue 14, 30 May 2008, pp. 4613-461. Communiqué de presse du NREL, Record Makes Thin-Film Solar Cell Competitive with Silicon Efficiency. 24 mars 2008 www.nrel.gov/news/press/2008/574.html. Vasilis M. Fthenakis, Life cycle impact analysis of cadmium in CdTe PV production. Renewable and Sustainable Energy Reviews, Vol. 8, Issue 4, August 2004, pp. 303-334. Liyuan Han, Fukui, A., Fuke, N., Koide, N., & Yamanaka, R. High Efficiency of Dye- Sensitized Solar Cell and Module, Photovoltaic Energy Conversion Conference, IEEE, 4th Vol. 1, May 2006, pp. 179–182. Platz, R., Vaucher, N. P., Fischer, D., Meier, J., & Shah, A. Improved micromorph tandem cell performance through enhanced top cell currents. Photovoltaic Specialists Conference, 1997, Twenty-Sixth IEEE, 29 Sept.-3 Oct. 1997, pp. 691– 694. Goya, S., Nakano, Y., Yamashita, N., Morita, S., & Yonekura, Y. Development of amorphous silicon/microcrystalline silicon tandem solar cells. 3rd World Photovoltaic Energy Conversion Proceedings, Vol. 2, 12-16 May 2003 pp. 1570– 1573. Soro, Y. M., Abramov, A., & Gueunier-Farret, M. E. Polymorphous silicon thin films deposited at high rate: Transport properties and density of states. Thin Solid Films, Volume 516, Issue 20, 30 August 2008, pp. 6888-6891. National renewable energy laboratory. http://www.nrel.gov/. Alonso-Garcia, M. C., Ruiz, J. M., & Chenlo, F. Experimental study of mismatch and shading effects in the I-V characteristic of a photovoltaic module. Solar Energy Materials & Solar Cells, Volume 90, Issue 3, 15 February 2006, pp. 329-340. Rabii, A. B., Jraidi, M., & Bouazzi, A. S. Investigation of degradation in field-adged photovoltaic modules. 3rd World Conference on Photovoltaic Energy Conversion, May 11-18, 2003. Yvonne Carts-Powell, Photovoltaics: Research targets more-efficient photovoltaics. Laser Focus World June, 2006. Cédric Cabal. Optimisation énergétique de l’étage d’adaptation électronique dédié à la conversion photovoltaïque. Thése Toulouse, 2008 [Energy optimization of the electronic adaptation stage dedicated to photovoltaic conversion. Thesis Toulouse, 2008]. Charles M. Whitaker, Timothy U. Townsend, & Jeffrey D. Newmiller. Application and validation of new PV performance characterization method. 26th IEEE PV Specialists Conference, Sept. 1997, Anaheim, CA. Sandia national laboratory, http://www.sandia.gov/.

Chapter 2

Performance Analysis of Solar Energy Conversion Technology Ajay Pratap Singh1, Sumit Tiwar1,*, Harender1, Prabhakar Tiwari2 and S. N. Singh3 1Shiv

Nadar University, Dadri, India University of Technology, Gorakhpur, India 3Indian Institute of Technology Kanpur, India 2M.M.M.

Abstract An opaque photovoltaic combined with thermoelectric cooler (PV-TEC) collector is presented in this chapter, in which a TEC module is incorporated at the base of a PV module to improve the overall electrical efficiency of the system. A mathematical model has been described with photovoltaic combined thermoelectric cooler with a duct of air and this thermal modeling is used to calculate the thermal and electrical energy gain. On an annual basis, weather data is received from IMD Pune for the month of May. Several types of temperatures such as solar cells, thermoelectric cooler top side, thermoelectric cooler bottom side, duct outlet temperatures have been calculated through the thermal modeling developed for the system. These results show overall electrical efficiency with TEC increasing by 10.34–12.01%. The addition of a thermoelectric module with a photovoltaic module increases the electrical power from 6.93176 W–15.6753 W to 7.64854 W–17.5571 W. The thermal energy gain by the photovoltaic-thermoelectric module along the duct is in the range of 7.89134 W–20.7169 W.

*

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

46

Ajay Pratap Singh, Sumit Tiwar, Harender et al.

Keywords: opaque photovoltaic, photovoltaic thermal (PVT), thermoelectric cooler (TEC)

Nomenclature 𝐴𝑚 B 𝐶𝑓 𝑑𝑥 𝐸𝑒𝑙 𝐸𝑃𝑉 𝐸𝑡𝑒𝑐 ℎ𝑖 ℎ𝑜 ℎ𝑝1 ℎ𝑝2 ℎ𝑝3 ℎ𝑡 ℎ𝑡𝑓 𝐼𝑡 𝐾𝑓 𝐾𝑔 𝐾𝑖 𝐾𝑡 𝐾𝑡𝑒𝑐 L 𝐿𝑓 𝐿𝑔 𝐿𝑖 𝐿𝑡 𝐿𝑡𝑒𝑐 𝑚̇𝑓 𝑄𝑢 𝑇𝑎

Area of PV module Collector breadth Fluid specific heat Elemental length Overall electrical energy Electrical energy generated by PV module Electrical energy generated by TEC module Inside heat transfer coefficient due to wind velocity Outside heat transfer coefficient due to wind velocity First penalty factor due to glass Second penalty factor due to tedlar Third penalty factor due to TEC module Heat transfer coefficient from back of solar cell to top end of TEC module Heat transfer coefficient from bottom end of TEC module to fluid Global solar radiation Thermal conductivity of fluid Thermal conductivity of glass Thermal conductivity of insulation Thermal conductivity of tedlar Thermal conductivity of TEC Length of collector Thickness of fluid inside the duct Thickness of glass Thickness of insulation Thickness of tedlar Thickness of TEC Mass flow rate of fluid Useful thermal energy gain Ambient temperature

Performance Analysis of Solar Energy Conversion Technology

𝑇𝑐 𝑇𝑓 𝑇𝑓𝑖 𝑇𝑓𝑜 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 𝑈𝑡,𝑐−𝑎 𝑈𝑏,𝑐−𝑓 𝑈𝑡𝑒𝑐 𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑎 𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑓 𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑎 𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑓 𝑈𝑓𝑎 𝑈𝑏−𝑎 𝛼𝑐 𝛽0 𝛽𝑡𝑒𝑐 𝜏𝑔 𝜂𝑚 𝜂0 𝜂𝑡𝑒𝑐 𝜂𝑒𝑙 (𝛼𝜏)𝑒𝑓𝑓 (𝛼𝜏)′𝑒𝑓𝑓 Nu Re 𝑃𝑟

47

Solar cell temperature Temperature of fluid Inlet fluid temperature Outlet fluid temperature TEC top end Temperature TEC bottom end Temperature Overall heat transfer coefficient from top of solar cell to ambient Overall heat transfer coefficient from back of solar cell to ambient Heat transfer coefficient through TEC Overall heat transfer coefficient from top end of TEC module to ambient Overall heat transfer coefficient from top end of TEC module to fluid Overall heat transfer coefficient from bottom end of TEC module to ambient Overall heat transfer coefficient from bottom end of TEC module to fluid Overall heat transfer coefficient from fluid to ambient Overall heat transfer coefficient from bottom of insulation to ambient Absorptivity of solar cell Temperature coefficient of solar cell efficiency Packing factor of TEC Transmissivity of glass Efficiency of solar cell Solar cell efficiency at standard condition Efficiency of TEC Overall electrical efficiency Combine form of absorptivity and transmittivity Effective combine form of absorptivity and transmittivity Nusselt number Reynold number Prandtl number

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Introduction In modern society, the need of energy is increasing day by day which forces to do research and development in the field of renewable energy such as biomass energy, wind energy, solar energy. Solar energy is mainly used for power generation through PV panels and TEC, and some part of solar energy is also useful for generating thermal energy. At the present time, many researchers have focused on photovoltaic thermal (PVT) technology which reduces the energy crisis and produces some thermal energy through the duct. This thermal energy is used for heating, cooling and drying. Tiwari et al. [1] developed a model based on PVT system integration of duct/pipe above or below the PV panel for circulation of water or air to remove thermal energy from PV panel. The PVT system generates the electrical energy from PV module and thermal energy from pipe/duct for space applications like heating, cooling and drying. Aste et al. [2] developed a model of glazed photovoltaic thermal water collector and found that proposed PVT water collector has higher overall efficiency than standard PV module. Renno and Petito [3] developed a concentrated PVT system by calculating the energy produced by the setup using thermal and electrical data. Khaki et al. [4] evaluated the exergy efficiency and energy efficiency of glazed and unglazed buildings combined with PVT systems and optimized parameters and air flow rates using genetic algorithms. It was found that in optimized system, the efficiency is high. Khanjari et al. [5] investigated solar intensity and fluid inlet temperature effects in a nanofluid-based PVT system and found that after a certain period of time, solar intensity increased, electrical efficiency decreased, and thermal efficiency remained constant. Dimri et al. [6] developed a PV-TEC system and found that by the addition of TEC module to PV, the module improves electrical energy output compared to a conventional PV system. He also developed a thermal model of the PVT system (photovoltaic thermal) combined with thermoelectric cooler and found that the electrical efficiency of semi-transparent photovoltaic-thermoelectric cooler is high as compared to PV collector by 7.266%. Its efficiency is also high as compared to semi-transparent photovoltaic-thermoelectric cooler by 4.723%. Yang and Yin [7] studied a combined system consisting of TEC, hot water and PV module (HW/TEC/PV) based on theoretical system which gives 30% more electrical energy output compared to hot water and PV system, photovoltaic thermal and conventional PV system. Liao et al. [8] gave a theoretical model on concentrated PV module with (thermoelectric generator (TEG) module (CPV-TEG) and found that CPV-TEG generates more

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electrical energy than conventional CPV and TEG system. Mohsenzadeh et al. [9] studied the electrical and thermal performance of parabolic type PVT collector with thermoelectric module and found that the thermoelectric module and the electrical part of the PV panel have an average efficiency of 0.16% and 0.167%, respectively. Du et al. [10] investigated a model with PV and TEG in addition to PCM and found that the overall efficiency increased by 6.28% when using PCM with a TEG-PV system. Naderi et al. [11] developed an integrated PCM-PVTEG system and found that the system has higher energy output and PV efficiency than using a PV system alone, and the electricity generation and photovoltaic efficiency of the system was increased by 100% and 1.38%, respectively. Zhang et al. [12] designed a low CPV-thermal system with TEG to generate electricity and found that series connection of the LCPVT-TEG system generated power and PV module efficiency in summer with an average value of 346.03 W and 8.17%. The photovoltaic power of LCPVT-TEG in parallel connection was 439.91W, and the photovoltaic efficiency was 15.3%, which shows that the power and efficiency were better in parallel connection.

Solar Thermal System In a solar thermal system, the receiver and its tracking mechanism are different. When higher fluid temperature is required, concentrated type solar thermal collectors are used to concentrate the solar radiation. Solar thermal collectors are mainly classified into two types. The classification of solar thermal systems is given in Figure 2.1.

Figure 2.1. Classification of solar thermal system [13].

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Non-Concentrating Collectors Solar energy is collected using a non-concentrating type of solar collector in which solar radiation is absorbed by a dark surface with no optical concentration. In this type of collector, the absorber area is equal to the aperture area, so the concentration ratio is almost equal to one. Flat Plate Collector (FPC) In flat plate solar collector, solar energy is absorbed by the dark surface. After that, a part of the absorbed solar radiation is transferred to the air or water. FPC is a major type of solar collector as it has no moving parts, is simple in design and requires little maintenance. It has temperature range around 40°C to 100°C. The intensity from the sun falls on an absorber plate after passing through a transparent cover in a flat plate collector. The radiation, which is absorbed, is partially transmitted to a liquid running through tubes attached to or integral to the absorber plate. The useful gain is the energy transfer. The constructional detail of liquid flat plate collector is given in Figure 2.2. Convection and reradiation from the top surface to the surrounding, as well as conduction through the rear, loses the remaining solar radiation collected in the absorber plate. Conduction heat loss is minimized by insulation and placing the insulating material on the back while the transparent cover minimizes damage by re-radiation and convection. By the use of selective coating, heat loss from the plate is reduced. The selective coating has very good absorption for incoming solar radiation and low emission for outgoing re-radiation [14].

Figure 2.2. Schematic of liquid flat plate collector [13].

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Solar air heater is similar to liquid flat plate collector. Solar air heater is made of plate which is of absorber type with a parallel plate below which forms a narrow tunnel through which hot air flows. An absorber plate of SAH (Solar Air Heater) is made of sheet metal. The thickness of the absorber plate metal sheet is about 1 mm. The transparent glass that is mounted on the top has a thickness of about 4 to 5 mm. Glass wool is used on the top and bottom sides for insulation. The construction details of solar air heater are given in Figure 2.3.

Figure 2.3. Schematic of solar air heater [13].

Evacuated Tube Collector (ETC) One way to increase the performance of a liquid flat-plate collector is to reduce the heat lost by the method of convection from the top. To do this a vacuum is created over the absorber plate. Consequently, the cover must be made of glass because only a tubular surface can withstand the strains induced by the pressure difference. Several designs have already been developed for the ETC. Several long cylindrical flat-plate collector modules are stacked side by side in a single design. Each module is a rectangular metal absorber plate enclosed in an empty cylindrical glass tube. A heat pipe of a selective surface coating is attached to the absorber plate. The construction details of the evacuated tube collector are shown in Figure 2.4. Between the heat pipe and end cover of the glass tube, a glass-to-metal seal is established. The heat is absorbed and the fluid inside the heat pipe evaporates in the evaporator section of the heat pipe within the evacuated glass tube. The evaporated fluid rises in the condenser section and condenses. Condensation heat is supplied to the fluid flowing into the collector header pipe through an aluminum block clamped to the heat pipe and header pipe [15].

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Figure 2.4. Schematic of evacuated tube collector [16].

Concentrating Collectors Another name for concentrating collectors is CSP (concentrating solar power). It is necessary to focus solar radiation when higher temperatures are desired. A concentrator and a receiver make up the collector. A mirror reflector in the shape of a cylindrical parabola is represented as a concentrator. It directs sunlight towards its axis, where it is absorbed onto the surface of the absorber tube and transferred to the fluid running through it. Convective and radiative losses to the environment are reduced by using a concentric glass cover around the absorber tube. Some tracking mechanism is also essential by the CSP to track the position of the sun in order to obtain higher temperatures. It gives a temperature (150℃ - 300℃) and 300℃ for generating the power. Linear Fresnel collectors (LFR), parabolic trough collector (PTC), compound parabolic collector (CPC), central tower receiver (CR) are used for power generation. Linear Fresnel Reflector (LFR) In LFR, the beam radiation is focused on a mirror. The reflector of LFR type collector is made of curved or flat mirror. The LFR receiver is composed of two parts: an absorber type tube and a glass cover. The schematic diagram of the LFR is shown in Figure 2.5. The fluid, which flows inside the tube, is converting solar heat into thermal energy for power generation. Its concentration ratio is generally varies from 10-40 and the temperature varies around 500℃. LFR is commercially acceptable for power generation as it has the lowest capital investment and highest land use factor [17, 18]. Parabolic Trough Collector (PTC) The PTC is also known as cylindrical parabolic collector. In PTC, the concentrator is made of like a parabola which concentrates the sun rays on the receiver. The receiver takes energy from the sun and the sun energy is

Performance Analysis of Solar Energy Conversion Technology

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converted into heat energy. When the heat transfer fluid passes through the tube, the heat energy is converted into thermal energy. The heat transfer fluid can be gas or water, molten salt; and mainly depending on the temperature and the application. The PTC concentration ratio is around 100 and the temperature is around 500°C. To track the motion of the sun, the PTC only needs a single axis tracking system. The schematic diagram of the PTC and its components is shown in Figure 2.6. PTC is the most popular type of solar thermal technology for generating the power due to its good solar to thermal conversion efficiency. The basic components of this type of collector are (i) the concentric transparent cover, (ii) the absorber tube, which is placed on the focal axis and through which the hot liquid flows, (iii) the reflector, and (iv) the support structure [20, 21].

Figure 2.5. Schematic diagram of LFR [19].

Figure 2.6. Schematic diagram of PTC [19].

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Compound Parabolic Collector (CPC) A CPC is not some fictional type of collector. Within a certain range, it has the ability to absorb all incident radiation that descends on the absorber. The tracking of the Sun is also minimized when this parabola is used with the two segments of a parabola facing each other. CPC is capable of absorbing radiation from different angles. There should be some distance between the receiver and the reflector so that the reflector does not act like a fin and radiates heat away from the receiver. Central Receiver (CR) It is also called power tower system. A two-axis tracking based reflector (heliostat) is used in a central receiver system to track the sun's rays put on the field and focus the rays into a receiver located on a centrally located tower. The concentration ratio in the central receiver is very high, usually more than 1000, and the temperature is about 1000 ℃. CR is typically integrated with other power generation systems such as biomass and natural gas based power plants. The schematic diagram of CR is shown in Figure 2.7.

Figure 2.7. Schematic diagram of central receiver [19].

Due to its high performance, power tower is the best technology for power generation. The heliostats field consists of several types of reflectors that focus the beam intensity into the receiver which is part of the solar tower. The heat energy is converted into thermal energy when the heat transfer fluid is passed through the receiver. This thermal energy is later transmitted to an electricity generation system, which then generates electricity.

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Parabolic Dish Collector (PDC) In a PDC the parabolic focal point is used to focus the beam radiation at the receiver, which is located at the parabolic focal point. Solar intensity, which is absorbed, is used to heat the fluid that passes through the receiver. Energy collected by the fluid that can be applied to drive a cycle such as the Brayton cycle or the Rankine cycle to generate electricity. PDC has a high concentration ratio and high efficiency, making them the most promising type of solar thermal technology. A solar dish concentrator consists of (a) receiver having Stirling engine system or heat exchanger, (b) paraboloid concentrator, (c) control units, and (d) tracking system. The schematic diagram of PDC and its components is shown in Figure 2.8.

Figure 2.8. Schematic of parabolic dish collector [19].

A plastic reflective surface or a metal surface forms a parabolic concentrator. In order to maintain the wind speed, this structure is made of high strength material. The tracking mechanism of this system is that the solar dish rotates horizontally and parallel to its axis to strike the solar radiation at the aperture.

Photovoltaic System Photovoltaic system works on the principle of photovoltaic effect. When solar intensity falls on a solar cell, this intensity separates the charged electron in the material. The voltage required for the flow of current in the circuit and then to produce electricity is provided by the electric fields present at the junctions in the material. This is known as the photovoltaic effect [22]. Figure 2.9 shows the flow of electrons in the PV panel.

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Figure 2.9. Technical diagram of PV module [23].

There are many types of PV cells around us. The types of PV cells depend on the type of material used to fabricate the solar cell. The classification of PV cells is shown in Figure 2.10.

Figure 2.10. Classification of PV cell [24].

The I-V curve shows the PV cell energy conversion capability at the present condition of solar intensity and time. Technically, I-V curve shows the combination of voltage and current at which the PV module operates. The IV curves of the PV module are shown in Figure 2.11.

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Figure 2.11. Current-voltage curve of PV module with and without illumination [23].

Hybrid Solar System Hybrid solar system is a combination of a thermal and photovoltaic system. Standalone PV systems can generate less electricity than hybrid systems. Hybrid systems can generate high amounts of electricity which is used for other applications. The classification of hybrid systems is shown in Figure 2.12.

Figure 2.12. Classification of hybrid system [25].

PV Air Collector In a PV air collector, air can flow up or down the photovoltaic module. By blowing air up or down, the module takes heat from the PV module and, therefore, increases the electrical efficiency of the hybrid system. Figures 2.13

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(a)–(c) show schematic diagram of photovoltaic thermal air collector, thermal resistance diagram of photovoltaic thermal air collector and cross sectional view of duct, respectively. Figure 2.14 shows when air passes through the PV bottom side of the module the air is taking heat from the bottom of the PV panel thereby increasing the efficiency of the PV panel. This thermal energy is used for some other purpose such as space heating.

Figure 2.13. (a) schematic diagram of photovoltaic thermal air collector (b) thermal resistance diagram of photovoltaic thermal air collector (c) cross sectional view of duct [26].

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From the above discussion, it is clear that hybrid systems are more practical than photovoltaic systems or solar thermal systems alone. In this chapter, thermal analysis of a hybrid solar thermal system, i.e., PV integrated thermo electric generator is presented which may be useful for researchers to analyze different solar hybrid systems. This chapter shows that PVT and TEG improve the efficiency of solar air collectors, which means that the thermal energy from solar air collectors is used for drying process of agricultural product. The combination of PVT and TEG also improves the energy yield of the system.

Figure 2.14. Greenhouse dryer integrated with PVT air collector [27].

System Description The principle of the opaque PV-TEC system combined with the air duct is that when the sun radiation falls on the PV module, the PV module and the TEC generate some electricity. The PV panel packing factor is taken to be unity (β_c = 1). The air duct is installed just below the thermoelectric module to remove energy in the form of thermals from the lower end of the TEC module. Here, the packing factor (β_tec = 0.4324) of TEC which is dependent on the volume of TEC module, TEC module and PV module area. When radiation falls on the PV panel, part of the solar radiation absorbed by the PV cells and part of the radiation is lost to the surroundings.

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The indirect benefit in the form of thermal energy is coming from the rear end of the PV module to the tedler, and then this energy comes from the tedler to the TEC module. The photovoltaic module packing factor is taken as one i.e., only indirect gain is coming in TEC module. Due to the indirect form of radiation, a temperature difference is created between the TEC module top end and the TEC module bottom end, and some additional electrical power is generated by the principle of the Seebeck effect. From the rear side of the thermoelectric module, thermal energy is transferred to the air duct where the air outlet temperature is raised. At the bottom of the thermoelectric cooler, air flows through a duct. This air then lowers the temperature of the lower end of the TEC to allow for a greater difference in temperature between the TEC. Figure 2.15 shows a diagram of an opaque photovoltaic-thermoelectric cooler with a duct. When the temperature difference between the TEC is high then more electrical energy is generated. Because the air flow in the duct lowers the temperature of the PV module, thus increases the electrical efficiency of the photovoltaic system. Therefore, only the presence of air duct in the system increases the total electrical output of the system. Insulation is used in air ducts to reduce losses caused by convection and radiation. PV-TEC collectors with air ducts can be used with forced type systems to make them self-sustaining, and they can also be used for drying.

Figure 2.15. Photovoltaic thermo-electric-cooler with air duct.

Thermal Modeling Several assumptions have been taken during the formulation of the energy equation for opaque photovoltaic panels (PV panels) and thermoelectric materials (TECs). These are: •

The photovoltaic-thermoelectric collector, which is opaque, is in a steady state.

Performance Analysis of Solar Energy Conversion Technology • • • • • • •

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The temperature gradient between the PV panel, the glass cover and the tedlar is very small. Tedlar, Glass cover and PV panel have very low C_p (specific heat). The flow of heat is one dimensional. The opaque PV module packing factor (β_c =1) is unity. The temperature of the PV panel is assumed to be the same as the tedlar top side temperature. The temperature of the top side of the TEC is assumed to be the same as the temperature of the bottom side of the tedlar. The air flow of the duct is like laminar flow.

Thermal modeling of photovoltaic-thermoelectric collector with an air duct takes into account the TEC packing factor (βTEC ≪ 1) which is less than unity. When (βTEC < 1), it means that the TEC module is partially covered the collector and (𝛽𝑇𝐸𝐶 = 1) means that the TEC module fully covered the collector.

Opaque Type Photovoltaic-Thermoelectric Cooler with Air Duct Consider the elementary area of bdx for the energy balance of a PV-TEC with an air duct [28, 29].

PV Module of Opaque Type If a solar radiation absorbed by PV panel is 𝐼𝑡 𝛼𝑐 𝜏𝑔 𝑏𝑑𝑥 then this solar radiation converted into electrical form of energy will be 𝜂𝑐 𝜏𝑔 𝐼𝑡 𝑏𝑑𝑥. When radiation is absorbed by PV panel, some radiation is lost to ambient by glass of PV panel which is 𝑈𝑡,𝑐−𝑎 ( 𝑇𝐶 − 𝑇𝑎 ) bdx. Some amount of energy is lost by the air which flows in a duct and this duct is placed below the thermoelectric module and the quantity of air which is lost through duct is 𝑈𝑏,𝑐−𝑓 (𝑇𝑐 − 𝑇𝑓 )(1 − 𝛽𝑡𝑒𝑐 ) 𝑏𝑑𝑥 over the TEC non packing area. The amount of the energy transfer from bottom of the PV panel to the top side of TEC over a packing area is ℎ𝑡 (𝑇𝐶 − 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥. One may write 𝐼𝑡 𝛼𝑐 𝜏𝑔 bdx = 𝑈𝑡,𝑐−𝑎 ( 𝑇𝐶 − 𝑇𝑎 )bdx + ℎ𝑡 (𝑇𝐶 − 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 + 𝑈𝑏,𝑐−𝑓 (𝑇𝑐 − 𝑇𝑓 )(1 − 𝛽𝑡𝑒𝑐 )𝑏𝑑𝑥 + 𝜂𝑐 𝜏𝑔 𝐼𝑡 𝑏𝑑𝑥

(2.1)

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Tedlar The input energy in the form of thermal energy comes from solar cell is ℎ𝑡 (𝑇𝑐 − 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 )𝛽𝑡𝑒𝑐 bdx and goes to TEC bottom side which is 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥. Then, ℎ𝑡 (𝑇𝑐 − 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 = 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 (2.2)

TEC In TEC module, the input energy is 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 and this energy is converted to thermal energy to give heat to the duct which is ℎ𝑡𝑓 (𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 − 𝑇𝑓 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 and TEC converts this energy to electrical energy which is 𝜂𝑡𝑒𝑐 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥. Then, 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 = ℎ𝑡𝑓 (𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 − 𝑇𝑓 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 + 𝜂𝑡𝑒𝑐 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 (2.3)

Duct The thermal energy is transferred from the thermoelectric module to the air duct which is flowing into the air duct below the TEC, is ℎ𝑡𝑓 (𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 − 𝑇𝑓 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥. Some amount of energy is lost by the air which flows in a duct are 𝑈𝑏,𝑐−𝑓 (𝑇𝑐 − 𝑇𝑓 )(1 − 𝛽𝑡𝑒𝑐 ) 𝑏𝑑𝑥 over the TEC non packing area. ℎ𝑡𝑓 (𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 − 𝑇𝑓 )𝛽𝑡𝑒𝑐 𝑏𝑑𝑥 + 𝑈𝑏,𝑐−𝑓 (𝑇𝑐 − 𝑇𝑓 )(1 − 𝛽𝑡𝑒𝑐 ) 𝑏𝑑𝑥 = 𝑚̇𝑓 𝐶𝑓

𝑑𝑡𝑓 𝑑𝑥

𝑏𝑑𝑥 + 𝑈𝑏−𝑎 (𝑇𝑓 − 𝑇𝑎 ) 𝑏𝑑𝑥

(2.4)

After solving the equations (2.1)–(2.3), the temperature of solar cell 𝑇𝑐 , temperature of TEC top end 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 , temperature of TEC bottom end 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 are obtained as 𝑇𝑐 =

(𝛼𝜏)𝑒𝑓𝑓 𝐼𝑡 + 𝑈𝑡,𝑐−𝑎 𝑇𝑎 + 𝑈𝑏,𝑐−𝑓 𝑇𝑓 (1− 𝛽𝑡𝑒𝑐 )+ ℎ𝑡 𝛽𝑡𝑒𝑐 𝑇𝑡𝑒𝑐,𝑡𝑜𝑝

𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 =

𝑈𝑡,𝑐−𝑎 + ℎ𝑡 𝛽𝑡𝑒𝑐 +𝑈𝑏,𝑐−𝑓 (1− 𝛽𝑡𝑒𝑐 ) 𝑈𝑡𝑒𝑐 𝑇𝑡𝑒𝑐,𝑏 𝛽𝑡𝑒𝑐 + ℎ𝑝1 (𝛼𝜏)𝑒𝑓𝑓 𝐼𝑡 +𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑓 𝑇𝑓 +𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑎 𝑇𝑎 𝑈𝑡𝑒𝑐 𝛽𝑡𝑒𝑐 + 𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑎 + 𝑈𝑡𝑒𝑐,𝑡𝑜𝑝−𝑓

(2.5)

(2.6)

63

Performance Analysis of Solar Energy Conversion Technology

𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 = (𝛼𝜏)′𝑒𝑓𝑓 𝐼𝑡 + 𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑎 𝑇𝑎 (1−𝜂𝑡𝑒𝑐 )+[(1−𝜂𝑡𝑒𝑐 )𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑓 +ℎ𝑡𝑓 𝛽𝑡𝑒𝑐 ]𝑇𝑓 (1−𝜂𝑡𝑒𝑐 )𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑎 +(1− 𝜂𝑡𝑒𝑐 )𝑈𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚−𝑓 + ℎ𝑡𝑓 𝛽𝑡𝑒𝑐

(2.7)

Equation (2.4) can be written as 𝑑𝑇𝑓

+ 𝑎 𝑇𝑓 = 𝑓(𝑡)

𝑑𝑥

(2.8)

where, a=

(𝑈𝑓𝑎 + 𝑈𝑏−𝑎 )𝑏

(2.9)

𝑚̇𝑓 𝐶𝑓

𝑓(𝑡) = [ℎ𝑝3 (𝛼𝜏)′𝑒𝑓𝑓 + ℎ𝑝1′ (𝛼𝜏)𝑒𝑓𝑓 +ℎ𝑝2′ ℎ𝑝1 (𝛼𝜏)𝑒𝑓𝑓 + ℎ𝑝3′ (𝛼𝜏)′𝑒𝑓𝑓 ]𝑏 𝐼𝑡 +(𝑈𝑓𝑎 +𝑈𝑏−𝑎 )𝑏𝑇𝑎 𝑚̇𝑓 𝐶𝑓

(2.10)

By solving equation (2.8) with 𝑇𝑓 |𝑥=0 = 𝑇𝑓𝑖 , the following solution of (𝑇𝑓 ) is obtained: 𝑇𝑓 =

𝑓(𝑡) 𝑎

[1 − 𝑒 −(𝑎𝑥) ] + 𝑇𝑓𝑖 𝑒−(𝑎𝑥)

(2.11)

The boundary condition is 𝑇𝑓 |𝑥=𝐿 = 𝑇𝑓𝑜 which is used to calculate the outlet temperature of fluid 𝑇𝑓𝑜 . ̅f is calculated as The average temperature of fluid T 1 𝑥=𝐿 𝑇̅𝑓 = 𝐿 ∫𝑥=0 𝑇𝑓 dx

(2.12)

After solving equation (2.12), we can find the expression for 𝑇̅𝑓 as

𝑇̅𝑓

{1−𝑒



[ℎ𝑝3 (𝛼𝜏)′𝑒𝑓𝑓 + ℎ𝑝1′ (𝛼𝜏)𝑒𝑓𝑓 +ℎ𝑝2′ ℎ𝑝1 (𝛼𝜏)𝑒𝑓𝑓 + ℎ𝑝3′ (𝛼𝜏)′𝑒𝑓𝑓 ]𝐼𝑡

=[

(𝑈𝑓𝑎+ 𝑈𝑏−𝑎 )𝐴𝑚 ṁf Cf

(𝑈𝑓𝑎 + 𝑈𝑏−𝑎 )𝐴𝑚 ṁf Cf

(𝑈𝑓𝑎 + 𝑈𝑏−𝑎 )

}

]

+ 𝑇𝑓𝑖 ⌈{

1−𝑒



(𝑈𝑓𝑎 + 𝑈𝑏−𝑎 )𝐴𝑚 ṁf Cf

(𝑈𝑓𝑎 + 𝑈𝑏−𝑎 )𝐴𝑚 ṁfCf

}⌉

+ 𝑇𝑎 ] × [1 −

(2.13)

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After substituting 𝑇̅𝑓 from equation (2.13) in equation (2.7), we find the bottom end temperature of TEC (𝑇̅𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 ) in average form. Then, substituting ̅𝑇𝑓 in equation (2.6), we get the top end temperature of TEC (𝑇̅𝑡𝑒𝑐,𝑡𝑜𝑝 ) in average form. At last, substituting 𝑇̅𝑓 in equation (2.5), we have the solar cell temperature (𝑇̅𝑐 ) in average form. The PV module efficiency is calculated [30] as 𝜂𝑚 = 𝜂0 [1 − 𝛽0 (𝑇𝑐 − 𝑇𝑎 )]

(2.14)

Electrical power generated by PV module can be calculated using equation (2.15) and here the packing factor of solar cell is taken as unity. 𝐸𝑃𝑉 = 𝜂𝑚 𝐼𝑡 𝐴𝑚

(2.15)

The energy received in the thermal form by the thermoelectric material is 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝐴𝑚 and when this thermal energy term multiplies with the thermoelectric material efficiency 𝜂𝑡𝑒𝑐 then it gives the electrical power generated by the thermoelectric material [6]. 𝐸𝑡𝑒𝑐 = 𝜂𝑡𝑒𝑐 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝐴𝑚

(2.16)

The total electrical power (𝐸𝑒𝑙 ) produced by the photovoltaicthermoelectric cooler combined with an air duct is calculated as: 𝐸𝑒𝑙 = 𝜂𝑚 𝐼𝑡 𝐴𝑚 + 𝜂𝑡𝑒𝑐 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 𝐴𝑚

(2.17)

Total electrical efficiency of the photovoltaic-thermoelectric collector with air duct is calculated by 𝜂𝑒𝑙 =

𝐸𝑒𝑙 𝐼𝑡 𝐴𝑚

=𝜂𝑚 +

[𝜂𝑡𝑒𝑐 𝑈𝑡𝑒𝑐 (𝑇𝑡𝑒𝑐,𝑡𝑜𝑝 − 𝑇𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 )𝛽𝑡𝑒𝑐 ] 𝐼𝑡

(2.18)

The useful energy in the thermal form generated by the system is calculated as 𝑄𝑢 = 𝑚̇𝑓 𝐶𝑓 (𝑇𝑓𝑜 − 𝑇𝑓𝑖 )

(2.19)

Performance Analysis of Solar Energy Conversion Technology

65

Result and Discussion Figure 2.16 shows the variation of the environmental parameters such as ambient temperature and solar radiation from 08.00 h to 17.00 h in the month of May. It can be seen from Figure 2.16 that the solar intensity (𝐼𝑡 ) value is maximum at 12.00 h which is 956.82 W/m2 and ambient temperature varies from 30.80°C to 38.50°C.

Figure 2.16. Variation of solar intensity and ambient temperature vs time.

It can be observed from Figure 2.17 that the average temperature of the solar cell (𝑇̅𝑐 ) is the highest followed by the average temperature of TEC topend (𝑇̅𝑡𝑒𝑐,𝑡𝑜𝑝 ), then TEC bottom-end average temperature (𝑇̅𝑡𝑒𝑐,𝑏𝑜𝑡𝑡𝑜𝑚 ). The average fluid temperature (𝑇̅𝑓 ) is high as compared to the ambient temperature (𝑇𝑎 ). It can be observed from Figure 2.18 that the efficiency of a PV module (𝜂𝑚 ) diminishes as the temperature of the solar cells (𝑇𝑐 ) rises. This occurs as a collision of electrons in the depletion region of solar cells that increases with temperature, resulting in increased electrical loss due to higher cell temperatures. It can be observed from Figure 2.19 that the total electrical efficiency (𝜂𝑒𝑙 ) of the system is high compared to the photovoltaic module’s efficiency (𝜂𝑚 ) because the TEC module generated an additional amount of energy. Figure 2.20 illustrates that the electrical power gain by photovoltaic module is high compared to thermoelectric material. The PV module generated the electrical power which is in the range of 6.93176 W–15.6753 W and TEC generated the electrical power which is in the range of 0.309939 W– 0.813673 W.

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Figure 2.17. Variation of solar cell, TEC, duct temperature vs. time.

Figure 2.18. Variation of photovoltaic temperature and efficiency vs. time.

Figure 2.19. Variation of overall efficiency and cell efficiency vs. time.

Performance Analysis of Solar Energy Conversion Technology

67

Figure 2.20. Variation of electrical gain of PV and TEC vs. time.

It can be seen from Figure 2.21 that the total electrical gain is in addition to the electrical power gain by the photovoltaic module and the TEC module which is in the range of 7.64854 W–17.5571 W and the thermal gain of the PV-TEC collector varies from 7.89134 W–20.7169 W. Thermal gain is maximum at solar peak time 12.00 h–13.00 h. The energy generated by this system can be used for various applications such as drying and space heating. It can be seen that in Figure 2.22, when the mass flow rate is increased, both the electrical and thermal gains increase simultaneously but the thermal gain is high. At an air flow rate of 0.1 kg/s, the thermal gain is 20.7169 W and the electrical gain is 17.5571 W. With further increase in air flow rate, the electrical and thermal gain does not increase much, and the electrical power requirement is also high so this mass flow rate is optimal.

Figure 2.21. Variation of thermal and electrical gains vs. time.

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Figure 2.22. Variation of the thermal and electrical gains vs. mass flow rate.

Conclusion From the present study, the following conclusions can be drawn: •

• • •

The photovoltaic module delivers an electrical power in the range of 6.93176 W–15.6753 W and the thermoelectric cooler module delivers electrical power in the range of 0.309939 W–0.813673 W. The photovoltaic module delivers electrical efficiency in the range of 12.29%–13.97%. Integration of TEC with PV module enhances the overall efficiency in the range of 10.34%–12.01%. The thermal energy gain by the photovoltaic-thermoelectric module with duct is in the range of 7.89134 W–20.7169 W.

The results suggest that efficient TEC modules are needed to enhance the electrical output. However, it is also clear that the TEC module not only helps in electricity generation but also increases the electrical output of the PV module by reducing its temperature.

Appendix Am = 0.1332 m2 b= 0.36 m

L= 0.37 m Lf = 0.01 m

β0 = 0.0045 /K τg = 0.9

Performance Analysis of Solar Energy Conversion Technology

Cf = 1005 J/kg K f =0.02622 W/m K K g = 0.816 W/m K K i = 0.166 W/m K K t = 0.033 W/m K K tec =1.82 W/m K

Lg = 0.003 m Li = 0.100 m Lt = 0.005 m Ltec = 0.004 m ṁf = 0.1 kg/s Ta = 298 K

69

ηtec = 0.08 V = 2 m/s βtec = 0.4324 αc = 0.9 ηo = 0.15

References [1]

[2]

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Tiwari, G. N., Mishra, R. K., & Solanki, S. C. (2011). Photovoltaic modules and their applications: A review on thermal modelling. In Applied Energy, Vol. 88, Issue 7, pp. 2287–2304. Aste, N., Leonforte, F., & del Pero, C. (2015). Design, modeling and performance monitoring ofa photovoltaic-thermal (PVT) water collector. Solar Energy, vol. 112, pp. 85–99. Renno, C., & Petito, F. (2016). Experimental and theoretical model of a concentrating photovoltaic and thermal system. Energy Conversion and Management, 126, pp. 516–525. Khaki, M., Shahsavar, A., Khanmohammadi, S., & Salmanzadeh, M. (2017). Energy and exergy analysis and multi-objective optimization of an air based building integrated photovoltaic/thermal (BIPV/T) system. Solar Energy, 158, pp. 380–395. Khanjari, Y., Kasaeian, A. B., & Pourfayaz, F. (2017). Evaluating the environmental parameters affecting the performance of photovoltaic thermal system using nanofluid. Applied Thermal Engineering, 115, pp. 178–187. Dimri, N., Tiwari, A., & Tiwari, G. N. (2017). Thermal modelling of semitransparent photovoltaic thermal (PVT) with thermoelectric cooler (TEC) collector. Energy Conversion and Management, 146, pp. 68–77. Yang, D., & Yin, H. (2011). Energy conversion efficiency of a novel hybrid solar system for photovoltaic, thermoelectric, and heat utilization. IEEE Transactions on Energy Conversion, 26(2), 662–670. https://doi.org/10.1109/TEC.2011.2112363. Liao, T., Lin, B., & Yang, Z. (2014). Performance characteristics of a low concentrated photovoltaic- thermoelectric hybrid power generation device. International Journal of Thermal Sciences, 77, pp. 158–164. Mohsenzadeh, M., Shafii, M. B., & Jafari mosleh, H. A novel concentrating photovoltaic/thermal solar system combined with thermoelectric module in an integrated design. Renewable Energy, 113, 2017, pp. 822–834. Du, D., Darkwa, J., & Kokogiannakis, G. (2014).Numerical modeling and simulation of an integrated TEG/PCM system for the enhancement of PV power output. 12th International Energy Conversion Engineering Conference, IECEC 2014.

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Chapter 3

An Extended Study of Frequency-Supported Wind Energy Conversion Systems Maloth Ramesh1, Anil Kumar Yadav1,*, Rajan Kumar1 and Pawan Kumar Pathak2 1National 2School

Institute of Technology Hamirpur, Hamirpur (H.P.), India of Automation, Banasthali Vidyapith, Rajasthan, India

Abstract The appearance of the solar, wind energy (WE) based renewable energy (RE) power plants into the power system brings many advantages to the modern power system, such as sustainable energy supplies and an ecofriendly environment by reducing carbon dioxide emissions and increasing the life span of the fossil fuels used in conventional power plants. The WE is one of the most abundant RE forms in the power sector. The rapid growth in wind energy conversion systems (WECS) is due to the sophisticated technology and a reduction in the cost of wind turbines. However, the intermittent nature and variability of wind speed in the WECS can cause several issues, such as severe fluctuations in the system frequency and a higher rate of change in frequency (RoCoF). The WECS cannot support system frequency like the frequency of traditional power systems due to its low inertia and separation of the WECS from the AC grid via a current source-based inverter. This chapter provides a detailed reviewof the support of the wind turbine generator (WTG) system in inertial control (IC), primary frequency control (PFC) approaches and the design of secondary controllers. An intelligent WECS is ultimately proposed to participate in a secondary frequency control scheme for reliable operation. *

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Keywords: primary frequency control, secondary frequency control, wind energy conversion system (WECS)

Nomenclature A Cp D D Δf G J J KB Kg Kt KWTG M ΔPB ΔPDEG ΔPL ΔPMG ΔPWTG Rd Tem Tem * Tg Tt TWTG u V 𝜔𝑚 𝛽 𝜆 𝜆𝑜𝑝𝑡

Swept area Power coefficient Microgrid frequency damping Damping coefficient Frequency deviation Gain multiplier Objective function Moment of inertia Battery gain Governor gain Turbine gain Wind turbine gain Inertia constant Battery power Diesel generator power Load power Microgrid power Wind generator power Droop constant Generated torque Reference torque Governor time constant Turbine time constant Wind time constant Control signal Wind speed Angular speed Pitch angle Tip speed ratio Optimal tip speed ratio

An Extended Study of Frequency-Supported Wind Energy …

75

Introduction The growing rate of worldwide electricity demand continued consumption of conventional energy resources, environmental damage, and rising costs of fossil fuels necessitate power generation from other than fossil fuels called solar-wind-based power generation systems [1]. The penetration of renewable energy (RE), mainly solar-wind energy-based system, brings many advantages such as reduction in global warming effects, free fuel availability, being eco-friendly and no need for colossal fuel transportation. It has been observed that many places across the world that, despite being connected to the grid, are facing power shortages for 10-12 hours a day. This has the most adverse effect on the health and economic growth of the residents of those regions. Many places are rich in RE sources such as solar, wind and biomass. Other renewable sources have some limitations. A commercial reservoir is needed to control the water flow in a hydropower system. Tidal and geothermal plants are geographically dependent, and there is an irregular tidal flow compared to the wind energy conversion system (WECS) [2]. Among various RE sources, wind energy (WE) is one of the most influential and purest forms of energy generation. According to the global wind energy council report-2021 [3], the total installed capacity of WE is 743 GW, which reduces CO2 emission by 1.1 billion tonnes worldwide. Innovations and economies of scale have nearly quadrupled the global WE market over the last decade, establishing itself as one of the world’s most costeffective and resilient energy sources. In market outlook, the regions Asia and Europe are the first and second in the growth of WE capacity predicted from 2021 to 2025, respectively. The WECS is used to convert the moment of WE into mechanical power. Again, the mechanical energy is converted into electrical energy by wind turbine generators (WTGs). The WTGs are classified into two types: 1) constant speed WTGs, and 2) adjustable speed WTGs. The constant speed WTGs have employed induction generators, whereas adjustable speed WTGs use either permanent magnet synchronous generators (PMSGs) or doubly-fed induction generators (DFIGs) [4]. The traditional plants have sufficient reserve power due to the kinetic energy (KE) stored in their rotating parts, which compensate for the sudden frequency fluctuations called IC. According to the IC perspective, the WTGs are constant speed (type-I and type-II WTGs) and adjustable rate (type-III and type-IV WTGs). The high penetration of the WE generation systems causes several technical issues. The most important technical issue is frequency control due to the unpredictability and seasonal dependency of the WE. Moreover, wind

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generators contain no or less reserve power for system support and are separated from the grid with a power electronic interface. This de-coupling from the AC grid via power converters makes the system’s inertia further low. The power electronic interface (PEI) is associated with a stator in PMSG and the rotor in DFIG. The PEI regulates the generator speed to obtain the maximum energy from WECS [5]. However, this PEI isolates the wind generator to participate in frequency control during the disturbances [6]. In the previous studies, many researchers proposed advanced control techniques to extract KE stored from the rotating blades of a WTG to improve the responses of inertial and primary frequency controls of the system. Due to the higher penetrations of wind energy, the overall system inertia is reduced, leading to the higher value of the rate of change of frequency (RoCoF). The higher values of RoCoF are good enough to activate the protection system even for a small value of frequency disturbance and cause disconnection of active electrical units. Hence, a secondary controller is required in the WECS to maintain the grid frequency to its prespecified values. However, the contribution of WE systems to load frequency control is an active research area. In this work, detailed aerodynamic modeling and wind power extraction at various wind speed (𝑉) patterns are explored. Many countries have widely accepted WE as an alternative conventional energy source, making WECS a globally leading renewable energy system for cost-effective and highefficiency energy production. Yearly global WE installed capacity is graphically shown in Figure 3.1 [7].

Figure 3.1. Global WE installed capacity [7].

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Literature Review The contribution of wind generators to frequency control relies primarily on active power modeling for droop, dead-band and reserve power control schemes. Based on the time frame involved from the onset of the frequency deviation, the wind contribution to the frequency regulation, classified as IC, PFC and secondary control, as depicted in Figure 3.2. As shown in Figure 3.2, the disturbance occurs at the point of the start of an event and lasts at 10 seconds, which is supported by the KE stored in rotating blades of the WTGs, called inertial control. A controller is activated, making the system stable for up to 30 seconds, called primary control. After the 30s to several minutes, secondary control is activated, maintaining the system frequency to its prespecified value, and wind reserve power is used for this control action [8]. WTG’s inertia emulation and fast power reserve inertia support are studied here. The PFC of the WTG system is analogous to the governor droop control of the synchronous generator (SG). In this method, a new frequency value is established by changing WTG output power according to the variations in the frequency. Here the system frequency decides the control action rather than the RoCoF of the de-loaded WTG system [9].

Figure 3.2. The time frame of frequency regulation schemes.

In [10], the authors proposed various wind technologies suitable for inertial frequency support and demonstrated their limitations in practical implementations. In [11], the authors suggested inertial and primary frequency control approaches in a variable speed WTG system. Three sets of controllers are designed to extract the KE stored in the hidden inertia of turbine blades. In [12], a new deloaded control mechanism of the WTG system is proposed for the system frequency regulation. A comparative analysis between the SG and DFIG is conducted to describe the superiority of the suggested control

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technique. In [13], a 20% penetrated WE system is added to the 2-area conventional power system. The advantage of fast response of the power electronically interfaced WTG system rather than conventional system is taken to design the frequency regulation scheme. Early frequency support is achieved by proper communication between WTG and the conventional system. The authors presented a control mechanism for WTG operating under high and low 𝑉 at optimal power acquisition and delivery of stored KE for system frequency support without a negative power spike [14]. In [15], a frequency droop scheme is presented for primary frequency control by controlling the pitch angle of WTG to provide power reserve at all operating regions. In [16], a novel technique to enhance the KE discharge capability of the WTG system by incorporating frequency deviation and derivative of frequency deviation in the torque control loop is presented. In [17], a particle swarm optimization (PSO)-tuned fuzzy logic technique is used to solve the load frequency control (LFC) issue. Furthermore, [18] proposed an inertia control and primary control scheme to enhance the power reserve capacity of the WTG system at the de-loaded mechanism. In [19], the primary response capability of the WTG system is improved by continuously adjusting the droop coefficients under various wind velocities. In [20, 21], a fixed gain control method is proposed to ensure an improved frequency response. But it causes over-deceleration of the rotor during low V and wastes the KE of the rotor during high 𝑉. It also causes a drop in secondary frequency due to the reduction of WTG power. In [22], a novel modeling approach for WTG farms through the automatic generation control (AGC) scheme. The traditional wind farm model works under the maximum power point tracking (MPPT). Still, the author developed a set point control approach to control pitch angle under various operating conditions. In [23], a technique for coordinating a combined heat power plant and a wind plant is proposed to suppress the LFC issue in large wind penetrations. In [24], a control and coordinated de-loading technique for real power control of the WTG system are suggested to contribute to the mitigation of the LFC issue. In [25], a symbiotic organisms-based double derivative proportional-integral-derivative (PID) controller is proposed for the reverse osmosis (RO) desalination method. A comparative analysis is carried out in terms of time-domain specifications to show the superiority of the proposed method with recently developed optimization techniques. In [26], multivariable teaching and learning optimization-based PID control technique is proposed for the RO desalination plant. The integral squared error (ISE) is considered an objective function in this work. In [27], a whale optimization-based two PID controllers

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are designed for the RO flux and conductivity of the plant. The ISE error is used as a problem formation objective function.

Wind Energy Conversion System (WECS) The WECS is used to convert the moment of WE into mechanical power. Again, mechanical energy is converted into electrical energy by wind turbines (WTs). The significant components of WECS are pitch angle control associated with the WT system, wind generator, controllers associated with machine side and grid side power electronic converters, DC link capacitor, MPPT controller, a transformer which isolates the converter and LFC [28]. The above-described WECS is responsible for converting KE to mechanical energy and mechanical to electrical energy which is considered here for the study of LFC [29]. The PEI is responsible for the operation of a generator during variable wind speed and control of the grid current, i.e., real and quadrature power delivered to the system [30]. The AC output of a generator is converted into DC with an insulated gate bipolar transistor (IGBT)-based bridge rectifier. The MPPT controller ensures the optimum rotor speed from the cut-in wind speed to the rated wind speed region to obtain peak power. The pitch angle controller regulates wind generator rated power at and above rated wind speed situations. The architecture of the WECS is shown in Figure 3.3.

Load Side Converter (LSC)

Machine Side Converter (MSC)

Transformer

ωt

Pitch Angle Controller

Wind Speed

Figure 3.3. Architecture of a WECS.

MPPT Controller

Grid/Load Voltage & Output Power

Duty Cycle

Grid/ Loads

Vdc

Duty Cycle

Generated Power/Voltage

Tm

Pitch angle

Wind Turbine

DC Link Capacitor

Wind Generator

MSC/LSC Controller

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Dynamics of WTG The power captured by the turbine blades depends upon the capacity of air mass flow at a particular wind speed (V) and air density through an area, as defined in equation (3.1). The ratio of generated mechanical power to the turbine rotor speed is called mechanical torque, as defined in equation (3.2) [31]. 1

𝑃𝑚 =

𝐶 . 𝜌. 𝐴. 𝑉 2 𝑝

𝑇𝑚 =

𝑃𝑚 𝜔𝑚

=

3

=

1

𝐶 (𝛽, 𝜆). 𝜌. 𝜋𝑅 2 𝑝

2

. 𝑉3

(3.1)

1 .𝐶 .𝜌.𝐴.𝑉𝑤3 2 𝑝

(3.2)

𝜔𝑚

where 𝐶𝑝 is the WT breadth coefficient, air density is 𝜌, radius is R, swept area is A of the WTG system, and 𝜔𝑚 is the angular speed of the WT. The breadth coefficient of WT is the combined function of the blade pitch angle (β) and tip speed ratio (λ) [32] and is stated as follows: 𝐶𝑝 (𝛽, 𝜆) = 𝑘1 (𝑘2 1 𝜆𝑖

=

1 𝜆+0.08𝛽



1 𝜆𝑖

1 1+𝛽3

− 𝑘3 𝛽 − 𝑘4 𝛽 𝑘5 − 𝑘6 ) 𝑒𝑥𝑝

1 𝜆𝑖

(−𝑘7∙ )

(3.3) (3.4)

where 𝑘1 to 𝑘7 are the constants of WT for constant and adjustable speeds. Numerical values of the same are given in [33]. The λ is described as: 𝜆=

𝑅×𝜔𝑚 𝑉

(3.5)

Equation (3.1) clarifies that the power generated from the WTG system mainly depends upon the V and C; the rest of the parameters are constantly specified by the manufacturer. Wind speed (V) is an uncontrolled quantity; hence the power output of WTG system is only the function of Cp. The Cp is function of 𝜆 and 𝛽. Here, λ directly depends upon the rotor speed. The rotorspeed regulation is helpful for obtaining the wind peak power below its rated values. The maximum efficiency of WT is obtained at a minimum pitch angle, i.e., zero 𝛽, as shown in Figure 3.4. In Figure 3.4 [34], the variation of Cp is observed concerning the 𝜆 under different 𝛽. Figure 3.5 describes the variation

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of turbine output power concerning the turbine speed under various 𝑉. The optimal power curve is also obtained at respective rotor speeds.

Figure 3.4. Cp vs. 𝜆 at different 𝛽 [34].

Figure 3.5. Power speed characteristics of WT [34].

Operating Regions and MPPT Used for WECS There are three control schemes of the WT system, viz. Yaw control, 𝛽 control and 𝜆 control. Among these fundamental schemes, pitch and tip speed ratio (TSR) controls are electrical control, and yaw control is mechanical. A general control layout of various operating quantities such as pitch angle, output power, and turbine speed is represented in Figure 3.6(a) [35]. The points P and Q represent the below cut-in 𝑉 region. In this area, the 𝛽 is 90, and no blade is undergoing wind, so there is no turbine rotation, as well as no power is produced from WT system. The points Q and R represent the area between the cut-in 𝑉 to-rated 𝑉. In this area, called the MPPT region, the 𝛽 is made to zero

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to hit the maximum amount of wind energy to the blades to get peak power from the available V. Here, the obtained power and turbine speed increases linearly and tries to reach their rated values. The points R and S represent the rated 𝑉 area. In this area, the available 𝑉 is more than the rated 𝑉, which causes the generated power to be more than cubic power. A 𝛽 control mechanism is used to maintain the wind power at the rated value instead of the very high power generated due to a high V by gradually increasing the pitch angle from zero to certain value. After this region, a cut-out wind speed region is the same as the first region with a pitch angle of 90°. Figure 3.6(b) demonstrates the power versus V curves based on various operating conditions. The role of the parking mode is to avoid the motoring operation by making the pitch angle high. Otherwise, a wind generator acts as a motor and draws power from the grid system.

(a)

(b) Figure 3.6. (a) General layout of WT control strategy (b) power vs. 𝑉 curves under various operating regions.

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Tip-Speed-Ratio (TSR) Algorithm TSR algorithm maintains a fixed-optimum value of the 𝜆 at which power obtained from the WT is maximized by governing the rotor speed of the WTG system [36]. To ensure the peak attainable power, this MPPT algorithm needs the measured velocity of the wind and the rotational speed of an AC generator to get the optimum value of TSR (𝜆𝑜𝑝𝑡 ) of the WT. The optimum value of the 𝑜𝑝𝑡

turbine-rotor speed of an AC generator is determined from [14] as 𝜔𝑚 = 𝜆𝑜𝑝𝑡 × 𝑉⁄𝑅. In this algorithm, a comparison is made between optimum and actual rotational speeds. The difference, i.e., an error, is fed as input to the controller, which changes the speed of the AC generator and reduces the error. In this region of operation, the power characteristics of WT are like a hill for the generator speed. The output power is the only function of Cp; the rest of the parameters are constants. Here Cp is the only function of λ (β = 0). TSR algorithm has the merits of quick response and high efficiency. Turbine

β

Gearbox

ωt R

λ

Cp (β, λ)

1 G

V

V

0.5*Cp (β, λ)*1.22*A* V

3

Pm

1

Fast Shaft

Tm

ωt

Tem

1 G

1 j.s + d

ωm

* Tem

ωm

1 G

R λopt

P (Cp max)

MPPT Control

* . .

1 G

Figure 3.7. Power extraction of WECS [34].

In contrast, its drawback is the need for an efficient anemometer to measure V, making the system costly specifically for small-size WECS [37]. The λ control method was employed between the region Vcut-in and Vrated to extract the maximum available power from the WTG system. Here the value of β is maintained to be zero. In this case, the breadth coefficient is the function of λ only. Irrespective of V, the value of λ is maintained constant, making Cp

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optimum to obtain the maximum power. Figure 3.7 describes the maximum wind power extraction scheme [34]. Here j is the moment of inertia in kg.m2, d is the damping coefficient in Nm.s, and G is the gain multiplier. Both ∗ electromagnetic reference (𝑇𝑒𝑚 ) and actual reference (𝑇𝑒𝑚 ) torques are equal for all generated power to satisfy the MPPT condition. Start

Measure V,

ωm

Caculate Tw, Tb

No

No If Tw-Tb =0

If Tw-Tb >0

Yes No change

Yes Increment in duty cycle

decrement in duty cycle

Return

Figure 3.8. Flowchart of TSR algorithm.

The TSR algorithm mainly depends upon the two time periods called Tw and Tb as shown in Figure 3.8. The Tw is defined as the time taken by the disturbed wind to re-establish itself. The time taken by the next blade to reach the location of the preceding blade is called Tb. Both parameters Tw and Tb are measured in seconds. If Tw < Tb, then the wind speed is low, and the turbine rotates at a very low speed. Hence, most wind escapes between the blade’s gap, and reducing the converter’s duty cycle. If Tw > Tb, then the wind speed is very high, and the turbine rotates at a very high speed. Hence, the air molecules never find a gap between the blades to escape, which increases the converter’s duty cycle. No peak power will be obtained in the two conditions mentioned above. The maximum power will be obtained at Tw ≅ Tb. 𝑇𝑤 = 𝑆⁄𝑉 where S is the length of the disturbed and incoming V, respectively.

(3.6)

An Extended Study of Frequency-Supported Wind Energy …

𝑇𝑏 = 2п⁄𝑛𝜔𝑚

85

(3.7)

where n is the no. of blades, 𝜔𝑚 is the angular speed rad/s.

Result and Discussion A 750-kW wind-powered microgrid system is modelled, including a diesel generator and battery storage system. The aerodynamics and detailed parameters of WTG are taken from [38]. This WTG system has a pitch angle control [39], and a small signal model has a unity gain and time constant of 1.5s. The diesel generator set has a governor and a turbine with unity gains and time constants of 0.08s and 0.4s, respectively [40, 41]. The microgrid parameters are as follows: speed regulation is 2%, and generator and load time constants are 0.2s and 0.015s, respectively [42, 43]. The transfer function model representation of the system under investigation is represented in Figure 3.9.

Figure 3.9. Transfer function model representation of the system under investigation.

The gains of the PID controller are optimized with the grey wolf optimization (GWO) technique with system constraints [44], and the pitch angle PID controller is tuned with the trial-and-error method. Grey wolves are preying animals that hunt other animals. Their hunting mechanism impersonates this algorithm. The grey wolves are classified based on their

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social hierarchy: alpha, beta, delta, and omega. The alpha grey wolf is the principle, decision-maker, and commander of the team. According to their hierarchy, they follow the instructions, and the omega grey wolf occupies the least position. There are four steps involved in their hunting process: 1) tracking and chasing, 2) perusing and encircling, 3) hunting, and 4) exploitation of the prey. The grey wolves’ strength is that they live in packs, humble and loyal to each other. This study considers the integral of time multiplied by absolute error (ITAE) as a fitness function (J). 𝑡

𝐽 = ∫0 (|𝛥𝑓|). 𝑡𝑑𝑡

(3.8)

where t = simulation time (s), Δf = frequency deviation. Minimize J with the following constraints, 𝐾𝑝𝑚𝑖𝑛 ≤ 𝐾𝑝 ≤ 𝐾𝑝𝑚𝑎𝑥 , 𝐾𝑖𝑚𝑖𝑛 ≤ 𝐾𝑖 ≤ 𝐾𝑖𝑚𝑎𝑥 , 𝐾𝑑𝑚𝑖𝑛 ≤ 𝐾𝑑 ≤ 𝐾𝑑𝑚𝑎𝑥 , where Kp, Ki, and Kd are PID parameters. The system model under investigation is shown in Figure 3.9 and is developed in MATLAB/Simulink. The system dynamics, evaluated under different scenarios, are shown in this section. Here, the source side uncertainties, such as fixed V (Scenario 1) and adjustable V (Scenario 2), are taken for the simulation studies. Relevant data used in this study to conduct the simulation work is given in Table 3.1. Table 3.1. System parameters used in the simulation System Microgrid DEG, Battery Wind

Parameter values M = 0.2, D = 0.015 Tg = 0.08s, Kg =1, Tt = 0.4s, Kt =1, Rd = 2, KB = 1/300, TB = 0.1s Pm = 750 kw, ρ = 1.225 kg.m3, A = 1648 m2, k1 = -0.6175, k2 = 116, k3 = 0.4, k4 = 0, k5 = 5, k6 = 21, k7 = 0.1405, j = 0.21 kg.m2, d = 0.0001 NM. s/rad, KWTG = 1, TWTG = 1.5s

Scenario 1 In this scenario, source-side uncertainty is considered. A fixed V of 12 m/s is applied to the system to assess the dynamic performance of the secondary controller and pitch controller of the system under study. The normal load of the system is 0.75 pu, the diesel generator is 0.5 pu, and the wind contribution

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is 0.75 pu. The battery storage system removes the sudden power mismatch among generators and load. As shown in Figure 3.10(a), the frequency variations are within the acceptable limits. The minimum value is 49.41 Hz, and the maximum value is 50.24 Hz. The power contribution of various generating units is represented in Figure 3.10(b). The 𝛽 is almost stabilized at a minimum value of zero. The 𝜆 is maintained constant at 9.775. The corresponding power coefficient value is 0.3736. The maximum limit of the breadth coefficient is 0.59. The angular turbine speed is 5.325 revolutions per second. Figures 3.10(c)-(f) represent the 𝛽, 𝜆, CP, and turbine speed, respectively. In this case, the obtained responses reveal the support of a wind generator in the frequency control scheme. Here, load and V are assumed to be constant. Figure 3.10(c) shows that the 𝛽 is constant in the steady-state operation to supply the active power of the wind system.

(a)

(b)

(c)

(d) Figure 3.10. (Continued).

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Maloth Ramesh, Anil Kumar Yadav, Rajan Kumar et al.

(e)

(f) Figure 3.10. Scenario-1 dynamic responses (a) frequency (b) power (c) β (d) λ (e) Cp (f) turbine speed.

Scenario 2 In this scenario, a variable V, as shown in Figure 3.11(a), is applied to the system to assess the dynamic performance of the secondary controller and pitch controller of the system. As represented in Figure 3.11(a), the wind speed is 8 m/s from 0s to 20s, 10 m/s from 20s to 40s and slightly higher than 12 m/s from 40s to 100s. The baseload of the system is 0.75 pu, and the wind contribution is 0.75 pu. The battery storage system suppresses the sudden power mismatch among generators and load. As shown in Figure 3.11(b), the frequency variations are within the acceptable limits. The minimum value is 49.14 Hz, and the maximum value is 50.85 Hz. The power contribution of various generating units is shown in Figure 3.11(c). The wind generator contribution is approximately 0.2 pu from 0s to 20s time interval. Similarly, from the 20s to 40s time interval, the wind contribution is nearly 0.38 pu, and, finally, 0.75 pu from 40s to 100s time interval. The pitch angle is almost stabilized at zero. The 𝜆 is maintained fixed at 9.775. The corresponding power coefficient value is 0.3736. The maximum limit of the breadth coefficient is 0.59. The angular turbine speed is varied according to the V variations. Figures 3.11(d)-(g) represent the pitch angle, turbine speed, tipspeed ratio, and power coefficient. Here, the load is assumed as constant, but the V is a variable quantity. Due to fluctuations in the V, the system frequency fluctuates, which leads to the system being unstable. Hence, the 𝛽 controller

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generates suitable signals for the wind system to participate in LFC action to stabilize the system frequency.

(a)

(b)

(c)

(d)

(e) Figure 3.11. (Continued).

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(f)

(g) Figure 3.11. Scenario-2 dynamic responses (a) wind variation (b) f (c) power (d) β (e) ωt (f) λ (g) Cp.

Conclusion A secondary frequency supported-WECS has been modelled and investigated in this chapter. The detailed dynamic model of the WECS, including aerodynamics and pitch angle control, has been studied. The obtained simulation results have revealed that the performance of WECS has been significantly improved with the system frequency support. The GWO tuned PID secondary control strategy has been proposed in this work. This study reveals that the wind-supported LFC scheme has improved the system’s transient response considerably, and resulted in a cost-effective solution instead of using additional frequency support-distributed power generating units. Under the source side uncertainties such as wind speed variabilities viz. constant wind speed and variable wind speed scenarios, the frequency fluctuations are within the grid specified limits. The controller design and comparative analysis with recently developed techniques are one of the future scopes of this work.

Acknowledgments This work is supported by H. P. Council for Science, Technology & Environment (HIMCOSTE), Shimla (H. P.), India, under HP Specific

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Research & Development Projects vide sanction order no. STC/F(8)-2(R&D 20-21)-123.

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Alaboudy, Ali H. Kasem, Ahmed A. Daoud, Sobhy S. Desouky, and Ahmed A. Salem. Converter controls and flicker study of PMSG-based grid connected wind turbines. Ain Shams Engineering Journal, 4, no. 1, 2013, pp: 75-91. Urtasun, Andoni, Pablo Sanchis, Idoia San Martín, Jesús López, and Luis Marroyo. Modeling of small wind turbines based on PMSG with diode bridge for sensorless maximum power tracking. Renewable Energy, 55, 2013, pp:138-149. Lydia, M., A. Immanuel Selvakumar, S. Suresh Kumar, and G. Edwin Prem Kumar. Advanced algorithms for wind turbine power curve modeling. IEEE Transactions on sustainable energy, 4, no. 3, 2013, pp: 827-835. Petković, Dalibor, ŽarkoĆojbašič, and VlastimirNikolić. Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renewable and Sustainable Energy Reviews, 28, 2013, pp:191-195. Kumar, Dipesh, and Kalyan Chatterjee. A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 55, 2016, pp: 957-970. Ramesh Maloth and Anil Kumar Yadav. Wind Contributed Load Frequency Control Scheme for Standalone Microgrid Using Grey Wolf Optimization. IEEE Delhi Section Conference DELCON (2022), 1-6. DOI: https://doi.org.10.1109/DELCON 54057.2022.9752690. Li, Hui, K. L. Shi, and P. G. McLaren. Neural-network-based sensorless maximum wind energy capture with compensated power coefficient. IEEE Transactions on Industry Applications, 41, no. 6, 2005, pp:1548-1556. Abdullah, Majid A., A. H. M. Yatim, Chee Wei Tan, and Rahman Saidur. A review of maximum power point tracking algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 16, no. 5, 2012, pp: 3220-3227. Nasiri, M., J. Milimonfared, and S. H. Fathi. Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines. Energy Conversion and Management, 86, 2014, pp: 892-900. Khamies, Mohamed, Gaber Magdy, Mohamed Ebeed Hussein, Fahd A. Banakhr, and Salah Kamel. An efficient control strategy for enhancing frequency stability of multi-area power system considering high wind energy penetration. IEEE Access, 8, 2020, pp: 140062-140078. Van, Tan Luong, Thanh Hai Nguyen, and Dong-Choon Lee. Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems. IEEE Transactions on Energy Conversion, 30, no. 2, 2015, pp: 578-587. Ramesh Maloth, Anil Kumar Yadav, and Pawan Kumar Pathak. Intelligent adaptive LFC via power flow management of integrated standalone micro-grid system. ISA Transactions, 112, 2021, pp: 234-250. Ramesh Maloth, Anil Kumar Yadav, and Pawan Kumar Pathak. An extensive review on load frequency control of solar-wind based hybrid renewable energy systems. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021, pp: 1-25. Pathak, Pawan Kumar, and Anil Kumar Yadav. Design of battery charging circuit through intelligent MPPT using SPV system. Solar Energy, 178, 2019, pp: 79-89.

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Chapter 4

RERNN-BCMO-Based Load Frequency Control in Multi-Area Power Systems Using Hybrid Renewable Energy Sources Arshad Mohammed* and R. Srinu Naik Andhra University, Visakhapatnam, India

Abstract This chapter deals with the load frequency control (LFC) by a novel dual tuned strategy for a conglomerated power system (PS) comprising renewable energy sources (RES). The hybrid strategy is the joint execution of Recalling Enhanced Recurrent Neural-Network (RE-RNN) and Balancing Composite Motion Optimization (BCM-O); hence it is named as RE-RNNBCM-O technique. This chapter presents the analysis of the LFC signal, which is a significant part of reducing frequency deviation (FD), area control error (ACE), and tie line (TL) power flow. The proposed method covers three area power systems such as wind, thermal, and hydro; whose system performance is improved by implementing a proportional-integral-derivative (PID) controller. The said PID tuning with the help of the RERNN-BCMO technique yields an excellent LFC. The benefits of such tuning are not limited, but are high speed with high reliable output and less complexity at increased predicting capacity. Simulation is performed using MATLAB/Simulink platform and results are compared with existing methods. The results obtained are promising and effective compared to others in all major aspects. *

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Keywords: balancing composite motion optimization, load frequency control, proportional-integral-derivative controller, recalling enhanced recurrent neural network, tie-line power deviation

Introduction Modern power systems (PS) have increased the capacity of the electric grid and the total number of connections, making PS complex. Therefore, these PSs are categorized into a group of control areas connected to others via tie lines (TLs). The speed of the synchronous machine [1] governs the power handling capability. Frequency variation [2–4] affects the load power and generation aspects. Therefore, the LFC plays a vital role in the control area to keep the frequency variation from minimum to zero after an interruption and maintaining the tie-line power within limits in each region [5–7]. For reliable conditions during frequency and power exchange among areas in various conditions [8], load frequency control (LFC) is of utmost priority. The main aim of LFC is to reduce the frequency loss as well as tie-line loss among control areas. Efforts are made to implement control techniques for LFC in two major aspects. First aspect provides different advanced methods to implement the best controllers for LFC in power system. For example, model predictive control (MPC), H8 and µ-synthesis, fuzzy logic, and sliding mode strategy were used for the LFC problem. Second aspect is a proportionalintegral (PI) controller that remained the engineer's ideal choice due to a reliable control system. The constant power transmission level triggers the power system functions to be good and reliable [9]. The unbalance condition between supply and load degrades the power system performance and complicates the control operations [10–14]. In order to achieve an enhanced system performance with better constancy function under lower interruptions in the system, the variations of structure, system size, and implementation or operation difficulties become a significant problem for interconnected power systems [15–19]. The frequency and load demand should have a specific limit at any time to achieve reliable power delivery and enhance the power system performance [20]. In order to improve the performance of the system, complex mathematical equations are used. Several investigations were carried out on the LFC of the conglomerated power system. Some of the works are reviewed as follows: Lu et al. [21] presented a robust controller tuned with constrained population extremal optimization of multi-area interconnected PS. In order to

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demonstrate the effectiveness of the proposed controller, three different twoarea interconnected power systems are used. Hasanien et al. [22] presented Salp swarm algorithm (SSA) for fine-tuning the PID controller gain of LFC having multi-area hybrid nonlinear PS. Fathy et al. [23] introduced an optimal fuzzy PID controller for the LFC of multi-interconnected areas via the Mine Blast Algorithm (MBA). The proposed MBA offers the optimal gains of controller input/output scaling factors to achieve the required performance with acceptable specifications and low time consumption. Sonker et al. [24] presented dual loop internal model control (IMC) scheme for LFC of multi-area multi-sources PS wherein, single-area and multi-area reheated thermal power system (TPS) systems are considered for validation of the scheme. Sahu et al. [25] demonstrated an improved Salpswarm optimized type-II fuzzy PID controller in LFC in an isolated two-area AC micro-grid system. Latif et al. [26] reviewed the contemporary controllers implementing soft computing models for planned load frequency supervision of single/multiple areas having renewable energy-based PS. The target was to present an evaluation of various controllers employed in traditional as well as renewable energy-based PS for load frequency management. Sun et al. [27] developed a robust H∞ LFC of multi-area PS with time delay via the sliding mode control method. A new sliding surface function was built to ensure rapid response with robust performance deeming the random disturbances caused by the incorporation of renewable energies. The SMC rule was structured to assure the capability of sliding surfaces in a finite-time interval.

Multi-Area Power System for LFC The implementation diagram of multi-area power system is shown in Figure 4.1 wherein, three control areas are considered for LFC operation. Each area consists of power generating sources such as wind, thermal, and hydro. The first area consists of wind power system. The second area consists of thermal power system and the third area consists of hydropower system. The power generating sources of each area is interconnected with separate governor, turbine, and re-heater unit. The main objective of this work is to control the load frequency of such system through the minimization of deviations such as frequency deviation, TL power deviation and area control error (ACE). The said deviations of every area are controlled with the help of PID controller. In this work, a novel hybrid strategy with PID controller is implemented, in order to achieve the

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optimal result of LFC in multi-area PS. The proposed method is the joint execution of Recalling Enhanced Recurrent Neural Network (RERNN) and Balancing Composite Motion Optimization (BCMO) algorithms. The area participation factor (APF) is an important part of LFC analysis. The APF of every Ith control area can be calculated [28] by (4.1)

Figure 4.1. Implementation diagram of multi-area power system.

The total load demand (

) of every Ith area can be expressed as (4.2)

RERNN-BCMO-Based Load Frequency Control …

where,

99

signifies total local contracted demand, which can be expressed

as (4.3) and overall un-contracted demand (

) is (4.4)

The listed variation of tie-line power flow ( represented as

) at the steady state is

(4.5)

(4.6)

The net TL power error can be calculated as (4.7) The generation of mechanical power ( system is computed by

) for every generating

(4.8) for

; and

In the first area, the wind power system is considered. The transfer function of wind system can be expressed as (4.9)

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where is the gain of the wind turbine (WT) system. In second area, the transfer function of thermal system can be expressed as (4.10) where is the gain of the thermal system. In third area, the transfer function of hydro system can be expressed as (4.11) where

is the gain of the hydro system.

Generally, the error is generated in all control areas during the generation and transmission process, which is expressed as (4.12) where represent the area number, represent the change of tie-line th power in I area, represents the frequency bias factor and represents th frequency deviation of the I area. PID controller helps to overcome the error from generating sources like wind, thermal and hydro through the control operation. The transfer function of PID controller is expressed [29] as (4.13) where

;

The wind power system generates the power depending on wind speed (WS). The outcome of the wind turbine is described as

PTW T

0,  P (v ),  n = Pra   Pra , 0,

v  vcin vcin  v  v N v N  v  vcout v  vout

(4.14)

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(4.15) where rated power output of wind turbine is specified as

, power output

of wind turbine is specified as and rated power of single WT is indicated as . Further, wind speed (WS), rated WS, cut-in WS, cut-out WS are indicated as , , and respectively. The thermal power system generates the electricity power from the heat energy. The modeling of thermal system can be expressed [30] as (4.16) where, denotes turbine’s time constant, denotes thermal time constant and, signifies the thermal gain. The modeling of the thermal governor can be expressed as (4.17) where

is the governor’s time constant.

The hydro power generation can be expressed [31] as (4.18) where is the height of the water, is the rate of flow in m3, is the acceleration due to gravity in m/s2 and is the efficiency constant which varies between value 0 and 1.

Problem Formulation In this study, the PID controller is utilized meant for LFC operation. During the implementation of the LFC system, the selection of objective function is an important one for the possible improvement of PS dynamic responses. Basically, there are several integral-error criteria such as (i) Integral Squared Error (ISE) (ii) Integral Absolute Error (IAE) (iii) Integral Time-weighted Squared Error (ITSE) (iv) Integral Time weighted Absolute Error (ITAE)

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which are used as the objective functions. These objective functions are implemented as single or multiple objective functions. (4.19) (4.20) (4.21) (4.22) Subject to limits of PID gains, expressed as (4.23) (4.24) where

and

represent minimum and maximum limits of PID

controller, respectively; and imply minimum and maximum limits of the filter co-efficient. A hybrid RERNN-BCMO technique is utilized to achieve the optimal values of control parameters under various operating conditions of PS.

Proposed Approach The RERNN-BCMO technique has been employed in this study to control the load frequency in a conglomerated PS. The hybrid strategy of balancing composite motion optimization and recalling enhanced-recurrent neural network aids to reduce the frequency deviation with area TL power deviation. The flowchart of RERNN-BCMO is illustrated in Figure 4.2.

Recalling-Enhanced Recurrent Neural Network (RERNN) The RERNN is an artificial neural network that uses the radial function implemented in mathematical modeling. Elman recurrent neural network has

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three layers, but the recalling enhanced neural network consist of six layers and is incorporated with selective memory property [32]. The basic structure of RERNN is depicted in Figure 4.3. The RERNN layers are input, state, memory, sum, hidden, delay, and output layers. The input node accepts the input and output of hidden layer with a delay function. The state layer accepts input that can be given to the memory layer. The memory layer accepts two inputs i.e., the preceding outcome of the sum layer and the existing outcome of the state layer. The major function of the memory layer is determined by the size of the previous sum layer’s information transmitted to the next stage. The sum layer adds the information of present input, final recurrent hidden outcome, and memory layer outcome. The hidden layer outcomes provide the final probabilistic value at the output layer. The delay layer propagates back to the present hidden layer output. The RERNN has the following steps:

Figure 4.2. Flowchart of RERNN-BCMO algorithm.

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Figure 4.3. Structure of the RERNN.

Step 1: Initialization In this step, the input parameters like frequency, power, and the number of iterations are initialized. Also initialize the number of nodes, weight vectors and the hidden node number. Step 2: Random Generation After the initialization process, the input vectors are randomly generated. Here, the input parameters are also considered as LFC parameters like wind power, thermal power, hydro power, and frequency which are generated in a random manner. Step 3: Fitness Function Evaluate the fitness function for all iterations, calculated as (4.25) where F denotes the fitness function, MIN stands for minimization and E denotes the error signal.

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Step 4: Check the Iteration The data is processed if the iteration is less than the maximum iteration and aborts the process in other cases. Step 5: Find the Learning Rate The learning rate is determined by following condition expressed as ;

(4.26)

Step 6: Calculation of New Weight The new weight is estimated by using following expression (4.27)

Step 7: Calculate the Direction The learning process direction is calculated by using the following expression (4.28) ;

(4.29)

Step 8: Termination Repeat the algorithm from step 3 until the objective condition is met. Once the condition is achieved, terminate the step.

Processing Steps of Balancing Composite Motion Optimization (BCMO) The BCMO is a new population-based optimization algorithm utilized to balance composite motion individual characteristics at the solution space. Here, candidate solutions (CSs) have been customarily developed at the space of solution S, and then few movement techniques are implemented to identify the optimal global region. The CSs have randomly created in S space at the starting stage, which evolves via productions to achieve the global optimal result via few heuristic conditions generally inspired by nature [33]. From a physical perspective, candidate solutions are treated as particles as well as

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heuristic conditions are composite in a physical space. This algorithm is best suited because the coordination of solutions on Cartesian space is synchronized with location of individuals in physical space without any encoding and decoding processes. In addition, the individual's changes and population can directly be identified by computing the individual's shift location gathered from the Cartesian space. The BCMO has the following steps:

Step 1: Initialization Initialize the solution space population distribution such that the upper and lower boundaries of Ith individuals and dimensional vector. Step 2: Random Generation After the initialization process, the initialized parameters are generated randomly by using the following equation (4.30) where,

and

represents the upper and lower boundaries of Ith

individuals. The dimensional vector is denoted as . Step 3: Fitness Function In this step the fitness function for all iterations is calculated, expressed as (4.31) where F denotes the fitness function, MIN stands for minimization and E denotes the error signal. Step 4: Finding Instant Global Point Initially, all the individuals are located in different regions. The instant global point can be determined using the following equation (4.32)

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Step 5: Selection In order to find the optimal solution, the individual with best objective function value is chosen to move on S space. Step 6: Updation After the selection process, the upper and lower boundaries in all iterations are updated. Step 7: Termination Once the objective condition is achieved, it will be terminated otherwise repeat the process from Step 3.

Result and Discussion This section summarizes the simulation results and presents discussions on LFC of a multi-area PS. Here, the RERNN-BCMO is proposed to reduce the ACE, frequency deviation and area TL power deviation. The simulation is carried out with the help of MATLAB/Simulink platform. The outcomes are compared with existing methods like particle swarm optimization (PSO), bacterial foraging optimization (BFO), salp swarm algorithm (SSA) and chaotic quasi oppositional chemical reaction optimization (CQOCRO).

Figure 4.4. Analysis of frequency deviation of (a) wind (b) thermal and (c) hydro systems.

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Figure 4.4 displays the analysis of frequency deviation of wind, thermal and hydro systems. An analysis of the frequency deviation of the wind system is presented in Fig. 4.4(a). Here the frequency deviation increases from 0.1 to 0.3 Hz and remains constant over a time period of 50 to 500 sec. An analysis of the frequency deviation of the thermal system is presented in Fig. 4.4(b). Here the frequency deviation increases from 0.1 to 0.29 Hz and remains constant over a time period of 50 to 500 sec. Further, an analysis of the frequency deviation of the hydro system is presented in Fig. 4.4(c). Here the frequency deviation increases from 0.1 to 0.29 Hz and remains constant over a time period of 50 to 500 sec. Figure 4.5 shows a comparison of the frequency deviations of wind, thermal and hydro systems for different approaches. In Figure 4.5(a), the proposed method (RERNN-BCMO) results in a maximum of 0.29Hz and the existing PSO, BFO, SSA and CQOCRO result in a maximum of 0.08Hz at 10sec, 0.18Hz at 0sec, 0.07Hz at 5 sec and 0.17 Hz at 0sec and maintains constant over a time period of 200sec to 500sec. In Figure 4.5(b), RERNN-BCMO results in a maximum of 0.29Hz and the existing PSO, BFO, SSA and CQOCRO result in a maximum of 0.05Hz at 10sec, 0.03Hz at 35sec, 0.12Hz at 0 sec and 0.2 Hz at 0sec. Further, in Figure 4.5(c), RERNNBCMO results in a maximum of 0.29Hz and the existing PSO, BFO, SSA and CQOCRO result in a maximum of 0.09Hz at 10sec, 0.03Hz at 35sec, 0.12Hz at 0 sec and 0.1 Hz at 0sec.

Figure 4.5. Comparison of the frequency deviations of (a) wind (b) thermal and (c) hydro systems for different approaches.

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Figure 4.6 depicts the tie line power and frequency deviation of wind, thermal and hydro systems. In Figure 4.6(a), the tie line power of wind system reaches the maximum of 0.02MW at 0sec and maintains constant over a time period of 50sec to 500sec. Also, the frequency deviation of wind system reaches the maximum of 0.4Hz at 0sec and maintains constant from the time of 80sec to 500sec. In Figure 4.6(b), the tie line power of the thermal system reaches the maximum of 0.05MW at 0sec and maintains constant over the duration 100sec to 500sec. Also, the frequency deviation of the thermal system reaches the maximum of 0.5Hz at 0sec and maintains constant over the duration 50sec to 500sec. Furthermore, in Figure 4.6(c), the tie line power of the hydro system reaches the maximum of 0.04MW at 0sec and maintains constant over the duration 40sec to 500sec. Also, the frequency deviation of the thermal system reaches the maximum 0.05Hz at 20sec. It maintains constant over the duration 50sec to 500sec.

Figure 4.6. Analysis of tie line power of (a) wind (b) thermal and (c) hydro systems.

Figure 4.7 portrays a comparison of the tie line powers of wind, thermal and hydro systems for different approaches. In Figure 4.7(a), the tie line power in case of RERNN-BCMO reaches the maximum of 0.02MW and in case of existing PSO, BFO, SSA and CQOCRO; it reaches the maximum of 0.014MW at 10sec, 0.002MW at 35sec, 0.015MW at 10sec and 0.017MW at 0sec. In Figure 4.7(b), the tie line power in case of RERNN-BCMO reaches the

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maximum of 0.06MW and in case of existing PSO, BFO, SSA and CQOCRO; it reaches the maximum of 0.01MW at 25sec, 0.02MW at 0sec, 0.07MW at 0sec and 0.06MW at 0sec. Moreover, in Figure 4.7(c), the tie line power in case of RERNN-BCMO reaches the maximum of 0.05MW and in case of existing PSO, BFO, SSA and CQOCRO; it reaches the maximum of 0.018MW at 10sec, 0.005MW at 35sec, 0.05MW at 0sec and 0.04MW at 0sec.

Figure 4.7. Comparison of the tie line powers of (a) wind (b) thermal and (c) hydro systems for different approaches.

Figure 4.8 portrays the frequency deviation and tie line powers of wind, thermal and hydro systems. In Figure 4.8(a), the tie line power of wind system reaches the maximum 0.02MW at 0sec and maintains constant from the time of 50sec to 500sec. Also, the frequency deviation of wind system reaches the maximum of 0.4Hz at 0sec and maintains constant over the duration 80sec to 500sec. In Figure 4.8(b), the tie line power of thermal system reaches the maximum of 0.05MW at 0sec and maintains constant over the duration 100sec to 500sec. Also, the frequency deviation of thermal system reaches the maximum of 0.5Hz at 0sec and maintains constant over the duration 50sec to 500sec. In Figure 4.8(c), the tie line power of hydro system reaches the maximum of 0.04MW at 0sec and maintains constant over the duration 40sec to 500sec. Also, the frequency deviation of thermal system reaches the maximum of 0.05Hz at 20sec. It maintains constant over the duration 50sec to

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500sec. Figure 4.9 shows the analysis of ACE of wind, thermal and hydro systems. In Figure 4.9(a), the ACE of wind system reaches the maximum of 0.18 at 0sec. It maintains constant over the duration 50sec to 500sec. In Figure 4.9(b), the ACE of thermal system reaches the maximum value of 0.21 at 0sec. It maintains constant over the duration 50sec to 500sec. In Figure 4.9(c), the ACE of hydro system reaches the maximum of 0.12 at 0sec.

Figure 4.8. Analysis of frequency deviation and tie-line power of (a) wind (b) thermal and (c) hydro systems.

Figure 4.9. Analysis of ACE of (a) wind (b) thermal and (c) hydro systems.

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Figure 4.10 shows the analysis of the error for wind, thermal and hydro systems. In Figure 4.10(a), the error of wind system reaches the maximum of 0.08 at 0sec. It maintains constant over the duration 60sec to 500sec. In Figure 4.10(b), the error of thermal system reaches the maximum value of 0.1 at 0sec. It maintains constant over the duration 60sec to 500sec. In Figure 4.10(c), the error of hydro system reaches the maximum of 0.05 at 0sec. It maintains constant over the duration 100sec to 500sec. Figure 4.11 shows the analysis of ACE and error of wind, thermal and hydro systems. In Figure 4.11(a), the error of wind reaches the maximum 0.08 at 0sec. It maintains constant over the duration 50sec to 370sec. Also, the ACE of wind system reaches the maximum of 0.9 at 0sec. It maintains constant over the duration 50sec to 370sec. In Figure 4.11(b), the error of thermal system reaches the maximum of 0.1 at 0sec and maintains constant over the duration 50sec to 500sec. Also, the ACE of the thermal system reaches the maximum of 0.1 at 0sec. It maintains constant over the duration 50sec to 500sec. In Figure 4.11(c), the error of hydro system reaches the maximum of 0.05 at 0sec and maintains constant over the duration 70sec to 500sec. Also, the ACE of hydro system reaches the maximum of 0.04 at 20sec. It maintains constant over the duration 50sec to 500sec.

Figure 4.10. Analysis of errors–(a) wind (b) thermal (c) hydro systems.

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Figure 4.11. Analysis of ACE and error–(a) wind (b) thermal (c) hydro.

Table 4.1. Performance analysis of proposed technique compared to the other existing techniques Technique PSO BFO SSA CQOCRO RERNN-BCMO (Proposed)

Overshoot (%) 1.934 1.867 0.756 0.675 0

Settling time (sec) 1.765 1.435 0.768 0.534 0.0988

Rise time (sec) 1.934 1.867 0.976 0.764 0.076

Steady state error 0.996 0.965 0.875 0.632 0

Table 4.1 details the performance analysis of RERNN-BCMO compared to the other existing techniques. Here, the overshoots in case of existing techniques i.e., PSO, BFO, SSA and CQOCRO are 1.934, 1.867, 0.756 and 0.675; whereas, the proposed method results in no overshoot. The settling times in the case of existing techniques, PSO, BFO, SSA and CQOCRO are 1.765, 1.435, 0.768 and 0.534. But the settling time in the case of proposed technique is 0.0988. Further, the rise times in case of existing techniques are 1.934, 1.867, 0.976 and 0.764, respectively; whereas, the rise time in the case of the proposed technique is 0.076. In, the steady-state errors in case of existing techniques such as PSO, BFO, SSA and CQOCRO are 0.996, 0.965, 0.875 and 0.632; whereas, the proposed technique results in zero steady state error. The proposed technique results in no overshoot, lower settling and rise

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times and zero steady-state error. So, the proposed technique provides better performance compared to the other existing techniques studied here.

Conclusion In this chapter, a new hybrid method is implemented to control the load frequency of multi area power system. Frequency deviation, ACE and tie-line power deviation are minimized using RERNN-BCMO with PID controller. The presented method provides good and reliable results with a small number of iterations. This method is used to make the calculations easier and with less complexity. The RERNN-BCMO algorithm is implemented in MATLAB/ Simulink and the output is compared with existing methods of PSO, BFO, SSA and CQOCRO. From the comparative result, the proposed method results in low ACE, low frequency deviation and low tie-line power; thus, provide better performance than existing methods.

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Chapter 5

A Review on State-of-the-Art Wind Energy Conversion Systems and Associated Control Strategies for Normal and Fault Conditions Arika Singh1,2,*, Kirti Pal1 and Hemant Ahuja3 1Gautam

Buddha University, Greater Noida, India Group of Institutions, Ghaziabad, India 3Ajay Kumar Garg Engineering College, Ghaziabad, India 2KIET

Abstract The current status of variable speed wind turbine (WT) generators and the associated control configurations are reviewed in this chapter along with their comparison. This review mainly focuses on the four main electrical facets of wind energy conversion systems (WECS) viz. wind turbine generators, power electronic converter (PEC) control, gridintegration issues, and dynamic behavior of WECS. Three main issues of the WECS control i.e., optimal power control, power limitation during high wind conditions, and the fault ride through (FRT) capability are reviewed extensively. The vast majority of relevant literature is concerned with the control strategies for various generators used for geared variable speed WECS with horizontal-axis WT. A review of the machines with power outputs in the range of hundreds to thousands of kW rating is emphasized in this chapter. Review related to the impact of a particular type of WECS on the power system operation is also presented in detail.

*

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Keywords: doubly fed induction generator (DFIG), fault ride-through (FRT), permanent magnet synchronous generator (PMSG), renewable energy, squirrel cage induction generator (SCIG), wind energy conversion system (WECS)

Introduction Wind energy is considered the most popular and promising source of green energy, all around the world. Over the last two decades, commercial-scale electricity generation through wind is expanding exponentially. Though the concept of wind energy is centuries old, its commercialization on a large scale has happened in the last few years only [1]. The generation of electricity through wind is environmentally safe as well as economical. The wind resource is freely available and will never run out. The electricity production through wind, the designing and making of wind turbines, and their erection and operation are growing fast. The same is expected to grow further as the entire world is looking for sustainable and clean electricity generation. Wind turbines have now become cost-effective with power ratings up to 20MW. The turbines with larger blade lengths are now being developed to provide enhanced power capabilities and better control features. This altogether has resulted in a reduction of the electricity generation cost. Despite its growth and the positive considerations, there is one major issue that the wind industry is still facing and that is its variability. Its varying nature prohibits its utilization as a single main energy resource. Various surveys on wind energy potential from different geographic locations present contradictory figures and it is difficult to come to any conclusion based on these surveys. In general, many of the power grids, where wind penetration has increased to over 20%, clearly show that wind is not a very reliable resource especially due to its variable nature leading to problems of stability and meeting the demand. As the installed capacity of WTs is raising, it is of great importance to have a well-proven technology for WECS. Extensive research work is reported in the area of WT technologies. The importance of PECs in wind power generation is increasing steadily and variable speed wind generation has become impossible without power electronics. Different WECS configurations use different types of power converters and each system poses a variety of design goals. With the increasing installed capacity, the

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implementation of new standards has provided further impetus to the innovation of WECS for better efficiency, stability, and lower cost. Considerable research and innovation have been carried out for the gridconnected variable-speed WECS. Full-rated back-to-back converter-based WECS are gaining huge momentum due to great improvement in semiconductor devices and control in power converters. Substantial research has been carried out in the last decade for innovating different types of WECS [2]. Different topologies for power converters have been introduced. Variable speed WECS with DFIG, PMSG and SCIG are gaining importance as each concept has its own merits. A lot of research progress is being made in each of the WECS concepts. Also, a tremendous amount of progress is made in power quality and grid fault ride through (FRT) of these WECS. This chapter therefore provides a detailed discussion about the literature available for the control and FRT of WECS. The literature is reviewed for three major WECS dominant in the wind market.

State-of-the-Art WECS Since 1990, rapid development in the WECS configurations is seen with the varying design configurations and new generators. Based on the topology and electrical configuration, the WECS are majorly grouped into four types [3], as given in Table 5.1, namely (i) fixed speed WT (FSWT) concept based on squirrel cage induction generator (SCIG), referred as Type A (ii) variable slip (wound rotor) induction generator with limited control through rotor resistance or opti-slip, referred as Type B (iii) Type C–variable speed wind turbine (VSWT) concept using doubly fed induction generator with partially rated converters (iv) Type D–variable speed fully controlled using either an SCIG or a synchronous generator along with full rated converter (FRC). Literature shows that the interest in fixed speed WT has declined in the last decade. Market penetration of (Type B) opti-slip concept has also decreased in the last decade in favor of more striking variable speed concepts. The market trend shows that Type B concept is being pushed out of the market. Type C and D concepts are still dominant as the variable speed generators in these types have the ability to harness maximum power under a specific range of wind speeds via rotor speed adjustment. A grid-connected DFIG (Type C) is a worthy option with a vividly growing market as it offers full controllability, allows optimal power extraction and independent control of active/reactive power components through cascaded power converters at

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reduced ratings [4]. Type D concept can be equipped with either synchronous generator (either electrically excited synchronous generator (EESG) or a PMSG) or SCIG along with a converter cascade of full rating. Table 5.1. State-of-the-art WECS types WT Concepts FSWT Concept (Type A)

VSWT Concept (Type B) VSWT Concept (Type C and Type D)

Power Regulation Stall

Generator Type SCIG

Active Stall Pitch

SCIG

Pitch

WRIG

Pitch

DFIG SCIG PMSG EESG PMSG

SCIG

Drive Train Type Geared Drive Geared Drive Geared Drive Geared Drive Geared Drive Direct Drive

Gearbox

Speed

3 Stage

2 Speed

3 Stage

2 Speed

3 Stage

2 Speed

3 Stage

Opti-slip

3 Stage 3 Stage 1/2/3 Stage -

Variable Speed

In past few years, the performance of permanent magnets (PMs) have improved while its cost is reduced, making PMSG more attractive for direct driven (without using gear mechanisms) wind generators. PMSG offers advantages like no field loss and better power factor with enhanced efficiency as compared to other variable speed generators. A multiple pole PMSG has the advantage of operating at the lower speeds thereby eliminating the need of a gearbox. As a gearbox gathers weight, associates loss and is costly, a gearless operation using PM generator brings an efficient as well as robust solution with reduced maintenance [5]. With power electronics expenses coming down, SCIG-based WECS with FRCs are gaining popularity. Benefits of reduced cost, ease of availability and rugged construction, suited for harsh environments, make the squirrel cage generators a better choice for the wind systems [6, 7]. The theory behind the control of these generators and their implementation for the above-mentioned variable speed WECS has been presented in literature through mathematical modeling, simulations and hardware prototypes. Various performance indices have been used in the literature for comparing different WECS configurations, mainly based on cost per unit power output, efficiency, torque density, weight of active material, total length, volume and generator cost, outer diameter, cost of energy etc. [8,

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9]. Chatterjee et. al. [10] described a variable speed system using different types of generators and power electronic converters to maintain power quality, grid synchronization, DC link voltage control, pitch angle control and fault ride-through etc. The recent advancement of each and every topology is discussed. The study also provides the challenges of the prevailing technology along with the already available alternatives. In [11], gave an analytical review of different stand-alone WECSs based on possible generator types, available in wind market and reported in the literature. The overview concentrated on the variable-speed turbines. Geared-drive turbines using induction generators and gearless drive turbines using synchronous generators were considered. The configurations and characteristics of different wind turbine systems were described and discussed along with their advantages and drawbacks. There are yet numerous aspects including availability and reliability of large wind generator systems, fault-related aspects, yearly energy production, cost and economics, control function and power quality etc. which may be further investigated for different WECS concepts [12].

WECS Control Aspects Ackerman [13] presents basic theoretical background, a brief review of past development and present status of wind generation. In addition, the said book covers the aspects of the power quality, its standards and measurements, interconnection requirements and standards as well as a discussion on power system requirements regarding wind power. Some books [14, 15] discuss various types of WECS in detail, along with their control structures. Both aerodynamic control and generator control are discussed. The wind power goes through three main stages of control as shown in Figure 5.1. This includes the aerodynamic control for high winds, generator control for optimal operation and grid converter control for power conditioning and grid synchronization. These control stages also provide control during abnormal conditions. These books [14, 15] also discuss the designing and modeling concepts related to WECS using induction and synchronous generators. In [16] presented the general aspects related to wind, the current status and market for renewables with main focus on wind power, the related policies and the penetration of wind power worldwide.

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Figure 5.1. Control aspects of WECS.

In [17], the author has reviewed the common types of WECS, the maximum power point tracking (MPPT) methods for the variable speed WECS and the prospects of potential WECS technologies. The typical four MPPT methods are summarized in [17], while the power feedback control and hill climb search (HCS) control are said to be the most popular ones. Various MPPT techniques including perturb and observe (P&O), power signal feedback (PSF) and fuzzy logic control (FLC) algorithms along with pitch control is discussed in [18, 19]. These MPPT algorithms track the maximum power for varying wind conditions. Pitch control comes handy when speed increases more than rated speed. At higher winds, the WT changes pitch angle in such a way that it will rotate at constant speed to generate maximum power. Carriveau [20] also presents various MPPT techniques including tip speed ratio (TSR) control, PSF control and HCS algorithm for WECS. Zhang et al. [21] explained pitch angle control techniques using PI controller and fuzzy logic controller where it is stated that FLC can work well even when knowledge about the WT dynamics is not available. This control is more favorable when WT contains strong non-linearity. In [22] explained various techniques for MPPT by making use of FLC, neutral network etc. Ahmed et al. [23] has discussed an algorithm for MPPT control of grid integrated variable speed WECS with boost converter at the generator/machine side. Haq et al. [24] has presented the MPPT control scheme of PMSG-based VSWT. This strategy can be applied to vector-controlled SCIG as well. The same can be applied with sensor-less speed estimation techniques so that the use of mechanical speed sensors can be avoided. Different MPPT techniques based on hill climbing (perturb and observe) methods are presented in [25]. In addition to the five methods (three conventional and two intelligent control) of MPPT viz TSR, PSF, HCS, artificial neural network (ANN) and FLC, the authors in [26] have presented additional methods based on sensorless and sensored approach referred as optimum relationship based (ORB) method of control. This method of control is fairly effective and particularly used for the high-capacity WECS. Hybrid methods of MPPT are also

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discussed in the same. Table 5.2 presents the comparison of various MPPT methods for WECS followed by Figure 5.2 representing the classification of various methods. Singh et al. [27] presented a detailed review of power quality improvement converters, inclusive of their configurations, control approaches, design features, selection of components and other related concerns. In [28] presents a detailed review of the current state power electronic converters for wind energy, along with different generator configurations. A detailed appraisal of different wind power technologies and their control is presented in [29]. A comparison of various WECS technologies is presented in [30, 31]. It is brought out that PMSG has good performance but costly and DFIG has low cost but not reliable. A half direct-driven PMSG and doubly fed brushless generator may be the preferred alternative choices for WECS in future. The results in [30] shows that the best technical choice depend upon the network characteristics. According to [31], the wind generator systems with singlestage gearbox seems to be the most attractive choice. Table 5.2. Comparison of various MPPT methods [26] MPPT Method

Tracking Speed Fast

Complexity

Optimal Torque (OT) and PSF Control Relation between voltage, current, power HCS or P&O Control

Fast

Low

Required

Medium

Low

Not Required

Slow

Low

Not Required

Hybrid control

Fast

Medium

Intelligent Control like Fuzzy and ANN based Control

Medium

High

Not Required Required

Tip Speed Ratio Control

Optimum Relation Based (ORB)

Low

Prior Knowledge Not Required

Application Suitable for medium rated WECS with rapidly varying wind speed Huge capacity WECS

Huge Capacity WECS with moderate wind speed variation Suitable for low cost and small capacity WECS Huge capacity WECS Suitable for large scale WECS

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Figure 5.2. MPPT methods for WECS.

Multiple aspects of different wind generator systems, such as the range of speed that can be handled, cost, FRT capability etc., have been compared by a number of authors. When two technologies are compared on one single aspect, the relative merits and demerits may not be brought out comprehensively. For example, when a geared-drive technology is compared with a direct-driven WECS, the advantages of the latter are low maintenance requirements, reduced noise and high efficiencies for lower wind speeds. The former also has certain advantages like reduced initial cost, transportation easiness and easy installation because of small machine radius, better efficiencies at greater wind speeds. It may not be practical to compare all the aspects of two different WECS; still, certain important factors have to be considered in the quantitative analysis of various topologies of WECSs [32]. Recent trends in research of WT are majorly centered to the offshore WECSs. The major difference in the requirements of on-shore and off-shore wind power systems is based on their need of maintenance and robustness. The offshore needs to be free from all this as it becomes extremely difficult and expensive, sometimes impossible to maintain and repair the offshore systems under indeterminate weather conditions. It is important to consider the availability and reliability of large wind turbines under adverse operating conditions. In [33] presented an overview and analysis of the state-of-the-art generator technology for offshore wind energy conversion systems (WECS) in order to identify the current technology frontier. The design and integration issues as well as the technology trends for directly driven off-shore WECS are also discussed. Datta et al. [34] have compared a variable speed DFIG-based WECS configuration with other alternative schemes in grid-connected mode

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considering the hardware components, region of operation, and output energy with a well-defined wind regime.

DFIG-Based WECS Control Aktarujjaman et al. [35] have presented the modeling and analysis of a variable speed DFIG-based WT and investigated the control dynamics under sub synchronous and super synchronous modes of operation. Synchronized control amid rotor side converter (RSC), grid side converter (GSC) and the DC link ensures maximum power extraction under variable speed conditions. Ghennam et al. [36] have presented a decoupled real and reactive power control of DFIG using RSC by using rotor currents d-q components and experimentally validated this scheme. Pena et al. [37] have described the architect and design of a DFIG with PWM controlled cascaded voltage source converters (VSCs) connected through the rotor as illustrated in Figure 5.3.

Figure 5.3. Schematic diagram of overall DFIG-based system [37].

A vector control scheme for GSC is shown resulting in autonomous control of real and reactive power while ensuring sinusoidal supply current. Field oriented control scheme for RSC is shown to have capability of capturing maximum energy from the wind at wide ranging operating speeds. Lei et al.

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[38] have modeled a DFIG for wind integration studies, in which the simulation of a power converter is carried out as a controlled voltage source, controlling the rotor current to supply the demand of real and reactive powers. Poitiers et al. [39] have presented a control scheme for DFIG-based WECS. They have regulated the stator real and reactive powers by governing the machine with three diverse controllers viz. proportional-integral (PI), polynomial RST based on pole placement theory, and linear quadratic Gaussian. A new control strategy for GSC is also proposed. Barendse et al. [40] have presented control strategies for commercial wind power systems. In [40], an effort has been made to fill the gap between academic research in WECS and the products as they exist today. Also, a typical hardware setup using a DC drive to emulate the WT is discussed along with experimental results.

Figure 5.4. Schematic diagram of field-oriented control for the DFIG system [40].

Figure 5.4 depicts the scheme of field-oriented control, applied to a DFIGbased system. Field-oriented control is exercised via RSC as it allows individual control of rotor excitation and torque. The other converter at supply side maintains a constant voltage at the DC-link and controls the power factor. Rabelo [41] presented a control design for reactive power regulation in DFIG drives with the help of linear control techniques. It is proved that the sharing of reactive power among the converters enable the distribution of reactive

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currents and therefore power losses are reduced. Babu and Mohanty [42] and Arrifujjaman [43] have presented the simulation cum modeling of WT driven DFIG, feeding power to the grid, using dynamic vector approach. Two PWM voltage source converters are used and power flow is controlled via GSC whereas the rotor excitation is provided by the machine side converter (MSC) and the system is simulated using MATLAB simpowersystems. A control scheme for GSC is also proposed in [44]. In order to produce optimal energy at particular wind regime and control the real and reactive powers individually, an adaptive fuzzy control employing stator flux oriented control is described in [45]. In [46] gives the recommendations for dynamic modeling of two WT generators, by GE Power, for use in studies on integration of WTs to the power grid. The report recommends the model structure of a DFIG-based WECS. Relevant data, capabilities, assumptions and limitations of the model used by GE are also provided in the same. The model is for positive sequence phasor time-domain simulations and assumes that the analysis is mainly focused on faults. The model provides the effects of wind speed fluctuations on the power output of the WT generator. Janssens [47] provided the strategy to control active power flow in DFIG-based WECS. When the power margin is varied with reference to the maximum available power, the set point torque variation rate also has to be regulated for avoiding unwanted mechanical stress and torsional oscillations on the shaft. Abbey et al. [48] presents the simulation cum modeling of a DFIG for providing individual control of real and reactive powers by the use of a battery at the DC link. In [49], three simulation software viz. FAST, Simulink and TurbSim are used for modeling the wind system, its electrical and mechanical parts and a DFIG with its controllers. Simulation results so obtained are used to see the interaction of mechanical, aerodynamic and electrical aspects which affects the WECS operation.

PMSG-Based WECS Control The most popular configuration of PMSG-based WECS comprises of a diodebased rectifier connected to the chopper with a voltage source inverter. This configuration provides handling simplicity and cost-effectiveness [50]. The voltage source inverter provides grid interface while rectifier with chopper combination is used to control the output current/torque of the PMSG. Another configuration comprises a controlled rectifier and an inverter combination. Vector torque control is applied in these end-to-end connected converters to offer the variable speed operation [51, 52].

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Chinchilla [52] proposed a control scheme for PMSG-based variable speed wind systems connected to the grid. The generator is tied to the grid via converters consisting of two insulated gate bipolar transistor (IGBT) bridges connected back to back where one bridge is connected to the generator and works as a rectifier while the other one is tied to the grid that works as an inverter. Both converters are PWM controlled. Real power control is achieved using rectifier whereas DC link voltage control is achieved by the inverter. Experimental results are also presented for this configuration. Figure 5.5 presents the blueprint of the control schemes used for PMSG.

(a)

(b) Figure 5.5. Control schemes used for PMSG (a) rectifier control and (b) inverter control [52].

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In [53], a similar set-up is discussed and the simulation is done using PSCAD/EMTDC. Reflecting the latest wind energy market trends and research articles, [54] presents a survey on important electrical engineering aspects of PMSG-based megawatt-level WECSs. A comprehensive analysis on power converter topologies for WTs, grid integration, digital control schemes, FRT compliance methods, and future trends is presented. Li and Haskew [55] focused on the PMSG characteristics analysis from both fixed and variable speed considerations. It is given that PMSG torque (hence active power) characteristics are directly proportional to the direct component of the stator voltage whereas the reactive power is majorly altered by the working speed and the stator voltage amplitude. Dai et al. [56] employed a high rated current source converter (CSC) to connect the generator directly with the grid for wind power applications. It is realized that at high-rated medium voltage system, the PWM CSC shows its advantages of having simplest configuration with a smaller filter giving smooth power and providing sure short circuit protection. The proposed scheme provides the power factor control as well as achieves optimal power tracking. Hu et al. [57] presented a simulated model for a megawatt level WT operating at variable speed and using a PMSG with full-rated converter developed through PSCAD/EMTDC. The WT is investigated for its low voltage ride-through (LVRT) abilities. A novel scheme with improved focus on DC link voltage regulation is made and its LVRT ability is tested. In PMSG-based WECS, although the MSC may be a diodebased rectifier along with buck-boost converter, it would be better to use a VSC at both grid side and machine side to obtain better power optimization, power quality and control during faults [58]. A detailed control structure employing a back-to-back converter configuration is described in [59, 60] where it is emphasized that vector control of the PMSG offers highly effective and adaptive speed operation further permitting the maximum energy production from wind. An active model with control structures matching a variable speed WT with PMSG is presented in [61]. The model comprises a PMSG-based model with a pitch-controlled turbine and a drive-train box. Based on the wind speeds, the pitch angle is controlled and electric power output is varied as per the input signals which ensure usual operation of WT, even at higher winds. The speed control is implemented using vector control, in which the d-axis current reference is set to null and the q-axis current controls the generator speed in accordance with the variation in wind speed. In [62], the configuration includes a six-pulse diode-based converter with a boost chopper followed by a 3-level neutral point clamped converter (NPCC). The control is majorly based on real power and reactive power loops. The

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system also includes additional blocks with virtual flux and the capacitor (used as a filter) voltage estimation for providing the sensor-less procedure. A sensor-less control based on field oriented control (FOC) approach for a PMSG-based system without gearbox is configured in [63]. In the experimental prototype, MSC is controlled to provide optimal power from the wind while the GSC maintains constant voltage at the DC link. A method for estimating the rotor position is investigated and also tested in simulation. PMSG modeling, simulation and vector control has been described in literature [64, 65] using back-to-back connected VSCs where MSC controls MPPT and GSC ensures constant DC link voltage.

Figure 5.6. PMSG-based WECS using diode bridge rectifier and a boost converter.

Ohyama et al. [66] proposed a PMSG-based WECS using boost converter with different speed control modes including high, medium and low wind speeds. The direct driven mechanism directly links PMSG with the wind turbine as shown in Figure 5.6. For wind speeds above rated, the WT torque is regulated by pitch mechanism control system so that the windmill speed is fixed at 105% of its rated value while the power output is fixed at the power rating of the PMSG. For wind speeds below rated, the WT speed is adjusted through the PMSG torque so that the energy conversion efficiency of the WT is maintained at its optimum. Two converters, rectifier and the PWM inverter are interconnected through a DC link capacitor. The PWM inverter is responsible to supply the active power obtained from the rectifier/converter side to the power system. Kim et al. [67] presented a new control scheme for PMSG-based WECS with FRCs. A supervisory reactive power control scheme to control the voltage at remotely situated location by regulating the reactive power delivered by GSC is discussed in detail. Yang and Chen [68] presented a control that does not require generator parameters, rather it is developed after comparing various

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techniques for dual PWM PMSG control. The control scheme changes the voltage at the DC link through MSC and the real power control through GSC.

SCIG-Based WECS and Associated Controls The books by different authors discuss FOC or vector control of three-phase machines [69, 70] that are easily applicable to WECS configurations. Dominguez-Garcia et al. [71] described a stator-side converter control strategy based on indirect vector control, which allows the system to work without sensors and also allows LVRT without extra devices. Cardenas et al. [72] and Senjyu et al. [73] presented speed and position sensor-less MPPT control for WECS with SCIG. Ahmed et al. [74] described an innovative control strategy for the pulse width modulated converter with variable speed induction generator (IG). Suebkinorn et al. [75] presented the vector control and d-q control of back-to-back connected converters for SCIG-based WECS. The experimentations in this work were carried using a 5.5kW squirrel cage generator tied to a 7.5kW WT simulator. In [76] V/f control is implemented on SCIG, for motoring operation and for controlling the dynamic transients as well as the voltage build-up. The controller is a special one, which maintains constant voltage at the DC bus under electrical load variations and even under wind variations. In [77], control of a grid-connected WECS is presented which used a sensor-less vector-controlled SCIG connected to a matrix converter. The generator is controlled using a model reference adaptive system observer that evaluates the generator speed and position of its rotor. The induction generator torque is controlled to operate the WECS at such an operating point where aerodynamic efficiency of WT is maximized. Blaabjerg et al. [78] provided an overview of grid synchronization and control of a distributed power system. They also presented different control strategies applied to GSC along with the relative merits and demerits. A detailed discussion about harmonic compensation-based control for GSC is also included in [78]. Different phase-locked loop (PLL) techniques for grid voltage angle estimation under normal and disturbed grid conditions are discussed in [79, 80]. Twining et al. [81] and Liang et al. [82] discussed the modeling and implementation of the synchronous voltage-oriented control (VOC) of grid converter with improved power quality. Raducu [83] and Svensson [84] described different control aspects for GSC in a back-to-back connected converter configuration. Teodorescu et al. [85] discussed proportional resonant control of grid converter.

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For GSC control, hysteresis current control (HCC) and VOC are increasingly being used in wind systems. In synchronous VOC, switching frequency of the inverter remains constant and hence, the harmonics have a specific pattern depending on the switching frequency; whereas in HCC, the switching frequency is a variable one and hence, the output filter design is difficult. However, with the help of adaptive band hysteresis control, a constant switching frequency can be obtained. The necessity of cross-coupling terms and the voltage feed-forward in controlling the GSC in synchronous reference frame makes it more complex. HCC does not require any information about system parameters. Moreover, VOC algorithm is more sensitive to voltage unbalances and asymmetries than HCC due to the double transformation of axes. Kazmierkowski et al. [86] presented the basic theory of hysteresis current control for a 3-phase VSC. Malesani et al. and Bose [87, 88] provided a detailed discussion about adaptive band HCC to have constant switching frequency. Kwon [89] discussed a novel SVM-based HCC for gridconnected inverters. Song et al. [90] described the control scheme for a gridconnected AC to DC and DC to AC power converter for a variable speed WECS having machine side rectifier based on diode bridge followed by a boost converter. Mokui et al. [91] have investigated the dynamic response of wind generators during wind gusts, control of reactive power, and symmetrical as well as unsymmetrical voltage dips. The impact of voltage dips on the d-q control conditions of the converters are also accounted for.

Fault Ride-Through (FRT) With the raised wind penetration levels in the power grid, it is expected that the WECS become more reliable and able to ride through short-term grid faults. FRT is a major requisite by the network operators and it is expected that the WT remains connected to the power grid, even during severe disturbances. Also, the same shall normalize as quickly as possible, after the disturbance. During the dropping voltage conditions at the grid, it becomes essential to keep the wind farm connected so that major blackouts are prevented. Many WT control systems installed just a few years ago did not have the LVRT feature. All those systems cannot be upgraded in an economical way to be acceptable according to the new grid codes [92]. With the high penetration levels, the overall power system gets adversely affected, therefore transmission system operators (TSOs) establish their own grid codes. Grid codes envisage establishing a standard operating practice for WTs to

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minimize these adverse impacts and reap maximum benefits. Internationally, the countries who are leaders in terms of installations have framed grid codes for wind. USA, Denmark, China, Spain, Germany, Nordic countries, Ireland and Canada have enforced their own grid codes. With the increasing penetration of WTs in India, as high as 14% in terms of installed capacity in states like Tamil Nadu, the need was felt to establish a standard operating practice for the WTs. This has led to the draft grid code [93] to establish guidelines specific to wind turbines in India. There are mainly five kinds of grid codes in the rest of the World i.e., E.ON Netz of Germany, Nordel of Nordic countries, Danish for Denmark, Alberta electric system operator (AESO) of Canada and Federal Energy Regulatory Commission (FERC) of USA [94]. All of these grid codes have a common framework dealing with issues specific to wind, however the regulations take into account––the nature of the grid, installed capacity, penetration level of WT, high wind potential zones etc. The grid codes deal with issues including frequency and voltage control, active power control, reactive power concerns, protection, FRT capability and power quality issues including harmonics and flicker etc. Literature review is further performed to study FRT techniques under research and in operation for asynchronous and synchronous generator-based WECS. In [95] gives a review of conventional and state-of-the-art methods for analysing the dynamic stability of WECS. Different transient models are simulated for various WT generator configurations, under different grid conditions.

DFIG-Based WECS with Partially Rated Converters Various strategies and configurations have been proposed to accomplish effective FRT in DFIG-based WECS. Some authors [96, 97] have discussed the use of a set of bypass resistors referred as crowbar which gets connected to the rotor winding ends in the event of fault. The schematic of crowbar application to DFIG is shown in Figure 5.7(a). In [98], the crowbar approach is being criticized since it makes the generator to carry a large rotor resistance resulting in a poor response which also consumes the reactive power during the fault. However, the operation of DFIG during faults can be made better by proper selection of crowbar resistance value [99]. It is also true that the timing sequence for the crowbar removal affects the variation in currents and electromagnetic torque after fault clearance. Use of crowbar invites the expenditure and installation of added devices as well as extra controls in the configuration,

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which also hinders the reliability of the system. The design/scheme of such system is developed after analyzing the topography of the wind power system. Once developed, the further changes are avoided as it would affect the performance of the system. Various crowbar topologies can be developed based on the system configuration as explained in [100].

Figure 5.7. WT equipped with (a) rotor crowbar (b) rotor crowbar with a DC chopper.

In [101], WTs equipped with rotor crowbar (Figure 5.7(b)) is discussed. DC-link DC-DC converter keeps the grid to stay connected and limit the current and voltage within their threshold values. During periods when crowbar is active and the DFIG is uncontrollable, there active current support to grid is provided by the GSC, temporarily resulting in its overload. However, GSC has to control the active power flow also by controlling the DC link voltage. A succeeding crowbar period (second one) after this voltage recovery indicates a poor operation as the grid demands high reactive power. A welldesigned DC-chopper and the crowbar firing, right at the moment of voltage recovery can be precluded (refer Figure 5.7(b)). Another technique is modifying the DFIG control, which is sometimes used along with a crowbar system. This technique is applied to the rotor and/or the grid side converter in order to decrease the peak values of the rotor currents and the voltage at the DC link, and inject reactive power into the grid, supporting the voltage re-establishment [102, 103]. In [104], an improved control method for DFIG to enhance FRT capability including blade pitch angle control, frequency stabilization and STATCOM operation of GSC is proposed. However, whether the wind farm can supply enough reactive current to ride over the voltage dip, whether the stator/rotor currents exceed critical values, these questions is still unanswered. There are literatures [105,

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106] in which authors consider a strategy to prioritize there active power (qaxis current) injection. The strategy basically involves RSC/GSC control modifications in all cases. A modified hardware set-up may be used along with the crowbar to augment the FRT capabilities of the system. The behavior of a WT can be improved by employing a set of resistors, static series compensator (SSC), series anti-parallel thyristors or a static synchronous compensator (STATCOM) [107, 108]. In [109, 110], a STATCOM and static VAR compensator (SVC) are connected at the grid interconnection point which provides the reactive power support during disturbances/faults. These solutions, however, add to the cost and complexity of the overall system. In [111], the proposed control structure measures stator currents and uses them to set the reference rotor currents during fault. It makes possible to synthesize a current in the machine in opposition to the currents produced during the fault, thus preventing the rotor/stator windings experiencing over currents. A new control method for smoothening of stator active or reactive powers ripple components under unbalanced grid voltages for WECS using DFIG is suggested in [112]. The negative sequence rotor current is used to suppress the ripples in real and reactive powers, which are extracted through a band-pass filter. It is presented that the reduction in reactive power ripple will result in the torque ripple reduction.

Variable Speed WECS with Fully Rated Converters Different strategies have been presented by various authors to enhance FRT capabilities of FRC-based WECS. Many devices, such as SVCs, dynamic voltage restorers (DVR), static Synchronous compensators etc. have been shown to improve FRT of WECS, but they will also increase the overall cost of the system [113, 114] especially in WECS with FRC. In [115], a nonlinear controller design for power converter-based WECS is presented which ensures current levels within design limits, even at greatly reduced voltage levels. A back-to-back power converter configuration is discussed in [116] where MSC controls the speed of shaft by flux vector control strategy and GSC controls the active and reactive powers by PWM strategy. In [117], transient analysis of a grid-connected WECS based on PMSG is presented by solving the differential equations describing the system dynamics. During fault in PMSGbased WECS, the converters have to be protected against high currents through GSC and high DC link voltage. According to [118], a crowbar has to

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be provided as a by-pass for over-flowing power, which can short circuit the DC bus. The position of the braking resistor in the PMSG wind turbine system is shown in Figure 5.8.

Figure 5.8. PMSG wind turbine with an electromagnetic braking resistor.

An improved crowbar strategy is proposed in [118] to limit the current and protect the converter. The strategy also supports capacitive current injection. A flexible active power control of distributed power generation systems during grid faults is presented in [119] with different techniques for grid converter reference current generation. In [120, 121], a combined synchronization method for FRC aiming to ride-through balanced and unbalanced voltage dips due to grid faults is proposed. A Kalman filter synchronization, capable of extracting the positive and negative sequence grid voltages, is designed to have a fast response to voltage disturbances. Then, a synchronous reference frame (SRF) PLL is used to generate synchronization signals. As a result, the converter is able to provide faster response during voltage disturbances to provide voltage support even in the cases when the voltage falls to zero at the point of common coupling (PCC). Also, an antiwindup strategy is introduced on the current control loop to avoid undesirable behavior during fault transitions. Control of GSC based on positive sequence synchronous reference frame is described in [122]. Ahuja et al. [123] have presented a coordinated control strategy for both real and reactive powers during grid faults. Substantial research has been carried out in innovating different strategies to enhance FRT capabilities of WECS. The majority of FRT strategies are designed to deal with faults on grid but these are not able to address the issues related to unbalanced grid conditions effectively. During grid faults, there occurs a difference between power generated and power consumed. It is because the grid is not able to send away the power generated. Voltage at DC link therefore rises due to this imbalance. The situation becomes more complicated when the fault is unsymmetrical resulting in appearance of dual frequency oscillations at the

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DC-link. The WECS needs to have a comprehensive control on active power as well as reactive power handling. Song and Nam [124] provided a dual current control scheme based on positive and negative sequence synchronous reference frames for the GSC under unbalanced input voltage conditions. This control strategy helps to reduce the DC link voltage ripple by eliminating double frequency oscillations in real power. Ramtharan et al. [125] described a de-loading scheme for the MSC under severe grid fault. Jianlin [126] described a reactive power injection scheme to support grid voltage under grid faults without exceeding converter current limits. Miranda et al. [127] proposed an improved controller whose control strategies are different during normal and faulty operating conditions. The reference signals for the controller are calculated to obtain zero power oscillations. Reactive power as well as real power is controlled by current control in [128]. Dash et al. [129] discussed the implementation of two different controllers namely PI and proportional resonant (PR) controllers in order to obtain the control of GSC during single phase to ground fault. The analysis includes grid current harmonic distortion along with the variation of active and reactive powers during the fault condition. Rioual et al. [130] and Benchagra et al. [131] presented the control of GSC during grid voltage unbalance. LVRT of SCIGbased wind turbines and grid requirements under fault are discussed in [132]. The study in [133] explains a variable speed wind system which uses different type of power electronic converters at the machine side and the grid side for providing grid synchronisation, maintaining power quality and operation at unity power factor. Khan et al. [134] have described the LVRT of WECS by suppressing the overvoltage appearing at the DC link and the active power limitation during an unbalanced fault. It however does not discuss about the oscillations occurring during the unsymmetrical faults. A generalized discussion about the control of WECS is provided for operation under unbalanced network conditions.

Findings and Research Gaps The review highlights an enormous growth of WECS, expected be witnessed in future, with new wind turbine technologies, generator types, converter controls and protection devices. These technological developments in WECS are predicted to result in development of more efficient and high power WECS for on-shore as well as off-shore requirements. A progressive trend is observed in harnessing the wind energy. With the current growth trends of WECS, it is

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expected that it will become a significant source of electricity in the near future. Switching to the wind power will definitely ensure a pollution free environment and energy independence. Although wind is becoming the most viable source, the review reveals that there are still lot of issues to be addressed. The literature high points a lack of clear consensus on the control of power electronic converters used for various WECS and an inadequate treatment of dynamic issues related to their operation at present. These problems have not been adequately addressed and continue to pose challenges to the control community. WECS control issues are therefore important, as new grid code requirements are becoming harder day by day. After critically examining the existing literature, the following research areas have been identified to be considered for further explorations: •







The variable speed WECS models are more or less generic, and there is still a requirement of well developed (more accurate) models along with their control capabilities to study the dynamic behaviorof WECS under varying wind speed and load conditions. Three types of generator concepts namely DFIG, PMSG and SCIG are the most dominant concepts in the market, however research is active on exploring the possibilities for the new generators like switched reluctance or brushless DFIGs. Each generator concept has its own merits and demerits. The best technical choice among these concepts depends upon the network characteristics and various other factors such as geographical location and nature of loads. Also, the performance of WECS is largely dependent upon the choice of generator and converter used. Therefore, it is needed to develop new generator control concepts having characteristics and capabilities such that they can be adapted to any site/condition. Various performance indices have been used in literature for comparing different WECS configurations, mainly on the basis of torque density, cost per unit power output, efficiency, active material weight, outer diameter, total length, total volume, total generator cost, cost of energy and so on. There are still many aspects like reliability and availability of large wind generator systems, fault related aspects, annual energy yield, economy, control function, and power quality etc. which need to be further investigated for different WT concepts. Sensorless vector control techniques for generator converter are well developed however new techniques need to be further explored which

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will help to eliminate speed sensing and hence facilitate better MPPT technique to be adopted. Direct torque control (DTC) and direct power control (DPC) for generator control, though given in literature, the same can be examined with and without speed sensing. FRT of WTs is becoming an important area of research. Literature available for FRT of full rated back-to-back converter system is not significant. A complete coordinated control of converters and pitch mechanism during grid fault conditions would be an interesting topic. DFIG-based WTs equipped with rotor crowbar are criticized for making use of extra devices which add to cost and complexity; also, they consume additional reactive power. The control strategy may be modified to enhance the FRT capabilities; the reactive power injection during fault to keep the voltage afloat will be a good option. Literature speaks very less on behavior of the DFIG-based WECS during grid faults. A complete behavior of DFIG during unsymmetrical and symmetrical faults is still not available in literature. A comprehensive control scheme for the power electronic converters that would work under normal as well as faulty conditions has to be developed for DFIG, PMSG and SCIG-based WECS. The present control strategies take care of only positive sequence currents, but during unsymmetrical fault conditions the negative sequence components also appear in the machine currents. Control strategies that manipulate both positive and negative sequence components of currents must be developed in order to ensure a proper control during unsymmetrical faults. Different reference current generation strategies for better performance during unbalanced fault can be explored and implemented. Current limiting of converters under faulty grid condition with complete utilization of the converter rating for reactive power injection can be done. Apart from dealing with symmetrical and unsymmetrical fault conditions, LVRT is an important characteristic, a wind farm in a weak power system should possess. Thus appropriate controls should be developed to achieve this. Development of advancedwind farms having control capabilities equivalent to conventional power plants is to be done. Their reliability and protective features are to be enhanced.

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Conclusion In this chapter an exhaustive literature review on complete control aspects of grid-connected DFIG, PMSG and SCIG-based WECS has been presented. From the reported literature it is observed that extensive work has been carried out on the analysis, modeling and controller design for these generators but still there is a need to develop accurate models for the WECS and get an objective comparison of their performances under similar operating conditions to make a judicious choice. Many of the presently installed wind systems do not have proper FRT controls and hence cannot operate satisfactorily in a weak power system. But, the grid requirements are becoming tough especially with the increasing penetration of wind power. Thus, research work is needed to develop suitable models for WECS based on different generators and also attempt to implement appropriate control strategies to achieve LVRT and FRT.

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Chapter 6

Simulation and Analysis of Three-Phase and Five-Phase Variable Speed PMSMs under Open Phase Fault Conditions Khadim Moin Siddiqui1,*, Farhad Ilahi Bakhsh2, Bhavesh Kumar Chauhan1 and Arif Iqbal3 1Shri

Ramswaroop Memorial College of Engineering and Management, Lucknow, India Institute of Technology Srinagar, Srinagar, Jammu and Kashmir, India 3Rajkiya Engineering College, Ambedkar Nagar, India 2National

Abstract Permanent magnet synchronous machines (PMSMs) are becoming prevalent in the wind energy conversion system because of their greater efficiency with better performance. This chapter presents the simulation and analysis of five-phase and three-phase PMSMs under healthy as well as under open phase fault conditions. The simulation of five-phase and three-phase PMSMs have been carried out digitally and after that the vital performance of both machines has been examined before and after failure. This is crucial because if the fault is not diagnosed at an early stage, it can result in expensive damage and lead to creating another fault. This unwanted condition leads to be prevented. Hence, a control system under one phase open in three-phase PMSM and multiple phases open in five-phase PMSM with sinusoidal back-EMF have been developed. The open phase fault has been created only for one and half cycle and after that proportional-integral (PI) controller compensates the effect of fault. From the obtained results, it is clear that the five-phase PMSM is having higher fault tolerant capability as compared to three-phase PMSM. 

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Keywords: five-phase PMSM, open phase fault, permanent magnet synchronous machine, PI controller, PWM, three-phase PMSM

Introduction The application of synchronous machines is growing in wind energy conversion systems which demand the need to improve their operating performances [1-3]. The synchronous machines are costly and are widely used in the industries due to its high performance and efficiency [4-8]. At present, as the variable speed applications in industries are increasing, permanent magnet synchronous machines (PMSMs) are finding significant scope for variable speed applications. In PMSM, one can reduce harmonics produced by the inverter significantly by increasing the number of phases [9-10]. Therefore, nowadays inverter-fed multi-phase PMSM is being installed in the industries to achieve variable speed for many applications. The multi-phase PMSM is applicable in the variable speed drive applications, mainly in the wind energy conversion systems. The detailed simulation and modeling of inverter-fed PMSM under healthy condition is given in [11]. The model has provided accurate information of variable speed PMSM (VS-PMSM). Nowadays, multi-phase PMSM is being recommended in the industries because of its many useful advantages especially for higher fault tolerant capability [12-13]. It is noticeable that the five-phase PMSM is a better solution for industries for optimal design and control [12]. The PMSM contains important features such as higher reliability, reduced torque ripple and higher torque density with good speed-torque characteristics [13]. Although the five-phase PMSM has better fault tolerance capability, the efficiency of the five-phase PMSM needs to be critically assessed with an identically rated three-phase machine [14-19]. This chapter presents the analysis of a five-phase PMSM with the help of a simulation model to verify the better fault tolerance capability as compared to the lower-phase machine. The PMSM drive commonly employs different modulation schemes for inverter control i.e., sinusoidal PWM inverter control and space vector PWM inverter control. For fast response and minimized harmonics, the SVPWM control is preferred [17].

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Since the last decade, three-phase permanent magnet machines have been recognized for many advantages such as greater efficiency over other machines. For variable speed PMSM, several control design methods were introduced to enhance machine efficiency because at higher speeds, harmonics would be higher. Inverters also cause some losses and reduce the overall performance of the machine. These days, synchronous reluctance machines are also recommended to be used in electric and hybrid vehicle applications [11-12, 19-21]. These machines have a desirable performance at higher speeds, fulfilling the constant-power speed requirements. In this chapter, open phase fault analysis of five-phase and three-phase VS-PMSM is done. For the analysis, five-phase and three-phase VS-PMSM simulation models have been developed in MATLAB/Simulink environment. The performances of the developed models have been analyzed under open phase fault condition and compared with each other. Three PMSM parameters viz. electromagnetic torque, rotor speed and stator currents have been considered for comparison of the performances. The proportional-integral (PI) based controller has been used for regulating the variable loss of PMSM. In this work, initially both the PMSM models are running with healthy phase and after some time open phase fault is introduced. Here, one phase is opened in three-phase PMSM and two phases in five-phase PMSM models and the changes are observed in the waveforms of considered PMSM parameters. From the obtained results of three-phase and five-phase PMSM with open fault topology, it is analyzed that five-phase PMSM has higher fault tolerance capability with good speed regulation than three-phase PMSM.

Simulation of Three-Phase PMSM The block diagram of complete simulation model of the three-phase PMSM is shown in Figure 6.1. The sub-systems of the complete simulation model are presented in Figures 6.2-6.5. The mathematical model of this system has already been reported in [11]. For the purpose of analysis, there are two control loops i.e., outer control loop and inner control loop. In the outer control loop, the reference speed is compared with the three-phase PMSM speed and the generated error signal is given to the PI controller. The sub-system of PI controller is shown in Figure 6.2. The value of the proportion gain (P) and integral gain (I) is set at 0.4 and 3, respectively. The minimum and maximum output limits are set as –150 to

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+150. The PI controller gives output (Iq_ref) according to the input speed error signal which is fed to the dq to abc block.

Reference Speed

Timer PI

0

Iq_ref

Fault Command

Tm

Id_ref

I_abc Va

Io_ref

I_ref

Theta Command

Load Torque

Relay

A

Vb

B

Vc

C

PWM Inverter

dq to abc

Stator Current Rotor Speed Rotor Angle Electromagnetic Torque

3-phase PMSM

Figure 6.1. Block diagram of three-phase PMSM simulation model.

Figure 6.2. Sub-system of PI controller block.

The dq-abc transformation is done by a sub-system model as shown in Figure 6.3. At the start of PMSM, it will run in healthy phases. After some stipulated time, phase ‘a’ will be opened by relay. After some set time PI controller will compensate for the open phase fault. The sub-system of the PWM inverter model is shown in Figure 6.4; where, it can be understood that the inverter output goes through the regulated voltage source block before it is supplied to the PMSM stator winding. The sub-system model of the timer block is shown in Figure 6.5. A changing signal is produced by the timer block at the particular time and this block is used to give a 0 or 1 signal, which is used to regulate the closing and opening time of the relay.

Simulation and Analysis of Three-Phase and Five-Phase ...

Figure 6.3. Sub-system of dq-abc transformation block.

Figure 6.4. Sub-system of pulse width modulated inverter block.

Figure 6.5. Sub-system of the timer block.

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Analysis of Three-Phase PMSM The load torque applied on the machine shaft is 7 N.m. In the simulation model (see Figure 6.1), two control loops have been used for the purpose of regulation. The inner loop is implemented to control the stator currents of the three-phase PMSM whereas the outer loop is used to control the speed of the three-phase PMSM. The PMSM starts under healthy condition (normal operation). The phase disconnection time is set by the timer block. A relay is connected with phase ‘a’ which will be open and close phase ‘a’ circuit as per instruction issued from the timer block. The timer block is connected with set time 0.06 sec which means, at t = 0.06 sec, phase ‘a’ will be disconnected. Another timer is used for fault command operation. In this timer, the time is set 0.09 sec. This means that at t = 0.09 sec, the current references of each controller are changed to compensate for the phase loss. The waveforms of three-phase stator currents, rotor speed and developed electromagnetic torque of three-phase PMSM are presented in Figure 6.6. It is evident from Figure 6.6(a) that at t = 0.06 sec, phase ‘a’ has been disconnected. Hence, phase ‘a’ current becomes zero after half cycle and remains zero till end. Phase ‘b’ and phase ‘c’ currents are not becoming zero. They shoot to almost 2 to 3 times the normal value. At t = 0.09 sec, the PI controller compensates for the phase loss and then, phase ‘b’ and phase ‘c’ currents stabilize to around twice the normal value after some transients. It is evident from Figure 6.6(b) that when phase ‘a’ gets disconnected i.e., at t = 0.06 sec, the speed of the three-phase PMSM reduces from the normal value and oscillates till t = 0.09 sec. At t = 0.09 sec, when the PI controller reacts, the speed of the three-phase PMSM again starts increasing and then oscillates near to the normal value. It is clear from Figure 6.6(c) that when phase ‘a’ gets disconnected i.e., at t = 0.06 sec, the electromagnetic torque of the three-phase PMSM starts reducing with the speed and oscillates from 0 N.m to around 2 N.m till t = 0.09 sec. At t = 0.09 sec, PI controller starts working to compensate the phase loss, then the electromagnetic torque of the machine reduces from its value (at t = 0.09 sec) and comes to the normal value but oscillates between 0 N.m to 14 N.m. However, the average value becomes 7 N.m as was under healthy condition.

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200

150

Three Phase Currents(A)

100

50

0

-50

-100

-150

0

0.02

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(c) Figure 6.6. Performance of three-phase PMSM (a) three-phase stator currents (b) rotor speed and (c) electromagnetic torque.

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Figure 6.7. Line-to-Line voltage between phases ‘b’ and ‘c.’

Figure 6.7 shows the line-line voltage in between the phases ‘b’ and ‘c.’ From Figure 6.7, it is noticeable that up to t = 0.06 sec, the pulse widths were uniform but after t = 0.06 sec, the pulse width destabilized and again becomes stable (but with different pattern) at t = 0.09 sec when the PI controller starts operating.

Simulation of Five-Phase PMSM The complete simulation model of the five-phase PMSM is presented in Figure 6.8. The machine configuration and parameters are same as discussed for the three-phase PMSM. In the outer control loop, the reference speed is compared with the fivephase PMSM speed and the generated error signal is supplied to the PI controller. The sub-system of the PI controller is depicted in Figure 6.9. The values of the PI gains are set at 20 and 11, respectively. The minimum and maximum output limits are set as –150 to +150. The PI controller gives output (Iq1*) according to the input speed error signal which is fed to the dq to abc block. The PWM inverter model for five-phase PMSM is shown in Figure 6.10; where, it can be understood that the inverter output goes through the regulated voltage source block before it is supplied to the PMSM stator winding.

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Timer

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I_ref

Vc

C

Vd

D

Ve PWM Inverter

E 5-phase PMSM

dq to abcde

Figure 6.8. Block diagram of the five-phase PMSM simulation model.

Figure 6.9. PI controller (subsystem).

Figure 6.10. Sub-system of the PWM inverter for five-phase PMSM.

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At the start of PMSM, it will run in healthy phases. The faulty phase will be considered for two cases viz. firstly, phase ‘a’ open and secondly, phase ‘a’ and ‘b’ open. It will be discussed in the result and discussion section. After some set time PI controller will compensate for the loss due to open phases. The sub-system model of the timer is the same as shown in Figure 6.5 which will be used here for performance analysis of five-phase PMSM. In a fivephase machine, three timers are used to perform a specific function for the system. The changing signal is produced by the timer block at the particular time and this block is used to give a 0 or 1 signal, in order to control the closing and opening time of the relays. In a five-phase machine, two circuit breakers are used to open the phase - first to open phase ‘a’ and second to open phase ‘b’ at different time. This open phase fault analysis will be presented in the subsequent section.

Analysis of Five-Phase PMSM The same fixed load torque (7 N.m) is applied to the shaft of the five-phase PMSM. In the simulation model (see Figure 6.8), two control loops are used for the purpose of regulation. The inner loop is used to control the stator currents of the machine whereas the outer loop is implemented to control speed of the machine. The machine starts under all healthy phases (normal operation). The phase disconnection time is set by the timer block. A circuit breaker connected with phase ‘a’ will be open and closed as per the instruction issued from the timer block. At t = 0.06 second, phase ‘a’ of the machine will be disconnected. Another circuit breaker is used to open phase ‘b’ and time needed to open this phase is set to 0.12 sec. Another timer is used for fault command operation. In this timer, the time is set 0.09 sec. and 0.15 sec. It means at t = 0.09 second and t = 0.15 second, the current references of each controller are changed to compensate for the phase loss. The waveforms of three-phase stator currents, rotor speed and developed electromagnetic torque of five-phase PMSM are presented in Figure 6.11. It is evident from Figure 6.11 that at t = 0.06 sec., phase ‘a’ has been opened and at t = 0.09 sec., PI controller compensates for the phase loss. Observing the waveforms between 0.06 sec. to 0.09 sec., one may notice certain disturbances when phase “a” is open. The speed is decreased in between 0.06 sec. to 0.09 sec. The ripples in the electromagnetic torque can also be observed in Figure 6.11(c).

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Next case is when phase ‘b’ is also disconnected. The phase ‘b’ is disconnected in the five-phase machine at time t = 0.12 sec. At t = 0.15 sec, PI controller starts working to compensate for the two-phase loss. 200 150

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Figure 6.12. Line-to-Line voltage between phases ‘b’ and ‘c.’

The inverter voltage is presented in Figure 6.12; where, one may see that the rated DC voltage i.e., 311V is achieved in between phases ‘b’ and ‘c.’ From Figure 6.12, it is observed that up to t = 0.06 sec, the pulse widths are uniform but after t = 0.06 sec, unequal pulse widths are obtained due to phase ‘a’ open. Phase ‘a’ is disconnected at t = 0.06 sec and phase ‘b’ is also disconnected at t = 0.12 sec. PI controller compensates for phase loss after t = 0.15 sec. Therefore, in between t = 0.12 sec to t = 0.15 sec, distorted pulse widths are observed.

Conclusion At present, electric vehicles are being popularized and the emphasis is on using such vehicles. Similarly many other applications make extensive use of PMSM with efficient control. Five-stage PMSM in particular has several advantages over three-phase machines such as quick response with high accuracy without transient and no overshoot/undershoot. In addition, PMSMs are also widely employed in wind power conversion systems. In this chapter, the performance analysis of three-phase PMSM and five-phase PMSM drives under healthy as well as open phase fault conditions is presented and compared. Therefore, after observing resulting waveforms of five-phase PMSM and three-phase PMSM, it can be concluded that the five-phase machine gives better performance compared to three-phase PMSM. In addition, the fivephase PMSM has demonstrated high fault tolerant capability. Therefore,

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preference should be given to multi-phase machines due to their effective performance. In future, the application of five-stage PMSM in wind power conversion system would make the system more robust.

References [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

Bakhsh, F. I., and D. K. Khatod, “Application of variable frequency transformer (VFT) for grid interconnection of PMSG based wind energy generation system,” Sustainable Energy Technologies and Assessments, Elsevier, vol. 8, pp. 172-180, Dec. 2014. Bakhsh, F. I., and D. K. Khatod, “A new synchronous generator based wind energy conversion system feeding an isolated load through variable frequency transformer,” Renewable Energy, Elsevier, vol. 86, pp. 106-116, Feb. 2016. Bakhsh, F. I., and D. K. Khatod, “A Novel Method for Grid Integration of Synchronous Generator Based Wind Energy Generation System,” IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), IIT Bombay, India, pp. 1-6, 16-19 Dec., 2014. Quntao, A., Guanglin, W., & Li, S., “A Fault-Tolerant Operation Method of PMSM Fed by Cascaded Two-Level Inverters.” 7th IEEE Int. Conf. Power Electronics and Motion Control Conference, pp. 1310-1313, 2012. Islam, S., F. I. Bakhsh, M. Khursheed, S. Ahmad and A. Iqbal, “A novel technique for the design of controller of a vector-controlled permanent magnet synchronous machine drive,” 2011 Annual IEEE India Conference, pp. 1-6, 2011. Tabrez, M., F. I. Bakhsh and S. Al-Ghanimi, “Position Estimators of Sensor-less vector control of a Permanent Magnet Synchronous Machine,” International Conference on Intelligent Sustainable Systems (ICISS 2017), SCAD Institute of Technology at Palladam, Tirupur, India, pp. 375-378, 7-8 Dec. 2017. Tabrez, F. I. Bakhsh, M. Hassan, K. Shamganth, S. Al-Ghnimi, “A comparative simulation study of different sensorless permanent magnet synchronous machine drives using neural network and fuzzy logic,” Journal of Intelligent and Fuzzy Systems, IOS Press, vol. 35, no. 5, pp. 5177-5184, Nov. 2018. Siddiqui, K. M., F. I. Bakhsh, R. Ahmad and V. Solanki, “Advanced Signal Processing Based Condition Monitoring of PMSM for Stator-inter Turn Fault,” IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 2021, pp. 1-4, 2021. K. Rahman, Atif Iqbal, M. A. Alhitmi, O. Dordevic, Salman Ahmad, “Performance Comparison of SVPWM Techniques in a Dual Matrix Converter Fed Five-phase Open-End Load.” IEEE Access, vol. 7, pg. 12307-12318, 2019. Iqbal, A., Moinuddin, S., Ahmad, S., Mohammad Ali, Adil Sarwar, Kishore N. Mude. ‘Multiphase converters,’ in Rashid, M. H. (Ed.): Power Electronics Handbook (Elsevier, USA, 2018, 4th edn.), pp. 457-528.

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K. Moin Siddiqui, F. Ilahi Bakhsh, B. Kumar Chauhan et al. Siddiqui, K. M., Upadhyay, S. K., Singh, S., Srivastava, R. K., Babu, R. (2017). Performance and Analysis of PWM Inverter Fed 3 Phase PMSM Drive, i-manager’s Journal on Electronics Engineering. Vol. 8, No. 1, pp. 9-18, Sept.-Nov. 2017. Arafat, A. K. M., & Choi, S., “Comparison of Electrical Losses in an Inverter-Fed Five-Phase and Three-Phase Permanent Magnet Assisted Synchronous Reluctance Machine.” IEEE Int. Conf. on Applied Power Electronics Conference and Exposition, pp. 2847-2854, 2016. Nagamani, C., & Somanatham, R., “Design and Analysis of Traction Drive System for Hybrid Locomotives using 5- Phase Permanent Magnet Synchronous Machines as Traction Machines” i-manager’s Journal on Electrical Engineering, Vol. 10, No. 2, 27-45, 2016. Jonq-Chin, H., & Hsiao-Tse, W., “The Current Harmonics Elimination Control Strategy for Six-Leg Three-Phase Permanent Magnet Synchronous Machine Drives.” IEEE Trans. on Power Electronics, Vol. 29, No. 6, 3032- 3040, 2014. Shamsi-Nejad, W., Nahid-Mobarakeh, B., Pierfederici, S., & Meibody-Tabar, F., “Control Strategies for Fault Tolerant PM Drives Using Series Architecture.” IEEE Int. Conf. in Vehicle Power and Propulsion Conference, pp. 1-6, 2010. Mohd Said, N. A., Xiao, D., Dutta, R., & Fletcher, J. E., “Control Strategy of Postfault Operation in Dual Inverter-fed, PMSM considering Zero Sequence and BackEMF Harmonic.” 8th IEEE Int. Conf. on Power Electronics, Machines and Drives, pp. 1-6, 2016. Chunyang Wang, L. Y., Shi, H., Xin, R., & Wang, L., “Simulation of PMSM FieldOriented Control Based on SVPWM.” 29th IEEE Chinese Control and Decision Conference, pp. 7407-4711, 2017. Xia, C., Yan, Y., Song, P., & Shi, T., “Voltage Disturbance Rejection for Matrix Converter Based PMSM Drive System Using Internal Model Control.” IEEE Trans. Ind. Electron., Vol. 59, No. 1., 361-372, 2012. Liu, H., & Shihua, L., “Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer.” IEEE Trans. Ind. Electron., Vol. 59, No. 2, 1171-1183, 2012. Saeidabadi, S. & L. Parsa, “Model Predictive Control for a Five-Phase PMSM Drive using Four Leg Five Phase Inverter.” The 46th IEEE Annual of the IEEE Industrial Electronics Society, 18-21 Oct. 2020. Kassa, M. T. & D. Changqing, “Design Optimization and Simulation of PMSM Based on Maxwell and TwinBuilder for EVs.” 8th IEEE Int. Conf. on Electrical and Electronics Engineering, 9-11 April 2021.

Chapter 7

Investigation and Mitigation of Distribution-Side Power Quality Issues A. Sharma1,* and B. S. Rajpurohit2 1Rajkiya 2Indian

Engineering College, Bijnor, India Institute of Technology Mandi, Mandi, India

Abstract Power quality (PQ) is becoming one of the major concerns in power distribution system. Interconnection of distributed generation (DG) and renewable sources at the distribution side or at low voltage level makes the system worse. The huge economic loss and the cost of mitigation strategies give a need for the understanding of PQ problems and their mitigation process. This chapter discusses the issues of PQ present in the distribution side of the power system. A wide variation of the PQ events and their adverse effects require an understanding of its causes as well as mitigation techniques. The purpose of this chapter is to provide a detailed classification of PQ events based on standards. Available solutions for mitigation of various PQ events are also discussed. A detailed analysis on the control strategies used in shunt active power filters is thoroughly discussed. A comprehensive study on the mitigation of major PQ events is presented.

Keywords: Active power filter, flicker, harmonics, power quality, reactive power, static synchronous compensator (STATCOM)



Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Introduction “Power Quality” can be defined as the ability of a power system to create a noise free pure sinusoidal power supply, which is always stable in terms of voltage and frequency. However, many loads are known to produce continuous distortions on the waveforms of the power supply that deviate from the supply in the ideal condition [1]. The source of disturbance is not limited to loads or the distribution facilities. Figure 7.1 shows that the sources of power quality (PQ) problems vary from utility to load with the main reason being the use of power electronic devices. The magnificent power handling and control capabilities of power electronics devices increase their use throughout the power system industries from generation to distribution and load [2]. But these devices are controlling the power by modifying the waveform of ideal current and voltage, which introduces many PQ problems.

Figure 7.1. Major sources of power quality problems [5].

PQ problems cover a wide area, so problems are classified on various grounds to define it in all possible aspects. Various international standards such as IEEE and IEC are used to define PQ problems. Standards help to differentiate problems from normal to severe mode and solutions can be defined accordingly. Solutions to PQ problems also span a wider area, as solutions involving voltage disturbances and current disturbances need to be

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addressed differently [3, 10]. The devices used to mitigate PQ problems are also power electronics-based devices. The advantage of precise controllability of these devices makes them suitable for this purpose. Although the devices used for mitigation are power converters, the control techniques used by each vary greatly depending on the PQ issue that is to be targeted. In this chapter, a possible classification of PQ problems is made to include all aspects. Standards and related ratings are defined to correctly differentiate between various voltage and current distortions. The solutions used to reduce PQ problems are thoroughly discussed through schematic diagrams and working principle. The research area of the shunt active power filter is analyzed to see the various techniques used for its control to overcome various PQ problems.

Classification of Power Quality Problems As discussed earlier, PQ is about maintaining the voltage and current of the power system at rated magnitude and frequency along a sinusoidal pattern. Any deviation from the defined standards will compromise the efficiency of the system. In particular, the deviation of voltage magnitudes, transients in voltages and currents, harmonics in the waveform, etc. are defined as PQ problems [1]. Typically, the voltage is regulated to enhance the PQ of the system, as compared to the current, with advances in control methods of voltage [2].

Figure 7.2. Evaluation and solution of power quality problems [1].

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Poor power quality imposes financial burdens on the utility as well as the customer. This leads to unwanted changes in the voltage and current parameters, leading to loss of power and economy [3, 4]. Evaluation of the PQ problem is essential to avoid economic losses. Finding the optimal solution to the problem requires a process of evaluation as shown in Fig. 7.2. The process is conducted in five stages. In the first stage, problems are categorized into any of the following: flicker, harmonic distortion, voltage regulation, voltage sag and transient. After categorization, the problem is formulated by collecting the data to find out the characteristics, cause and equipment effect. In the third step, the solution to the problem is identified. In this stage, solutions are checked under various steps like equipment design, utility transmission system, end use customer interface or utility distribution system. The identified solutions are evaluated in the fourth step by conducting procedural analysis and technical alternatives to the solutions are also identified. In the fifth step, the optimal solution to the problem is worked out to achieve maximum economic outcomes.

Figure 7.3. Classification of PQ disturbances as per IEEE-1159 standards.

Two standards IEEE and IEC classify PQ problems. In Figure 7.3, the classification of PQ problems as outlined in IEEE Standards 1159 is shown. The standards have mainly classified the problem into 7 parts viz. flicker, long-term voltage fluctuation, short-term voltage fluctuation, voltage

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imbalance, waveform distortions, transients, and customer end devices. Voltage related problems are further classified into three parts, which are increase/decrease in voltage magnitude and supply interruption. Waveform distortions are mainly related to current distortions due to non-linear loads. The presence of DC offset, harmonics, inter-harmonics, notches and noises distorts the sinusoidal waveform of the current. A more detailed classification of PQ problems with causes and effects is shown in Table 7.1. "Voltage fluctuation" is a deviation in the amplitude of voltage due to heavy load switching such as in arc furnaces. Sustained fluctuations in voltage cause light to flicker, causing eye irritation, sometimes causing interruption of continuous process and sensitive equipment. "Voltage spikes" are a sudden increase in the magnitude of a voltage for a very short span of time. The time duration ranges from 1 cycle to less than a few seconds. Usually, lightning is the main cause of voltage spikes, but sometimes the switching of capacitors in power systems also causes spikes in the voltage waveform. Voltage spikes lead to loss of unsaved data and sometimes sensitive equipment is damaged or shut down. PQ problems related to changes in voltage magnitude can be classified as "voltage sag" and "voltage swells". Voltage sag is a condition when the magnitude of the voltage is below the rated level for a considerable duration [6]. The main cause of voltage sag is excessive network loading which reduces the voltage magnitude level. Other reasons are faults, inrush currents, inadequate wiring in some places, etc. When the magnitude of voltage increases from its rated value, the condition is known as voltage swell. Generally, when heavy loads are disconnected from the network the voltage swells and this leads to data loss and premature aging of the equipment [7]. "Long time voltage interruption" refers to the total interruption of voltage or current levels for a period of few milliseconds to one or two seconds [7]. Longer duration is caused by failures of insulation and protection devices. "Waveform distortion" can be described as the deviation of voltage or current from an ideal sine waveform of the fundamental power frequency. Deviations are caused by power electronics converters and equipment used as non-linear loads. The types of distortions are DC offset, noise, notches, harmonics and inter-harmonics. The effect of waveform distortions is saturation and overheating of the transformer as harmonics increase the effective value of current and voltage. "Power frequency variation" is the variation in the fundamental frequency of the power system. Heavy load switching is the main cause of frequency variation in power system networks. This affects the operation of motors and other frequency sensitive devices.

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Table 7.1. Classification of PQ problems Power Quality Causes Issues Voltage fluctuations Switching of heavy loads Voltage spikes

Lightning

Voltage sag

Faults in the system, excessive network loading, inrush current, source voltage variation, inadequate wiring Stop of heavy load, source voltage variation Control malfunction, insulation failure, failure of protecting devices Non-liner loads, noises, arc furnaces

Voltage swell Long time voltage interruptions Waveform distortions Power frequency variations Harmonics Noise Transient Flicker

Effects Over voltage /under voltage causes flickering of light Generally, data loss due to switching, stoppage or sometimes damage of the sensitive equipment Intermittent lock-up, fumbled data

Data loss, damage to equipment Malfunctioning in processing data

Saturation and overheating of the transformers Heavy load Affects motors and frequency sensitive devices Non-linear loads Heating in the equipment, fault switching of protective devices Improper grounding, electromagnetic Disturbance in the sensitivity of interference the equipment Lightning, power electronics Disturbance and false switching of commutation, RLC snubber circuit the devices Fluctuation of supply voltage, arc Irritations to eyes, damage the furnace sensitive equipment

"Harmonics" are the integer multiple components of the fundamental frequencies present in a current or voltage waveform. The presence of harmonics in current is mainly due to the use of non-linear loads [8]. With an increased value of currents, the equipment heats up. Due to harmonics the zero crossing of the waveform is changed which gives incorrect operation of the protective devices. "Noise" is the high frequency signals superimposed on power frequency signals. Improper grounding or electromagnetic interference with other devices creates noise in the power system waveform. The noise creates disturbances in sensitive equipment. The short-term instant change of energy is referred to as "transient". Transients can be observed in the voltage and current waveforms in the power system network when a surge occurs on the power system. Transients also lead to disturbances and false switching of devices [2]. "Flicker" is the variation of the voltage waveform from 90% to 110% of the rated value [9]. Fluctuations in voltage supply or heavy loads such

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as arc furnaces cause flicker in the voltage waveform. Effect of Flicker while using devices like computers, it irritates the eyes and sometimes it damages sensitive equipment.

Power Quality Standards PQ problem occurs when the deviation of voltage, current or frequency exceeds the prescribed limit. Standards for allowable deviation of voltage, current or frequency have been defined by various international agencies as given in Table 7.2. Table 7.2. Power quality standards and its guidelines Power Quality Standards IEC 61000-3-2 (1995-03) IEC 61000-3-4 (1998-10) IEC 61000-4-15 IEEE 519 IEEE Standards 141-1993 IEEE Standards 1159-1995

IEEE Standards 1250-1995 IEEE Standards P1546 IEEE Standards P1409 IEEE Standards P1547 IEEE Standards 1547a

Guidelines Defines allowable limits for emission of harmonics current for the equipment having rated input current < 16A [8, 10-12]. Defines allowable limits for emission of harmonics current for the equipment having rated input current >16A [13]. Characterizes the flicker [14]. Defines allowable limits of harmonics in current and voltages at the point of common coupling [10, 11]. Preservation of property, safety of life, voltage regulation in limits, maintenance and care, flexibility [15]. Impact of poor power quality on customer equipment and utility, definitions of power quality terminology, monitoring of power quality (AC systems), measurement of electromagnetic phenomenon [8, 16-17]. Instantaneous voltage disturbances in AC system, its effect on equipment and mitigation techniques [18]. Characterizing the performance of voltage sag [19]. Guidelines to improve power quality issues through custom power devices. [18, 20-21]. Interconnection of the distributed generation devices to the existing power system [20-21]. Defines guidelines to allow the equipment to tolerate voltage sag with improved stability [22].

The standards defined for current are basically injection of harmonics into the current. The extent of harmonics injection depends on the rated current carrying capacity of the power system network. Harmonics produce more distortion for lower rated current levels and vice versa for higher rated current

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networks. Therefore, there are some limits to the allowable distortions generated by the load or power converters at the substation. The point of common coupling is an important point because different types of loads are directly connected to this point. If any load at this point produces harmonics, the harmonics will interfere with the performance of other equipment connected to the same point. Therefore, an allowable limit of harmonics is described in the voltage and current at the point of common coupling. Acceptable solutions to various PQ problems are also defined in the standards. The solution to a particular PQ problem depends on the specific type of shunt or series equipment having some reactive or real power capacity. For voltage-related PQ problems, series devices (either passive or active) are used; whereas for current related PQ problems, shunt devices (either active or passive) are used. The control algorithm of the devices used for the solution plays an important role, as they decide which PQ problem is to be dealt with. The capacity of PQ mitigation device increases as the mitigation requirement increases. Therefore, the control algorithm is dedicatedly used to mitigate the specific PQ problem. Standards are defined for the integration of distributed generations without violating the limits of PQ. Integration of renewable sources also causes some PQ problems due to their intermittent nature. Hence the guidelines are defining to integrate renewable without violating the stability of power system network along with PQ problems.

Proposed Solutions to Power Quality Problems The nature of PQ problems is different from each other, so their mitigation process is also different. Various solutions like STATCOM, Unified Power Quality Conditioner (UPQC), Static VAR Compensator (SVC) etc. are used by the power industry. Historically, spinning reserves (SRs) have been used to compensate for unexpected imbalances between load and source generation [23, 24]. The SR depends on the capacity of the generator to supply power in excess of its rated value when additional torque is applied. This scheme does not work with present renewable sources. Distribution Static Compensator (DSTATCOM) is used to improve power quality by mitigating current harmonics and correcting power factor. It also helps to regulate the voltage on the distribution bus and performs load balancing [25, 26].

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The D-STATCOM is a shunt connected custom power device also known as an active power filter with a shunt connected voltage source converter (VSC) [27]. Figure 7.4 shows the schematic diagram of a D-STATCOM used to enhance power quality at the point of common coupling and connected to the power system network through passive L-R filters. Control is done using dedicatedly designed control algorithms. These algorithms are used to generate harmonics components that are shifted 180 degrees to compensate for the harmonics injected by the load. Voltage regulation, power factor correction and load balancing are also performed. Pulse width modulation (PWM) technique is used to generate pulses for the D-STATCOM via a control algorithm.

Figure 7.4. Schematic diagram of D-STATCOM connected for power quality enhancement [25].

Figure 7.5. Schematic diagram of UPQC connected to the power system [28].

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Figure 7.6. Circuit diagram of UPQC connected to the power system.

In UPQC, a combination of series and shunt active filters are used, they are connected with each other in a back-to-back topology and share the same DC capacitor. Series filters are generally used to correct supply side disturbances, i.e., voltage sag/swell, unbalance, harmonics and voltage flicker. Shunt filters mitigate poor PQ problems at the load side i.e., load current harmonics, unbalance and power factor correction. The UPQC maintains the load voltage at the rated value by supplying additional voltage through series compensator. The shunt compensator injects current into the system to make the supply an in-phase balanced current [28]. Figure 7.5 shows the schematic diagram of the UPQC connected to the distribution side network of the power system and Figure 7.6 shows the equivalent circuit for the same. The unbalanced voltage of a three-phase system is expressed in (7.1). Vs (t) = Vs(+ve) (t) + Vs(−ve) (t) + Vs(zero) (t) + ∑ Vsh (t)

(7.1)

Where, Vs(+ve) is the positive, Vs(−ve) is negative, Vs(zero) is zero sequence component and Vsh is the harmonics component of the voltage. In case of any unbalance, the negative and zero sequence components will be non-zero components. The series converter supplies the difference between the load and source voltages as mentioned in (7.2). V0 (t) = VL (t) − VS (t)

(7.2)

Similarly, the non-linear load current can be formulated as (7.3) IL (t) = IL(+ve) (t) + IL(−ve) (t) + IL(zero) (t) + ∑ ILsh (t)

(7.3)

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The harmonics, negative and zero sequence components of the load currents are compensated by shunt active power filter. The current supplied by the shunt converter is given by (7.4), which is the difference between the load current and the source current. Generally, the active power component of the load current is supplied by the source and the reactive component is supplied by the filter to reduce the additional burden of supplying reactive power by the source generator. This basically solves two purposes - the need for a higher capacity generator on the source side, and reduces the thermal capacity limit of transmission cables. I0 (t) = IL (t) − IS (t)

(7.4)

Uninterruptible power supplies (UPS) are widely used in low power applications to provide power back-up to telecommunication devices and computers to protect against data loss. UPS has a bidirectional power conversion principle. In the presence of a power supply, they convert incoming AC power into DC, which is used to charge the battery [27]. When the power supply is turned off, the UPS converts the DC into high quality AC power. This scheme works in low capacity due to heavy storage requirements. Previously, this methodology was used for power factor correction [29]. A reduced switch count configuration based modular per-phase system topology is proposed in [30]. The topology proposes enhanced filtering as well as faster transition from normal to back-up mode. The topology removes the circuit breaker as it provides isolation from the grid to prevent the flow of backward current from the UPS into the grid [30]. Another PQ issue i.e., transient voltage is suppressed by Transient Voltage Surge Suppressor (TVSS). It has a simple structure to operate in alliance between power loads and sensitive loads. It serves to limit the magnitude of the transient voltage to the safest value. The TVSS has a nonlinear resistance (preferably a Zener diode) to limit the high magnitude voltage and conduct it to ground. In [31], a TVSS system is discussed with a shunt protector capacitor on both the input and output sides. Isolation transformers are also used to isolate sensitive equipment from transients and noises. The isolation transformer uses a non-magnetic grounded shield to provide isolation between the primary and secondary side. When the transient comes on to the primary side it transfers to the capacitor shield which transmits it to the ground and saves the secondary side from transient. The isolation transformer improves neutral to ground voltage due to the capacitor but it does not have the feature to protect the system during voltage fluctuations [32, 33]. For

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mitigation of voltage sag/swell, dynamic voltage restorer (DVR) is used. DVR is used to stabilize the voltage across the load terminals. It consists of a voltage source in series with the load and a step-up transformer to make the voltage constant. It also injects active and reactive powers through voltage converters at the output terminals [34]. Varistor, surge protector and capacitor are used for over-voltage protection. The time duration of a voltage surge ranges from 15 milliseconds to half a second. If the duration of the surge is more than 2 seconds then it is known as over-voltage. The main causes of surges are heavy load switching in power network. Metal oxide varistors (MOVs) have disc-like shapes and are made of a ceramic-like material. They have a composition of zinc oxide and suitable additives. The MOVs form a low ohmic path for the flow of surge current. They have the ability to work fast and show high clamping voltage. The surge energy absorbing capacity of the MOV can be enhanced by increasing the size of the disc. Some newer MOVs can prevent smoke, overheating, and potential fire [35].

Figure 7.7. Configuration of static transfer switch (STS) [27].

Static Transfer Switch (STS) is generally used to switch the load from the original source to any other available source in case of voltage disturbance. It is used in case of voltage dips and interruptions [27]. The STC consists of two three-phase static switches which are connected to the same output through two anti-parallel diodes as shown in Fig. 7.7. During normal operation, the primary source is connected to the output, but in case of any disturbance, the

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second source is connected to the output. It is a low-cost method of reducing voltage variation as compared to other methods. For the control of AC voltage by absorption and generation of reactive power through passive elements, an SVC is used. SVC can be used for flicker reduction by absorbing changes in reactive power. It is also used to balance unsymmetrical loads [27]. As shown in Figure 7.8, the SVC consists of a thyristor-controlled reactor (TCR) and a few thyristor switched capacitor (TSC) branches [36].

Figure 7.8. Static VAR compensator [27].

In the TCR branch, the inductive reactance is continuously controlled by controlling the firing angle of the thyristor. The capacitors of the TSC branch are turned on or off only at the zero crossing of the current to remove the inrush current in the capacitor. SVC is used to generate reactive power in a specified range. The rating of TCR is equal to that of one of the branches of TSC branches. The response speed of the SVC is limited by its configuration; therefore, it is not able to completely correct the highly variable flicker [27]. Figure 7.9 provides frequency control in power systems. It operates in three modes viz. primary, secondary and tertiary [3]. The control of frequency from the load side is not considered in this concept. Primary control is automatic. In this mode, reserves are used to supply power to overcome changes in frequency. Secondary mode provides back-up power control for frequency deviation. Tertiary mode is used when the secondary mode is not sufficient to overcome the frequency deviation. In this mode, the transmission system operator controls the congestion and returns the frequency to its previous value.

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Figure 7.9. Scheme for frequency control [3].

Power Quality Enhancement Enhancement of power quality is a vast topic, involving many devices used for voltage and current corrections. Shunt devices are used for mitigation of harmonics and other current related PQ problems, whereas series devices with appropriate real power support are used to mitigate voltage related PQ problems. Shunt devices such as Shunt Active Power Filters (APFs) have found many applications in PQ enhancement. With the emergence of computationally intelligent techniques, APF's control algorithms have been intensively researched to improve its performance. In this topic, the working of APF will be discussed. The techniques used to generate compensation signals for APF will be discussed. Light flicker is a major concern in building power system standards. Electric arc furnaces and other heavy loads are known to be a common cause of PQ problems [57]. In this section, the mitigation techniques used for light flicker are reviewed.

Active Power Filters for Mitigation of Distribution-Side Power Quality Problems The concept of active filtering of distortion of power distribution lines comes from the 1970s [37-40]. Many researchers have done extensive research on

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the configuration, function and signal processing of APFs. In [41], APFs are classified on the basis of (i) the time or frequency domain signal (ii) the current or voltage type converters used. In addition, [42] reviews APF technologies in terms of configuration, objectives, and controllers. In [43], Peng reviewed shunt and series APF and concluded that shunt filters are used to mitigate current distortions while series filters are used for voltage distortion problems [44]. In this chapter, attention is paid to the control algorithm of the APF which is used to inject compensating currents into the line. APF control includes several functions such as reference current generation, control of current and DC bus voltage control. Figure 7.10 shows the main diagram with the basic elements of an APF as follows: •

• •





Distortion Identifier: A signal processing unit to form a reference waveform 𝑟(𝑡) from the distorted waveform d(𝑡), with the aim to reduce the distortion. STATCOM: A power electronics converter with the ability to reproduce the reference waveform of current, 𝐼𝐴𝑃𝐹 . STATCOM Controller: Current and voltage control loops with pulse width modulator or hysteresis controller to generate controlled pulses for STATCOM. Synchronizer: A signal processing unit to ensure the synchronization of the STATCOM waveform with the point of common coupling voltage. DC Bus: A capacitor to supply the controlled reactive power to the load.

The distortions produced by non-linear load or distorted load 𝑑(𝑡) is identified by a distortion identifier, and a reference signal 𝑟(𝑡) is sent to the STATCOM controller. The controller uses a signal processing unit to generate the pulses for the STATCOM through pulse width modulation of hysteresis current control technique. STATCOM injects reactive power, anti-harmonic current, unbalanced current, etc., depending on the controller output, through the DC bus. A synchronizer, typically a phase locked loop or unit template, is used to synchronize the generated current and voltage waveforms of the STATCOM at a point of common coupling voltage. APF is used to fix the following power quality problems:

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• • • • •

Harmonic distortions in current Flicker Reactive power Negative sequence fundamental components (unbalanced components) Zero sequence fundamental components (neutral line current)

APF is commonly used for the correction of harmonic distortions with compensation for fundamental frequency reactive power [45]. In a three-wire system, correction of the negative sequence fundamental component is performed to correct the unbalance condition [46]. In a four-wire system, zerosequence fundamental and zero-sequence harmonics may flow, which is compensated for by the four-wire APF [47]. DVRs are commonly used to mitigate flicker, but the controllability of the APF allows it to mitigate the flicker efficiency [48].

Figure 7.10. Schematic diagram of APF [41].

In the various PQ problems discussed above, the problem to be corrected by the APF is decided by the control unit. Each PQ problem correction contributes to an increase in the capacity of the APF, therefore increasing the economic burden. For example, compensation for unbalance and flicker requires large DC side capacity of an APF, because variations in instantaneous power have to be considered. The scheme used to compensate for harmonics can compensate for some of the inter-harmonics, thereby reducing flicker. But the compensation for flicker increases the capacity of the APF. Therefore, the effectiveness of a control algorithm is determined only by its ability to compensate for the PQ problem for which it is designed.

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The performance of control algorithms depends on the ability to detect distortions and provide compensation for distortions. Two categories have been defined for compensation: 1. Waveform compensation: In this compensation, the objective lies to reduce a supply current with only fundamental active current component. 2. Instantaneous power compensation: In this compensation, the objective lies to deduce a constant instantaneous power from the supply. A detailed explanation of the above-mentioned categories is explained in the following sub-sections.

Waveform Compensation The approach behind wave compensation is to decompose the different frequency components of the distorted current using some signal processing techniques, then separate the component that is to be compensated from other components. This task can be easily done by filtering methods but for real applications the non-ideal properties of the filter must be considered when using this approach. Other methods that can be used are pattern learning and adaptive filtering.

Filter Based Method The filtering of the signal can be done in time-domain or Fourier based frequency domain for distortion identification. The filter performance is evaluated from the following parameters: 1. Attenuation––ability to attenuate the stop band signals heavily and to preserve the magnitude of the pass band signal. The transition period between the pass band and stop band is small. 2. Phase-distortion––ability to preserve the phase of the signal, as the cancellation signal is produced in phase opposition of the original signal. 3. Time response––the filter should be able to reproduce the distorted load current as it is subjected to change frequently.

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There is direct and indirect identification of the distortion factor. In direct method, the filter is used to directly determine the distortion by working in transient domain. In indirect identification, the filter determines the fundamental signal 𝑓(𝑡), which is subtracted from the instantaneous distorted signal 𝑑(𝑡) to produce the reference signal 𝑟(𝑡), as shown in Figure 7.11.

(a)

(b)

Figure 7.11. (a) Direct identification (b) indirect identification.

Direct identification methods are not suitable in case of transient, because it introduces a time delay between the measured filter’s output and the reference value to be identified. Hence, an error comes in distortion calculation. In indirect identification method, while the distortion term is cancelled, the real power is exchanged through the APF. The exchange of real power increases the capacity of the APF due to variation in the DC bus voltage. One more difference lies between direct and indirect identification-based compensation. In direct compensation, a particular band of the signals has to be compensated, while in indirect compensation the signal apart from the retention signals has to be compensated. Frequency domain filtering also finds application in control algorithm of APF. It requires discrete or fast Fourier transform (FFT) of the distorted signal, which is sampled at more than twice of the maximum valued frequency component present. In frequency domain filtering, the transition period is nil and complete cut off can be achieved. Pass band ripple and phase distortions can also be minimized if synchronization, phase window and buffer length values are chosen correctly. One of the major disadvantages of frequency domain filters is that they are not real, hence for sampling, sufficient time must be elapsed. As mentioned in [49], the signal use for sampling must be steady and periodic. The FFT determines the periodicity of the sampled waveform, and the FFT window should be synchronized with the fundamental signal to accurately measure the phase and magnitude of the signals. If the window size does not match the integer number of fundamental signal, then spectral leakage occurs and accuracy suffers. Hence, in frequency domain filtering method, the selectivity of Fourier method plays an important role [46].

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Figure 7.12 shows the direct and indirect identification methods of filtering in frequency domain. The fundamental component is made zero in the direct method, and the cancellation reference is set for harmonic distortions. If both the harmonic distortion and reactive power is to be cancelled, then real power component is made zero. In indirect filtering, the harmonic distortion is made zero and the fundamental component is subtracted from the instantaneous value to generate the reference signal. In case, if the imaginary fundamental component is made zero then the subtraction of the fundamental from instantaneous signal does not solve the purpose and the component will be compensated by APF. In [46], the direct method is used to separate the individual components. In certain harmonics, the compensating and load currents are compared and integrator-based error correction is used. While in other cases where a chance of resonance in electrical system persists, the open loop control is used.

Figure 7.12. (a) Direct method and (b) indirect method of filtering in frequency domain.

The frequency domain filtering is not accurate in case of transients as the periodicity is lost, the presence of time delay in the filter adversely affects the distortion identification. A cycle of sampled data is temporarily stored in the buffer and then processed in the next cycle during which the next cycle data is stored in the buffer. The data is received at the output during de-buffer stage. Hence, two complete cycles of sampled delay are common in frequency domain filtering process. Shortening the time delay is possible when the FFT is performed for each half cycle of the present data and half cycle of the previous data. This leads to a time delay of only one complete cycle.

Heterodyne Method Another method of waveform identification is known as the heterodyne method. In this method, a sinusoid is multiplied by the distorted signal. If the

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sinusoidal signal has a fundamental frequency, the multiplication leads to a DC term that can be filtered out by a low-pass filter and another is a double frequency term. If the sinusoidal signal is in phase with the system voltage, the multiplication leads to fundamental component of the active current. The quadrature sinusoid gives the fundamental reactive current. This signal processing method is very common in single-phase systems [50]. In a three-phase system, phase-locked loop (PLL) is widely used to perform heterodyne distortion identification. The PLL locks the fundamental component of the sinusoidal system voltage [51]. The filter elects to pass the present DC term and additionally rejects the present double frequency term of the multiplied waveform. Higher order filters will have longer step response while lower order filters will have poor waveform separation as well as current cancellation properties. In [51], a closed loop identification with integral action is done, which gives a system to respond in 2–3 main cycles. In Discrete Fourier Transform (DFT), a heterodyne operation is performed for each harmonic and then averaged and filtered for all unwanted signals. The heterodyne method is typically used for a small number of distorted components. In [52] proposes a recursive DFT in which a moving average method is used instead of a simple average for heterodyne. In [53], heterodyne filtering is applied to adaptive filtering. In adaptive filtering, the adaptive element has a heterodyning function that is used to extract the fundamental term, which is then subtracted from the load current to generate a cancellation reference signal.

Pattern Learning and Identification Because of the time delay in the frequency domain and the error in the time domain method (due to filter limitations in the distortion identification process), pattern learning is used. In this approach also, direct and indirect methods can be used. In [54], harmonic identification is done through a two-layer neural network, which is commonly used for the estimation of Fourier coefficients of the distorted waveforms. Primarily the application of neural network in APF is done to achieve a target voltage distortion level through suitable correction current in [55]. Due to the presence of multiple distorted loads at the distribution side, it was not possible to measure the load current. Therefore, the presence of a harmonic component in the supply voltage is determined via the conventional method and the total conductance of the network is used to obtain the required current. The end points/outcomes of the procedure were used as a training current for the neural network. In the process, a separate

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network is required for each harmonic frequency. In [56], the proposed method is tested and it was confirmed that the neural network-based pattern identification takes 10-15 cycle to identify the new distortion. It was suggested to combine both traditional and neural network, in which traditional method is used to identify the harmonics in the load current and neural network to operate on voltage error. In the direct mode of application, the neural network was trained to identify the 3rd and 5th harmonic cosine functions from the load current [57]. The network is small as the calculation is less which leads to better accuracy and reduced time delay. Fourier series terms are calculated using artificial neural network (ANN). The fundamental active power is calculated to deduce the fundamental reference current. In indirect method of distortion identification, the neural network is used to track all terms of harmonics series. But only the fundamental term is used. In this method, the predicted load current is compared to the actual/measured load current at each sample value and the error is used to update the weights of the neural network. The sampling rate used was high as the full harmonic series was calculated. Therefore, the real-time implementation of the algorithm was questionable.

Instantaneous Power Compensation In this approach, instantaneous active and reactive powers are calculated through various transformations (𝛼𝛽0, 𝑑𝑞0). The instantaneous power calculated is filtered out to get the active and reactive power components of the current which is used to generate the reference current. The schemes described above are specifically for shunt APF. Due to the advantages of adaptive filter-based methods, they are continuously a topic of research in this field.

Artificial Intelligence Based Control Algorithm In the present scenario, the role of artificial intelligence (AI) technologies has increased in the form of control algorithms. The adaptive nature of AI techniques provides a flexibility to intelligently control the operation. AI based methods have the ability to adapt to changes in system operation with every cycle of operation. The basic methods used in active power filtering through AI techniques are to avoid PLL for synchronization and use a voltage

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waveform instead. The active and reactive power components of the load current are extracted and used to produce the pulses of the D-STATCOM. With the advantage of adaptive nature, algorithms exist which can have adaptive nature with noise rejection capability [58, 59]. The noise rejection property of the algorithm makes it work even in noisy conditions.

Light Flicker Mitigation through STATCOM Controllers for passive compensators such as the TCR and others can only be updated to the power system's fundamental frequency, so they are unable to compensate for transients or flicker [60, 61]. However, the controller of a PWM based VSC can be updated in real time [62] to a frequency of as low as 1kHz, which can be useful to correct the rapidly changing nature of flicker. The STATCOM application for light flicker mitigation is reported in the literature [63]. The capacity required to mitigate the flicker is high due to a compensation of large frequencies of inter-harmonics [64]. The control algorithms used for STATCOM are the same as discussed above. Waveform compensation techniques are commonly used for the device. The instantaneous power theory is also reported in the literature, but does not find a successful application due to the limitation of working only in balanced condition. The commissioning of an electric arc furnace usually results in a slight unbalance in the system [65]. Battery energy storage systems with STATCOM are also considered as an alternative to mitigate voltage flicker, as the voltage control provided by the battery energy system will control the voltage fluctuations that occur during flicker. Faster operation of batteryoperated systems is possible due to the fast control action of STATCOM [62]. Small signal stability analysis is not a new topic, but the emergence of impedance based small signal stability analysis is a new topic that is attracting attention nowadays. Stability analysis is needed to get scope for future expansion. Several internal control loops exist in STATCOM, mainly voltage and current control loops. The effect of individual loops needs to be analyzed to rule out the possibility of system imbalance.

Conclusion The problem of PQ is discussed in depth in the chapter. A detailed study has been done on the sources of PQ problems, which shows that most of the

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current related problems have resulted from non-linear loads and voltage related problems through the utility. PQ events vary from one to another. The individual PQ problem is assessed; it gives the methodology to be adopted to mitigate them. A detailed analysis of the equipment used for mitigation along with the internal control strategy is presented in the chapter. Current related PQ problems are generally mitigated by using shunt filters and voltage related problems are dealt with by use of series filters. A detailed study on static and active filters used for PQ problem mitigation is presented. Control strategies play an important role in the use of mitigation devices. This chapter emphasizes the state-of-the-art control techniques used for these devices. A more focused analysis on distribution side PQ problems and mitigation of light flicker is also presented.

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A. Sharma and B. S. Rajpurohit Malesani, L., L. Rossetto and P. Tenti, “Active filters for reactive power and harmonic compensation,” IEEE Power Electronics Specialist Conference, pp.321330, 1986. Allmeling, J. H., “A control structure for fast harmonic compensation in active filters,” IEEE Power Electronics Specialist Conference, pp. 376-381, 2002. Nabae and M. Yamaguchi, “Suppression of flicker in an arc-furnace supply system by an active capacitance-a novel voltage stabilizer in power systems,” IEEE Transactions on Industrial Applications, Vol., 31, No. 1, pp. 107-111, 1995. Rudnick, H., J. Dixon and L. Morán, “Delivering clean and pure power,” IEEE Power & Energy Magazine, Sept./Oct. pp. 32-40, 2003. Mariethoz, S., and A. C. Rufer, “Open loop and closed loop spectral frequency active filtering,” IEEE Transactions on Power Electronics, Vol. 17, No. 4, pp. 564573, 2002. Jou, H. L., J. C. Wu and H. Y. Chu, “New single-phase active power filter,” IEE Proceedings, Electric Power Applications, Vol. 142, No. 3, pp. 129-134, 1995. Tepper, J. S., J. W. Dixon, G. Venegas and L. Morán, “A simple frequency independent method for calculating the reactive and harmonic current in a non-linear load,” IEEE Transactions on Industrial Electronics, Vol. 43, No. 6, pp. 647-653, 1996. Rechka, S., E. Ngandui, JianhongXu and P. Sicard, “A comparative study of harmonic detection algorithms for active filters and hybrid active filters,” Thirtythird Annual IEEE Power Electronics Specialists Conference. Proceedings, Cairns, Australia, pp. 357-363 vol.1, 2002. Lou, S., and Z. Hou, “An adaptive detecting method for harmonic and reactive currents,” IEEE Transactions on Industrial Electronics, vol. 42, No. 1, pp. 85-89, 1995. Osowski, S., “Neural network for estimation of harmonic components in a power system,” IEE Proceedings on Generations, Transmissions and Distributions, vol. 139, No. 2, pp. 129-135, 1992. Kumamoto, A., T. Hikihara, Y. Hirane, K. Oku, S. Tada, K. Mizuki and Y. Ogihara, “Suppression of harmonic voltage distortion by neural network controller,” IEEE Industrial Applications Annual Meetings, pp. 754-761, 1992. Round, S. D., and N. Mohan, “Comparison of Frequency and Time Domain Neural Network Controllers for an Active Power Filter,” IEEE IECON ‘93, vol. 2, pp. 10991104, 1993. Pecharanin, N., H. Mitsui and M. Sone, “Harmonic Detection Using Neural Network,” IEEE International Conference on Neural Networks, pp. 923-926, 1995. Sharma and B. S. Rajpurohit, “Maximum Versoria Criteria Based Adaptive Filter Algorithm For Power Quality Intensification,” IEEE 9th Power India International Conference (PIICON), SONEPAT, India, 2020, pp. 1-5. Sharma, B. S. Rajpurohit & S. Agnihotri, “Affine projection sign algorithm based control for mitigation of distribution side power quality problems,” Energy Conversion and Economics. doi: 2. 10.1049/enc2.12035. Jervis, W. B., “An assessment of power system voltage disturbances in terms of lamp flicker perception,” Central Electricity Generating Board, UK, 1982.

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Virulkar, V. B., and M. V. Aware, “Flicker Detection, Measurement and Means of Mitigation: A Review,” Journal of the Institution of Engineers, India Ser. B 95, 149– 162, 2014. Larsson, T., “A PWM-operated voltage source converter for flicker mitigation,” European power electronics conference EPE, Trondheim, Norway, pp. 3. 1016– 3.1020, 1996. Clouston, J. R., J. H. Gurney, “Field demonstration of a distribution static compensator used to mitigate voltage flicker,” IEEE Power Engineering Society Winter Meeting 2, 1138–1141, 1999. Reed, G. F., Greaf, J. E., Matsumoto, T., Yonehata, Y., Takeda, M., Aritsuka, T., Hamasaki, Y., Ojima, F., Sidell, A. P., Chervus, R. E., & Nebecker, C. K. “Application of 5 MVA, 4.16 kV D-STATCOM system for voltage flicker compensation at Seattle Iron and Metas,”IEEE Power Engineering Society Winter Meeting 3, 1605–1611, 2000. Chen, S., and G. Joos, “Direct power control of DSTATCOMs for voltage flicker mitigation,” IEEE Industry Applications Conference, Chicago, IL, USA, vol. 4, pp. 2683-2690, 2001.

Chapter 8

Enhancement of Power Quality in Microgrid Using Optimized PV-Based DSTATCOM Alok K. Mishra*,1, Suvendu M. Baral2, Somya R. Das3, Prakash K. Ray2, Tapas K. Panigrahi3, Asit Mohanty2 and Akshaya K. Patra1 1Department

of EEE, SOA University, Bhubaneswar, Odisha, India of EE, OUTR, Bhubaneswar, Odisha, India 3Department of EE, PMEC, Berhampur, Odisha, india 2Department

Abstract This work proposes a novel metaheuristic optimised controller for a photovoltaic based Distribution Static Compensator (DSTATCOM) to mitigate voltage sag and Total Harmonic Distortion (THD) in a microgrid. The control scheme of the DSTATCOM is governed by the practically used Synchronous Reference Frame (SRF) Controller. Two Proportional Integral (PI) controllers are used to regulate and track the output of the control scheme. Earlier practises imply that the trial and error method was used for the determination of the controller gains, which is tedious and erroneous. This paper proposes a recently established metaheuristic optimization technique called the Dragonfly Algorithm (DA), for determining the optimised values of the controller parameters. The obtained results imply that the optimised control system produces a better control scheme in harmonic mitigation and voltage profile maintenance. The results were compared with another wellaccepted optimization scheme, Particle Swarm Optimization (PSO). The simulation results reveal that the DA optimised SRF controller is

*

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

.

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Alok K. Mishra, Suvendu M. Baral, Somya R. Das et al. successful in improving the power quality of the microgrid. The proposed work was simulated entirely in Matlab/SIMULINK and the characteristics plots as well as the THD graphs are vividly shown.

Keywords: DSTATCOM, microgrid, PSO, dragonfly algorithm, SRF

Introduction In recent years, the weakening of the quality of power has been the foremost cause of apprehension for the utilities and also for the customers. This phenomenon of degrading power quality also puts a doubt on the reliability as well as the security of the system. The accumulative use of unbalanced and nonlinear loads over the past few decades has contributed a lot to the degradation of the quality of power supplied to the customers, causing hostile effects. The presence of various nonlinear loads in the distribution system causes various power quality problems like waveform distortion and high demand for reactive power, and also affects the general functioning of electrical equipment. Static power converters and adjustable speed drives are some of the common loads that come under the category of nonlinear loads. Phenomena like sag, swell, load sheds, blackouts, and flickering are common occurrences in a large interconnected power system. The use of nonlinear loads all over the power sector plays a major role in the injection of harmonic components which hamper the power quality and reliability to a large extent. Poor power quality and an increasing number of such abnormalities can also incur a substantial loss in terms of product and energy as well as cause instrument damage. Thus, in recent years, it has been a compulsion to be educated with the understanding of the power quality of the grid and some standard norms such as the IEEE-519 standard to be able to prevent malfunctioning and abnormalities due to sudden power quality deviations [1, 2]. One of the main members of this class of devices is DSTATCOM (Distribution STATCOM). It can basically be said as a solid state device, possessing a fast response time and is capable of providing efficient voltage control at the Point of Common Coupling (PCC) and contributing to improving the power quality of the system. The exchange of active and reactive power with the distribution system is done mainly by the alterations of the amplitude and phase angle of the inverter voltage with respect to the PCC [3, 4].

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An extensive literature review has been done on DSTATCOM over the last decades. A comprehensive study of different DSTATCOM configurations has also been proposed by Bhim Singh et al., [5]. The effect of state feedback control of multilevel inverters on DSTATCOM applications was clearly stated by A. Ghosh et al., [6]. A unified approach to mitigating voltage sags and voltage flicker by the use of Kalman filter techniques and their derivatives was proposed in [7] by A. Elnady et al. In [8] author examines the performance of a PV-powered DSTATCOM. The nonlinear modeling of a DSTATCOM with a PV cell as a DC supply for power quality improvement is vividly illustrated in [9]. In this proposed work, we have tried to implement a photovoltaic based DSTATCOM governed by the SRF of control. The gain constants of the two PI controllers in the SRF control scheme are optimised using Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA) [10-12]. PSO as proposed by Eberhart has previously been implemented in optimising the controller gains in the case of an electric ship power system [13]. PSO has also found its contribution in automatic generation control (AGC) in the form of craziness factor [14]. In paper [15], S.P. Ghoshal clearly illustrated the technique for optimization of the controller gains in automatic generation control using the PSO. This paper tries to put through a comparative study of the effectiveness of the two optimization techniques. The main motive of the proposed work is: a) Improvement of power quality by reducing the THD levels in the PCC region. b) Maintaining the voltage profile by mitigating voltage sag in the system and bringing the voltage back to the previous level.

System Modeling The system modeling starts with the detailed modeling of the DSTATCOM, the photovoltaic cell.

Figure 8.1. Equivalent circuit diagram of the entire system.

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DSTATCOM The dynamic equations of the system can easily be obtained from Figure 8.2 as follows: di1 j dt

D j V pv

=

dV pv

L

=

dt

i pv Cdc





Vsj R iij − L L

D j i1

(8.1)

(8.2)

Cdc

where j represents all the three phases a, b and c. The transformation to the d-q reference frame gives us the following set of equations: Dd V pv Rd di1d V = − i1d − sd + i q dt L L L

di1q dt dVpv dt

=

Dq Vpv

=

L



Vsq Rd i1q − − ωi d L L

1 1 i pv − [Dd i1d + Dq i1q ] Cdc Cdc

(8.3)

(8.4)

(8.5)

where i1d, i2d, i1q, i2q are the d-q axis currents of the VSI inverter and the filter currents, respectively. D stands here for the duty cycle functions. The above equations, when written in state space representation, can be written as:  di1d   − Rd  dt   L  di    1q  =  −   dt    dV pv   − Dd     dt   C dc

 − Rq L − Dq C dc

− Dd     L   i1d  Vsd   0  − Dq    1    i1q + Vsq + 0 i pv L    L    1   0     V pv   C dc  0  

(8.6)

Enhancement of Power Quality in Microgrid …

− Rd   L  where A=  − ω − D d  C  dc

ω − Rq L − Dq C dc

197

− Dd   L  − Dq  is called the system parameter matrix. L   0  

In this case, i pv acts as the input parameter which in turn controls the PV voltage Vpv that maintains constant voltage across the DC capacitor.

PV Cell The DSTATCOM can be modelled as a VSI with the input being an energy storage device, mainly a capacitor that can facilitate the real power exchange. The DC capacitor has an initial value. During the control scheme, the DC capacitor discharges or charges itself depending upon the injection or acceptance of reactive power and again comes back to a constant value. In order to incorporate this concept, the control scheme of DSTATCOM should have the scope to track the DC voltage error. Mathematically, a solar cell can be represented by the double diode model as shown in Figure 8.2.

Figure 8.2. Two-diode equivalent diagram of a solar cell. I = I ph − I s1[e

q ( V+ IRse ) kT

− 1] − I s 2 [e

q ( V+ IRse) AkT

− 1] −

(V + IRse) Rsh

(8.7)

where Iph is the solar cell current which is governed by irradiance and temperature. Is1 is the saturation current of the diode which is mainly caused

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due to the effect of diffusion. Is2 is the saturation current which is mainly caused by the recombination of the space charge. Rsh represents the shunt resistance responsible for sending the leakage current to ground. Rse represents the series resistance, k represents the Boltzmann constant, q represents the charge of an electron and V denotes terminal voltage of the solar cell.

Control Technique Used There are a large number of control schemes available, such as instantaneous reactive power theory (IRPT), synchronous reference frame theory (SRFT), and unity power factor (UPF), that are mainly concerned with the control of DSTATCOM. These are based, instantaneous symmetrical components are based, etc. In this paper, the SRFT control scheme of the DSTATCOM is suitably discussed and clearly shown in Figure 8.3.

Figure 8.3. The Block Diagram of the SRF control scheme of DSTATCOM.

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Step 1: Similar to the p-q control scheme, the three phase components are first converted to α-β frame of reference. After this step considering a transformation angle of θ, the α-β components of current is then converted to the d-q with reference frame the aid of Park’s transformation.

i d   cosθ sin θ  i α  i  =     q  − sin θ cosθ  i β 

(8.8)

Step 2: The SRF controller helps in isolating the DC components from the d-q components with the help of a low-pass filter. Using reverse Park’s transformation, the extracted DC components are transformed back to α-β reference frame.

i αdc   cosθ sin θ  i ddc  i  =     βdc  − sin θ cosθ  i qdc 

(8.9)

From the α-β reference frame, reference currents in the three-phase abc frame can be generated using Clark’s transformation as follows: i *sa  *  i sb  = i *   sc 

1 / 2 2 1 / 2 3 1/ 2 

1 0  i0    − 1/ 2 3 / 2  i αdc  − 1 / 2 − 3 / 2 i βdc  

(8.10)

These three-phase reference currents are then fed to the PWM signal generator to provide the final gating signals to the DSTATCOM.

Optimization Technique Used This task of changing the controller gain value is minimised by the use of optimization techniques. There are various optimization techniques discovered, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Training and Learning based Optimization (TLBO) Technique, Dragonfly Algorithm (DA), and many more. In the proposed work, the system performance has been tested with two optimization techniques, PSO and DA.

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Particle Swarm Optimization The PSO generally consists of four attributes: the current position of the particle pi, the current velocity of the particle vi, the personal best Pb, and the global best Gb. The particles are to be updated in position and velocity. The new velocity, in each iteration of individual particles, can be mathematically expressed as: vij (k + 1) = wvi (k ) + c1r1[ pbesti, j (k ) − pi, j (k )] + c2 r2 [ gbest j (k ) − pi , j (k )]

(8.11) The particles’ local best position is updated accordingly, and the new positions of the particles can be calculated as:

pi , j (k + 1) = pi , j (k ) + uvi , j (k + 1)

(8.12)

Dragonfly Algorithm The dragon fly algorithm is motivated by the movement and the behaviour of the dragon flies [11, 12]. They use their unique and superior swarming technique for hunting and migration. They are small predatory insects that hunt small insects. The hunting behaviour of the dragon flies is called static (feeding swarm), and the migrating behaviour is called dynamic (migratory) swarm. The main inspiration for the algorithm is that these two types of swarming behaviour depict the two main phases of optimization: exploration and exploitation. The behaviour of swarms mainly follows four basic rules: separation consistency, affinity, alignment, near food source, and distraction from the food sources. Isolation (Si) is defined as the stationary clashing prevention of one form from other forms in the vicinity. Alignment (Ai) is termed as the velocity equity of individuals to others in a locality. Cohesion (Ci) indicates an inclination of particles at the mass centre of the region. The mathematical formulation of DA can be described as: Let us assume the population of dragon flies is of magnitude N. The point th of i dragonfly can be mentioned as:

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Xi = (Xi1,Xdi,…XiN) where I = 1,2,3,….N, Xid correlates to the instance of the ith dragon fly in the dth dimensional attribute of the search space, and N is the number of causalities. The separation (Si) can be calculated as follows: Si = −



N j =1

X −X

j

(8.13)

where X is the position of the current individual, Xj shows the position of the jth neighbouring individual, and N is the number of neighbouring individuals. Alignment is calculated as follows: N

V Ai =

j

j =1

(8.14)

N

The velocity of the jth neighbouring individuals is shown by Xj. The cohesive factor is expressed as:

CJ

 =

N j =1

X

N

j

−X

(8.15)

In the above expression, X represents the current position of the individuals, N stands for the number of localities, and Xj stands for the jth neighbouring individual. The tendency of the dragonfly to reach for food based on their affinity can be expressed as: Fi = X + − X

(8.16)

Here X is the current individual position, and X+ stands for the original food position. The enemy deviation factor is expressed as:

Ei = X − + X

(8.17)

where X is the current individual position and X ¯ indicates the enemy position.

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The Euclidian distance between all the dragon flies, by selecting N of them, helps us calculate the distance of the neighbourhood. The distance rij is given by:

rij =

 (x d

K −1

I ,K

− x J , K )2

(8.18)

The Separating Weight (S), alignment (a), cohesion (c), food (f) and enemy (e) factors for each dragon fly are initialised on their own.

X t +1 = (sSi + aAi + cCi + eEi ) + wX t

(8.19)

The value of the fitness parameter, which determines the most probable local best and global best, is mainly done with the help of an objective function. An objective function can basically be thought of as an equation that needs to be optimised by either maximising or minimising within a set of given parameters. The objective function proposed in this work that has been optimised by both the techniques is the Integral Time Absolute Error (ITAE). Figure 8.4 represents the schematic block diagram of the objective function.

Figure 8.4. Schematic diagram of the objective function.

Simulation and Result The proposed work implements a distribution network as shown in Figure 8.5. The network comprises a diesel generation system, a DFIG-based wind farm, and a PV system connected to the grid at the PCC. A rectifier load with a nominal power rating of 10 KW is connected to the PCC. The nonlinear nature of the rectifier is mainly responsible for the injection of source side harmonics. The purpose of DSTATCOM is two fold:

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Figure 8.5. Distribution system as discussed in the proposed work.

Case A: Role of DSTATCOM in Mitigation of Harmonics and Maintaining the Power Quality Figure 8.6 demonstrates the effect of the nonlinear loads on the current drawn from the PCC. The distortion in the sinusoidal nature of the waveform implies the presence of harmonic components due to the nonlinear nature of the loads. Figure 8.7 explains the harmonic distortion measured in the system in the absence of DSTATCOM.

Figure 8.6. PCC currents injected with harmonics.

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Figure 8.7. THD graph in the absence of DSTATCOM.

Now, in the presence of DSTATCOM, the control scheme forces the DSTATCOM to allow current exchange to or from the system, which aids in harmonic mitigation, as illustrated in Figure 8.8.This figure clearly indicates that the sinusoidal nature of the waveforms is restored and, irrespective of the presence of nonlinear loads, the currents drawn from the PCC are sinusoidal. The THD can be seen to have decreased by a considerable amount. This paper has proposed the use of two optimization techniques for determining the controller gains of the two PI controllers in the SRF technique. The values of the gain constants of two different optimization processes are listed under Table 8.1.

Figure 8.8. The PCC current waveform in the presence of DSTATCOM indicates the removal of harmonics.

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Table 8.1. Optimized values of the controller gains CONTROLLER GAINS Kp1 Kp2 Ki1 Ki2

PSO (c1 = 0.5,c2 = 0.5) 16.4798 15.8455 19.7066 42.6567

Dragonfly Algorithm 9.3361 49.0275 8.3531 30.4035

The THD levels in the presence of DSTATCOM but under two different optimization techniques are also noted in Table 8.2. The THD level is 61.60% without DSTATCOM, 1.45% with DSTATCOM optimised by PSO, and 1.17% with DSTATCOM optimised by DA. The graphical comparison is also clearly done in Figure 8.9(a) (DA) and Figure 8.9(b) (PSO). It can be concluded that the optimised values of the controller gains by DA yield a better result than that of PSO. To verify the robustness of the DA optimised DSTATCOM, a load change is applied at t = 0.3 sec (load increased) and at t = 0.5 sec. (load decreased). It can be clearly seen that the DSTATCOM is able to maintain the DC link voltage at 700V within 2.5 cycles of the supply voltage. Various waveforms under dynamic conditions are shown in Figure 8.10.

(a)

(b) Figure 8.9. THD graph when DSTATCOM is present (a) DA optimised controller (b) PSO optimised controller.

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Figure 8.10. Dynamic response of the system with DA optimised DSTATCOM.

Table 8.2. THD under various optimization schemes Criteria

THD Levels

Without DSTATCOM

61.60%

With PSO optimized SRF controlled DSTATCOM

1.45%

With DA optimized SRF controlled DSTATCOM

1.17%

Case B: Role of DSTATCOM in Maintaining Voltage Profile Figure 8.11 clearly explains the drop in voltage levels during a time interval of 0.4 to 0.6 sec. The cause was a sudden three-phase fault occurrence during the particular duration. This gave rise to an increase in line current and a corresponding drop in voltage. The sudden drop in voltage increases the reactive power demand to maintain the power flow in the system within the nominal limits. As shown in Figure 8.12, the DSTATCOM plays a great role in mitigating the voltage sags. In Figure 8.12, it has been clearly demonstrated that the voltage sag which existed in Figure 8.10 has been nearly mitigated. This is because the DSTATCOM helps in reactive power injection, which brings the voltage level to its previous value, thus maintaining the voltage profile.

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Figure 8.11. In the absence of DSTATCOM, the PCC voltage is in a fault condition.

Figure 8.12. PCC voltage in a fault condition in the presence of DSTATCOM.

Figures 8.13 and 8.14 demonstrate the DC voltage fluctuation across the capacitor in the absence and presence of DSTATCOM, respectively.

Figure 8.13. Capacitor voltage in the absence of DSTATCOM.

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Figure 8.14. Capacitor voltage in the presence of DSTATCOM.

Conclusion The proposed work is a photovoltaic based DSTATCOM present in a microgrid. The gains of the PI controllers in the SRF control scheme have been optimised by two algorithms, namely the Particle Swarm Optimization Technique (PSO) and the Dragonfly Algorithm (DA) technique. The performance of the DSTATCOM both in the absence and presence of optimization techniques has been observed. Comparison has also been done in the tabular form to establish the DSTATCOM performances in terms of the THD (Total harmonic Distortion) considering voltage sag in PCC voltage. It has been observed that the THDs have been considerably decreased and voltage sag is being mitigated, thereby maintaining the smooth voltage profile.

Appendix System ratings DC Capacitance DC Voltage Filter Impedence Resistive Load Three Phase Series Rlc Load Inverter Load PSO Gains DFIG Wind Farm(Rating Of One Generator × 6) PV Array Diesel generating system Grid Voltage

2800 µF 700 V 0.1 + j0.003 2 KW, 5 KW, Inductive Power-100 Var 10 KW, Inductive Load- 100 Var c1 = 0.5, c2 = 0.5 Nominal Power= 1.6 MVA Stator voltage(nominal) = 575 V Rotor Voltage(nominal) = 1975 V 100KW Nominal power: 3.125 MVA 575 V

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[3]

[4]

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[13]

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[15]

Ghosh A., G. Ledwig “Power quality enhancement using custom power devices.” Norwell, M A: Khuwer 2002. Ghosh A., G. Ledwig, “A flexible DSTATCOM operating in voltage or current control mode.” IEE Proc.-Generation, Transmission and Distribution. Vol. 149, No. 2, Murcli 2002. Chang W., K. Yeh, “Design and Implementation of DSTATCOM with Symmetrical Components: Method for Fast Load Compensation of Unbalanced Distribution Systems.” IEEE International Conference Proceedings, pp.1548-1551. Blazic B., I. Papic, “A new mathematical model and control of DSTATCOM for operation under unbalanced conditions.” Electric Power Systems Research 72 (2004) 279–287. Singh, B. P. Jayaprakash, D. P. Kothari, A. Chandra, “Comprehensive study of DSTATCOM configurations.” IEEE Transactions on Industrial Informatics, Vol. No. 10, 2014, pp: 4854-869. Shukla A., A. Ghosh, “State Feedback Control of Multilevel inverters for DSTATCOM applications.” IEEE Transactions on Power Delivery, vol 22, No 4, pp: 2409-2418. Elnady A., Magdy, “Unified approach for kitgating voltage sags and voltage flicker using DSTATCOM.” IEEE Transactions on Power Delivery, vol 20, No 2, 2005, pp: 992- 1000. Kannan V. K., N. Rengarajan, “Investigating the performance of Photovoltaic based DSTATCOM using I cosφ algorithm.” Journal of Electrical Power and Energy Systems, 2014, pp: 376-386. Mishra S., P. K. Ray, “Nonlinear modelling and control of a photovoltaic fed improved hybrid DSTATCOM for power quality improvement.” Journal of Electrical Power and Energy Systems, 2016, pp: 245-254. Mishra A. K. et al., (2020) “PSO-GWO Optimized Fractional Order PID Based Hybrid Shunt Active Power Filter for Power Quality Improvements.” IEEE Access 8, 74497-74512. Rahman C. M. and T. A. Rashid, “Dragonfly Algorithm and Its Applications in Applied Science Survey.” Computational Intelligence and Neuroscience, 2019. Mafarja M. M. et al., “Binary dragonfly algorithm for feature selection.” In 2017 International Conference on New Trends in Computing Sciences (ICTCS) (pp. 1217). IEEE 2017. Mitra P., G. K. Venayagamoorthy, “A DSTATCOM Controller Tuned by Particle Swarm Optimization for an Electric Ship Power System.” IEEE 2008 international conference. Gozde H., M. C. Taplamacioglu, “Automatic generation control application with craziness based particle swarm optimization in a thermal power system.” Electrical Power and Energy Systems 33 (2011), pp: 8-16. Ghoshal S. P., “Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control.” Electric Power Systems Research 72 (2004), pp: 203-212.

Chapter 9

Role of Machine Learning in Forecasting Solar and Wind Power Generation Rachna Vaish* and U. D. Dwivedi Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi, India

Abstract Power generation from solar photovoltaic plants and wind power plants fluctuates with the prevailing climatic conditions and time of day. To forecast the power generation from these plants is a challenging task and beyond human capacity. Therefore, artificial intelligence techniques come into play for such complex predictive problems. Machine learning is one of the powerful tools of artificial intelligence which is widely used for classification and prediction. Using machine learning forecasting models, potential power generation from renewable sources can be forecasted in advance based on weather data. Such power generation prediction will be very helpful in economic load dispatch and maintaining stability. This chapter will mainly focus on giving a generalized idea of Machine Learning and its applications. It also provides step by step process for time series forecasting as well as an indepth discussion of various models used for renewable energy forecasting. In addition, a brief state-of-the-art review for forecasting power generation from solar photovoltaic (PV) and wind plants is included which will help readers to get a better understanding of the current research scenario. Furthermore, the chapter has been consolidated with a case study of wind power forecasting using the seasonal autoregressive integrated moving average (SARIMA) model to demonstrate the time series forecasting framework. Also, the results of *

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Rachna Vaish and U. D. Dwivedi the SARIMA model have been compared with those of long short term memory (LSTM).

Keywords: LSTM, machine learning, SARIMA, solar PV power forecasting, time series forecasting, wind power forecasting

Introduction Global warming, climate change and the energy crisis have had a significant impact on government economic policies in recent decades [1]. Due to the negative impacts of climate change, the need to move towards more sustainable technologies and approaches is now more important than ever. Earlier the power sector was responsible for 65% of worldwide greenhouse gas (GHG) emissions, however, the reliance on renewable energy (RE) resources has shown a vital role in decarbonization [2]. Apart from the environmental aspect, the continual dependence of power generation from conventional sources creates a burden on existing resources as their regeneration rate is much slower than their consumption. The threat of scarcity of fossil fuel and the challenges of energy security have sparked an interest in using RE sources which are gradually replacing the conventional energy sector and preventing sole reliance on conventional sources for power generation for sustainable development and prevent power outage [3]. The power generation production cost from RE sources has decreased to a competitive level [4]. Hydropower plants had always been in the power sector however, interest in the development of solar and wind power is drastically increasing [5]. The forecast of RE output, especially wind and solar power, has a substantial impact on power system operation and management decisions. Accurate forecasting of RE power generation is essential for maintaining grid reliability and availability, as well as reducing the risk and expense of the energy market and systems. Forecasting will assist not only power plants and grid managers but also energy dealers and regulators. Equipping only RE resources for power generation will pose significant challenge to meet the power demands. Therefore, we cannot rely solely on RE to feed the national electricity grid [3]. The fluctuating nature of RE makes it difficult to integrate into power networks in terms of geographic and demographic conditions. Solar energy is plentiful in the equatorial region while scarcely available in the polar region and wind energy is abundant in the nearshore but scarce in the landlocked regions. In addition, uninhabited land

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is required for setting up windmills and solar parks [6]. The major limitation of RE sources is their unpredictability due to various weather conditions and their limited use [6]. However, RE sources are readily available and their extraction is cost-effective, however, their installation is costly [7]. The basic goal of an electricity grid is to meet the demands without compromising the supply chain and make sure that all customers have access to the electricity. This can be accomplished by building energy storage units and committing to stand-by generation capacity, although such integration increases the cost of the power grid [2]. Power generation forecasting from RE sources is essential for their large-scale penetration/integration to make them reliable and cost-effective. It facilitates utility operators in monitoring the power system and helps optimize generating station reserve capacity by making appropriate unit commitment decisions. As a result, it lowers energy generation costs and improves system reliability. Thereby, it is essential for economic load dispatch and maintaining system stability. Various efforts have been undertaken to optimize electricity costs by embracing new technologies for forecasting. Machine learning (ML) can play a major role in this scenario because it can transform real-time data into a decision-making module [2]. Generally, the day before operating hours, power producers can trade their forecasted power in the market. During the last hour, there may be a larger variation in the predicted power than the actual. If the forecast is incorrect, power producers can trade with other producers on the same day 45 minutes prior to the operational hour, after which the market closes. However, large variations in power generation from bulk RE sources can result in power imbalances and related issues. The transmission system operators can maintain the imbalances in power supply and demand by starting or stopping units. The reserve units must be prepared to respond swiftly in the event of an imbalance. Even though RE sources can provide cheaper clean energy, the drawback is that they create bigger imbalances, which raises the demand for regulating electricity costs. Therefore, enhancement in the prediction accuracy would be the best way to reduce the imbalances and minimize the cost. Thus, the higher the proportion of RE sources, more is the need for accurate forecasts [4] which can be achieved using ML models.

Machine Learning This section gives a quick overview of ML and its applications. Several MLbased classification and regression models are discussed with emphasis on

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time series forecasting and its models. In addition, a generalized framework for time series forecasting is provided.

Overview ML enables the computer to learn from the past data and extract information about the system for which the data has been stored. Thus, ML lets the computer learn without being explicitly taught, allowing it to make decisions and predictions on its own. With an increase in the sample count in the dataset, the performance of the algorithm improves adaptively. Medical diagnosis and disease spread forecasting, search engine, stock market, navigation, gaming, recommendation system, image and speech recognition, weather forecasts such as wind speed prediction, load forecast, solar and wind power prediction, fraud detection, parameter estimation, feature selection, and system identification for unknown non-linear systems are all examples of where ML paradigms are being used. ML is commonly used to address complex problems with large training datasets and a high number of variables. Depending on the available data and their intended output, ML can use an unsupervised, supervised, or reinforcement learning paradigm. These learning techniques include a variety of algorithms that can be used to solve classification, regression, and forecasting problems [8].

Classification The objective of the classification algorithms is to identify the belongingness of a data sample among different pre-defined groups or classes in the data. The given data is divided into distinct groups using classification algorithm. The classifier produces a label or discrete response to identify the class of the test or unseen data using class labels for the given dataset. Some of the classification models available are logistic regression (LR), naive bayesian (NB), decision tree (DT), know-nearest neighbor (KNN), support vector machine (SVM), neural network (NN), and various ensemble techniques such as random forest (RF), bagging and boosted decision trees (BBDT), adaptive boosting (AdaBoost), stacking and voting [8]. Regression These are used to predict or forecast values for unknown data using patterns found in the given data. For this, regression algorithms generate a continuous response using values from known data to accurately estimate values for the

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test or unseen data. Regression analysis is a collection of ML algorithms for predicting a continuous outcome variable (y) based on the values of one or more predictor variables (x). Some of the regression models available are linear and polynomial regression, regression tree (RT), support vector regression (SVR), Gaussian process regression (GPR), random forest regression, and gradient boosted regression tree (GBRT) [8]. In a nutshell, the purpose of a regression model is to create a mathematical model/equation that specifies y as a function of x. Then, using the test values for the set of input predictor variables x, this equation can be utilized to predict outcome y. There are various new variants of regression models, specialized in finding outcomes in the form of a set of values known as time series values. Such regressive models which predict time series data are known as time series forecasting models [9]. The other specialized ML forecasting models are recurrent neural network (RNN), long short-term memory networks (LSTM), and gated recurrent unit (GRU). RNN is a NN having recurrent connections between neurons, allowing it to learn from both current and previous data to find a better solution. However, due to gradient vanishing and explosion difficulties, it is difficult to extract relevant information when two RNN cells are far apart [10]. The solution is to use LSTM, which is a particular sort of RNN with memory cells that are special neurons. The LSTM can store important information for an arbitrary amount of time because of these unique neurons. Furthermore, by modifying three separate controlling gates namely, the forget gate, input gate, and output gate; LSTM cells may be able to figure out what information needs to be read, saved, and erased from memory. The forget gate determines whether the information from the cell state should be kept or discarded. The input gate, on the other hand, determines which input values have to be stored in the cell state. In addition, the output gate is in charge of passing the stored information to the following neurons [10]. GRU is a variant of the LSTM that shortens the training period. GRU has lesser controlling gates than LSTM since it lacks an output gate. The structure of GRU is much simpler than LSTM since it just has two gates that govern the information flow between the units–the reset gate and the update gate [10].

Time Series Forecasting Prediction in regression appears to estimate the value of the future, present or past, with respect to the supplied data. Whereas time series forecasting

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estimates future values based on historical values in a time series format. A time series model, also known as a signal model, is a dynamical system that can be used to fit signals or time-series data. Multivariate models are created because time series are multivariate. Both time-and frequency-domain data can be used to estimate time-series spectra. Time series forecasting can be done for various time spans depending upon the need [9]. The forecast horizon is a significant component that influences both the data resolution and model structure decisions. Depending on the intended application or decision-making process that the forecasting will serve, it might be very short, short, medium, or long-term. Horizon classifications are not agreed upon, and the categories may overlap [11]. Time series forecasting is capable of identifying the presence of seasonal components in the data/signal and thereby it can be removed so as not to get confused by the seasonal components in case of long-term forecasting. Elimination of seasonal components is called seasonal adjustment. Some other advantages of time series forecasting models are filtering/elimination of noise from signals, prediction of one series from the observation of another series, e.g., forecasting future sales pattern from expenditure data pattern on the advertisement, and helping to make adjustments of parameters priorly to control future values of a series. Simulation studies can also be benefitted from time series models for making timely changes in the studies [9]. The key finding is that time series models like autoregressive integrated moving average (ARIMA) outperformed ML regression models and some other time series models for short-term prediction. The ability of the model to capture transitions over time is the fundamental reason for its superior performance [12]. Autoregressive moving average (ARMA) and ARIMA are extensively used algorithms for time series forecasting. These methods mean that the past values of the series, referred to as the series history, affect the future of the series through a combination of auto-regressive (AR) and moving average (MA) features. In a pure auto-regressive process, the future values of the series are determined entirely by its past values. The future values of the series are determined by random variables that are independent of one another and are treated as white noise in the moving averages method [13].

Time Series Forecasting Framework The stepwise procedure to be followed for time series forecasting is as follows:

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1. Determine the objective of the problem in relation to your desired prediction or forecast. List the data needed to make the desired forecast. 2. Acquire data from a reputed source. In case data from various sources have been obtained, arrange the data so that they have the same time scale. Do the feasibility analysis for the chosen forecasting horizon (short, medium, long, etc.) [8]. 3. Plot the time-series data and study the key features of the graph, analyzing for a trend, seasonal components, any visible sudden pattern changes, or any unusual observations. 4. One can use the auto-ARIMA module to get the best model for their data and then perform grid search on that model for the best parameters to train the final forecasting model and do predictions. Then straightaway follow step 8 to get prediction accuracy. However, to manually try for various other forecasting models follow the steps from 5–7. 5. It may be necessary to do some transformations on the data, in order to obtain the stationary residuals and for this to get rid of the trend and seasonal components. If the magnitude of the variations increases substantially with the level of the series, the modified series will have more constant magnitude variations. If any data is negative, before computing the logarithm, add a positive constant value to each such data to ensure that all values are positive. Trend and seasonality can be removed by a number of techniques, some of which involve computing the components and removing/subtracting them from the data, and the other method relies on differencing the data, i.e., replacing the original series for some positive integer d. Regardless of the method used, the goal is to create a stationary series whose values will be referred to as residuals. 6. Using different sample statistics, including sample autocorrelation function, choose a forecasting model to fit the residuals. 7. Forecasting will be done by forecasting the residuals after which invert the transformations used earlier to get the original series predictions [9]. 8. Evaluate the forecasted series performance for better reliance on prediction. Various performance metrics used for forecasting ML models are root mean square error (RMSE), R squared, mean absolute percentage error (MAPE), and mean absolute error (MAE) [3].

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Solar PV Power Forecasting This section discusses the challenges of solar PV power generation and the anticipated role of the grid. Also, a brief literature analysis on solar power forecasting has been included to give an overview of the research scenario.

Integration Challenges and Importance of Solar PV Power Forecasting Semiconducting material in a PV panel can convert solar radiation directly into the electrical energy. Even though solar cells are seen as a significant source of future energy generation, their low return on initial investment and high upfront capital costs are preventing their widespread use [14]. Solar radiation, weather, ambient temperature and the geographic location of the PV panel all play a role in its use [10]. The RE industry is working to increase the solar energy generating plants. The increased level of electricity grid integration can create grid instability, making it the industry's largest hurdle. As a result, forecasting power generation from RE plants is an important step in future development. Consequently, it is important to accurately forecast the power generation capacity of PV plants over different time horizons. Solar power forecasting is difficult since it is heavily reliant on changing weather conditions. To overcome the aforementioned obstacles, highly efficient methods must be used to achieve legitimate and reliable forecasting results [10]. Solar power prediction is a multidisciplinary project that requires input from the meteorology department, solar cell technology, electrical engineering, and ML computing [14]. Solar panels have a hard time performing to their full potential when the weather is bad or the air is polluted. Knowing the power output of solar panels ahead of time can aid in proper installation of solar panels and ensure that they perform at their full capacity. The environmental situation should be taken into account while employing solar panels to generate electricity. Solar PV panels can operate at their maximum potential when the sun shines directly on them and they are not partially shaded. However, factors such as weather e.g., cloudy or rainy, and air pollution may cause partial shade due to dusting [15]. PV modules can operate as stand-alone solar power generators to run water pumps in farms or mounted on the roof of a building for domestic

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purposes and can operate in grid-tied mode as solar parks or mounted on the roofs of commercial building such as hospitals, schools, colleges, and companies [16]. In recent years, the installation of solar panels has expanded year after year. By March 2021, total solar PV power generation capacity has reached 714 GW globally [17]. Conventional energy-based power generation and distribution rely on regular load and stable power supply cables. Solar PV power when working in grid-tied mode could interfere with power grid operation, making it unstable or even impossible to operate. Controlling suppliers, customers, and prosumers (producer+consumer) can improve grid efficiency [16]. Solar PV power forecasting is also essential for restructuring and setting up solar PV parks, commercial PV power generation to sell to the grid, and power system stabilization and power system disturbance warnings on selfgoverning power systems. The energy production of the PV power generation station must be forecasted a day in advance, and the process has to be followed daily by the PV power generation station. The forecasted output error has a significant impact on economic operations as well as the productivity of the power system. Accurate PV power prediction is challenging due to several meteorological characteristics such as temperature, cloud quantity, and dust. Various forecasting methodologies have been created for PV power forecasting throughout the last few decades that can be used by producing stations [3]. The lower costs of solar power production, increased costs of conventional energy sources, environmental degrading factors, and legislative requirements are among the numerous reasons why electricity service providers are boosting their interest in the solar power. The support of the government in form of subsidies is leading to an increased number of homes and small businesses equipped with solar PV panels and storage systems. This helps them generate solar energy that is being used in homes and businesses, stored in battery banks, or sold back to distribution companies in tiered or realtime pricing structures. The ability to forecast the output of large solar PV plants and wind farms gives electricity suppliers the time they need to make modifications to the baseload requirements from conventional fossil fuelbased power plant output to reduce peak power plant usage [18]. Prosumers can also benefit from anticipating solar PV power production that may help make decision about power storage, use, and sale as the realtime pricing offered to consumers varies. Prosumers, on the other hand, require different datasets for forecasting than power providers. Also, prosumers, do not have access to similar datasets that power providers have

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like cloud motion vector data. They do not require day-ahead forecasts, which are essential for base power plants for steady-state power output. Based on minimal data sources available prosumers require short-term forecasts [18]. As the energy from PV plants is increasing at an exponential rate, forecasting their output is more important in the future, as earlier PV plants were mainly built with a "fit and forget" mindset. Solar plant production is insecure, because of the unpredictable clouds that keep forming and moving in the sky. To maximize the performance of these systems, precise forecasting ML models based on data from weather forecast providers are required. Accurate forecasting enables maximization of self-consumption of power and minimization of the levelized cost of the energy from the perspective of owners of commercial-industrial and residential PV plants; this improves the possibility of operating in a grid-parity regime [19]. When commercial-industrial and residential PV plant systems are part of a microgrid, power prediction is also helpful for energy management systems to optimize storage device state of charge (SOC). Utility-scale PV plant managers, on the other hand, employ predictions to optimally arrange plant downtime for maintenance purposes, taking into account larger-scale plants and fuel parity. In addition, the forecasting model enables optimization of timetables for suppliers with a day-ahead power market to offer pricing on the electricity market across nations, avoiding penalties and low profits [19]. Accurate forecasts improve dependability and lower costs by permitting more efficient solar energy trading and secure grid management. Furthermore, forecasting reduces the number of units on standby and lowers the cost of running the overall power system. It is also worth noting that PV plants can be combined to form virtual power plants. In this situation, solar irradiance variability has little impact on the electricity grid to which the virtual power plant is connected but this form of alliance—"behind the meter"—can generate load forecasting mistakes [19].

Machine Learning-Based Solar PV Power Prediction The physical approach, statistical method, hybrid approach, and AI are the four types of PV generation forecasting methodologies. To anticipate solar PV power generation in time series frames in different horizons using past measured data, statistical approaches are mainly based on the data-driven formulation [3]. The authors of [20] used linear regression and M5P regression tree to forecast solar power generation for the harsh environment of Qatar

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which helped them to make a strategic plan of cleaning panels to remove dust in summers which caused very low PV power output. Using these simple models with correlation-based and relief-based feature selection gave them simple and easily interpretable models showing adequate performance. Another tree-based model is used in [21] to forecast solar power in time series form. The author compared the performance of RF and GBRT with generalized additive models and SVR for estimating hourly solar PV power generation. They did predictions for 28 and 45 hours ahead forecasts using 36 weather parameters as inputs to the algorithms. For two distinct data sets, accuracy using 30 Kfold cross-validations for the RF model comes out to be the best of all the models. A novel model based on GBRT was proposed in [13]. This generated 6 alternative GBRT models, one for each step, from 1 to 6 steps (hourly) ahead prediction. Lagged power data and forecasted data of temperature, cloud cover, humidity, precipitation, and wind speed were utilized as inputs to the models. The parameters of these models were fine-tuned independently using 5 Kfold cross-validations of training data from several sites. In terms of accuracy, GBRT was more accurate than AR, with the lowest normalized RMSE. A multiple step ahead PV power output prediction for 5 min to 3 hours ahead approach is proposed in [22] which uses different ML algorithms namely NN, RF, SVR, and LR giving high accuracy. The key point lies within data resampling techniques which enables multiple steps ahead predictions on the re-sampled time-series using a simple single-step ahead forecasting. The suggested approach creates a new representation of the original time series based on the resampling process at each prediction step. Now on this resampled time series representation, another forecasting model is developed for single-step forecasting. This forecasting on the re-sampled time series for a single-step forward prediction corresponds to the mth step ahead prediction original based on re-sampling factor m of the original time series. Because the suggested method combines a series of models built for single-step ahead prediction to calculate multiple steps ahead predictions, it greatly minimizes the error for higher prediction steps. The work presented in [23] looked into utilizing NN to forecast 24 hours ahead PV power outputs for the following day, using PV panel power data of the previous day and the weather forecast data for the following day as inputs. They divided the weather patterns into three categories: bright day, cloudy day, and rainy day, and constructed a different prediction model for each category using a NN. Based on the expected solar irradiance and cloudiness for the next day, self-organizing map (SOM) was utilized to learn the features

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of the three sorts of days. The SOM is used to select the category of day for forecasting PV output for a new day, and then the NN model corresponding to the matching day category is selected to do prediction. The MAPE for bright, overcast, and rainy days was calculated based on a 12-day analysis. Another work presented in [24] used NNs to forecast solar power generation for the next day on an hourly basis. NN with a single hidden layer of 9 neurons and 24 output neurons, matching the 24 (hourly) predictions, was used. Gradient descent was used to train the NN model. The model uses the next day's weather forecast data of temperature, humidity, wind speed, aerosol index, and power output of the plant for the previous day. The MAPE of this model was found to be low for forecasted data. Using 24 neurons in the output layer of single layer NN [23] and [24] predicted next-day power outputs for 24 hourly bases. In comparison to systems that generate a distinct model for each prediction step, this greatly reduces model training time. Work presented in [25] uses SVR on behalf of NN to predict solar PV power for the next 24 hours in 15-minutes intervals. They also classified the days into four sorts as in [23] – cloudy, clear sky, foggy, and rainy; and then developed SVR models for each sort of day. They identified the day as one of four sorts depending on that area weather forecast before applying the relevant SVR model to predict the PV power output the next day. The PV power output data for the recently occurred similar sort of day to be predicted, as well as the temperature forecast (average, max, and min) for the upcoming day, were fed into the SVR models. The model was validated on data from a 20kW solar PV plant, MAE was calculated for different sorts of weather conditions. SVR was also used in [26] to estimate solar power output for 30 minutes to 6 hours ahead of time. They forecasted power for intervals consisting of the highest and least probable power output for each time scale, in contrast to the previous investigations. They trained a distinct SVR model for each time scale, using both past power data for different time scales and four meteorological variables as inputs. SVR beats the NN-based model showing lower MAE over all time scales. SVR and NN models were used for forecasting solar power for 1 hour ahead at intervals of 5 minutes [27]. Both the models were tested in two ways– –univariate i.e., only previous power data was used as an input, and multivariate i.e., both previous weather and power output data were used as inputs. To generate the predictions, a correlation-based feature selection method was utilized to select the most informative features to use as inputs to NN and SVR models. When compared to multivariate models, the results showed that univariate models had equivalent or superior accuracy. A

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comparison of the direct technique to the iterative approach utilizing the same ML algorithms revealed that the direct strategy produced a 3–5% increase in accuracy over the iterative approach. Another short-term forecasting model for solar PV power using LSTM is presented in [28]. Following the statistical feature extraction process, the WT is used to decompose the stored solar PV power time-series data into distinct frequency series. The statistical WT-based features along with meteorological data temperature, wind speed, pressure, cloudy-index, humidity, and altimeter index are used as input to the LSTM model to forecast solar PV power generation in different time-horizons (hourly and daily basis). The performance of the proposed WT-LSTM model was better than the linear, ridge, elastic net, and LASSO regression techniques. A comparative study for solar power forecasting using LSTM and GRU is presented in [10] where GRU outperformed LSTM. Solar PV power prediction using deep learning is presented in [29] using CNN and LSTM with attention mechanism; and in [30] using RNN, LSTM, and GRU. The performance of such a forecasting model gave a better result than other regression algorithms. A 24 hour ahead solar PV power prediction work is presented in [31] using radial basis function NN, least-square SVM, and ARIMA. The comparison showed that ARIMA performed best for forecasted power, showing the lowest RMSE and MAE. From the survey, it was found that among the AR model, linear regression model, and an AR model with exogenous input (ARX model) for forecasting solar PV power output, the ARX model outperforms the others [32].

Wind Power Forecasting This section discusses the challenges of wind energy integration and the significance of forecasting and importance on the grid operation. Also, brief literature assessment on the wind power forecasting is concluded to provide a research scenario in this area.

Integration Challenges and Importance of Wind Power Forecasting Wind power is another RE source with the lowest electricity generation cost and abundant resources. As a result, most countries are realizing that wind

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power offers a big future in a power generation opportunity. This led to increasing installed wind capacity at a rate of more than 30% each year [33]. By March 2021, total wind power generation capacity has reached 733 GW globally [17]. The power a wind turbine produces is determined by wind speed, which fluctuates over time and is affected by regional terrain types, weather patterns, and seasonal variations [7]. Wind turbines often have a gap between expected and actual production, causing severe imbalances in the power grid. Reserve markets will address these imbalances in the power system through reserve units. Unforeseen changes in wind power generation can increase the cost of running the power system due to increased primary reserve requirements, as well as pose potential threats to power supply reliability [34]. The transmission system operator in the respective area must test and accept units acting as reserve units/market. Power plants, boilers and batteries are currently allowed to be admitted as reserve units/markets, however, wind turbines are not eligible. This is owing to the intermittent nature of the wind. If a reserve/balancing unit is purchased, it must be available at all times. The hours of operation for power plants are decreasing as wind power capacity grows. Due to reduced operation of power plant units, their net profit is reduced thereby creating a problem in delivering services, this creates challenges in reserve markets as very few power plants operate. Thus, the price will increase as market competition reduces [4]. The most well-known difficulty was wind intermittency, which was seen as a major obstacle to widening the penetration of wind power as it would require a high level of regulation and reserves to ensure reliability. According to the IEEE/PES summary, a wind penetration level of approximately 20% of the system peak load will increase the operating cost of the system, such as unit commitment cost, due to wind fluctuations and uncertainty. As a result, several utilities have limited the quantity of wind power that can be used on their systems. Moreover, as the capacity of wind farms grows, the strain they exert on the transmission lines grows as well, because it may be possible that transmission grids are not able to transport all of the generated wind power [35]. As wind integration expands, the demands for solving various problems, such as effective market design, ancillary service requirements, real-time grid operations, electricity market clearing, costs, maintaining power quality, power system stability and reliability, upgrading transmission capacity, and interconnection standards, become more difficult [33]. Improved wind forecasting is one of the most cost-effective ways to address many of the

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issues. Many challenges can occur in wind power forecasting in general. In most cases, the algorithms are based on a secondary prediction, such as wind direction, wind speed, or temperature. These data are obtained from the weather forecasts agency, which adds another layer of uncertainty to the forecasted output of the model. If there are erroneous values in the input data, there will be more chances of errors in the output data with larger fluctuations. In other words, because of the uncertainty in the input data on which the model is based, there is a higher possibility of inaccuracies in the model output i.e., forecasted wind power. Certain levels of mistake can be tolerated depending on the purpose. Transmission system operators place a premium on product availability, which is the main reason for exclusion of wind power from the market [4]. From a reserve standpoint, generally, transmission system operators are unconcerned about predictions being 100% accurate. The caution mindset of operators stems from the uncertainty of purchasing power based on availability. A possible solution to this problem is to allow wind turbines to participate only if the predicted wind power is greater than a certain threshold [4]. However, to qualify wind power generating stations as spinning reserve units in managing grid operations, power system operators must foresee fluctuations in wind power generation from accurate wind speed forecasting. This may help to reduce spinning reserve capacity and boost wind power penetration in the grid [34]. As wind power generation is site-dependent based on wind speed in that region, wind power forecasting is not "plug-and-play." The form of the surface landscape, as well as large-scale atmospheric conditions, influence wind speed. As a result, it should consider the wind profile in that area, climate, and terrain type. The wind speed will be stable in an area if the magnitude of variations in the atmosphere is small. Thus, wind power predictions would be more accurate in that scenario [35]. Forecasting wind speed onshore having complex topographical conditions differs from forecasting wind speed offshore. Thus, it is difficult to come up with possible prediction systems for the two as they have different characteristics. The roughness of the sea surface is quite low, and the thermal stratification of the atmosphere, as well as the thermal stability of the wind, are very different from the near-neutral scenario seen onshore. However, because of the low roughness, offshore wind speed stability has a better speed profile than onshore. As a result, large wind power capacity expansions in the future are likely to occur offshore [35].

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Machine Learning Based Wind Power Prediction The author of [36] studied the KNN approach over a variety of time horizons and discovered that it works best for very short-term predictions. In [37] the author claimed that in comparison to other ML models, KNN takes less time to train and test. Also, KNN performs admirably with a limited number of features. According to the study done in [38] on a wind farm case of northern Iran, KNN shows errors in the forecast throughout the seasons. KNN could not incorporate fluctuations in temperatures. This is because KNN is a lazy learning algorithm that does not learn from its output. Similarly in [39], it is observed that KNN performs poorly in high wind speeds. Outliers produce higher errors in the KNN model. The studies done in [37] and [39] reveal that SVM is a robust prediction algorithm, based on a good performance in wind power prediction for longterm wind power forecasting. SVMs also show a consistent accuracy with both small and large number of features. When compared to other methods, the error normally increases as the number of features increases, yet SVM maintains a consistent performance. In [40], short-term wind power forecasting is done using SVM and it was discovered that the method exhibits large mistakes in wind power prediction at high wind speeds but low errors at low wind speeds. The use of SVM has some drawbacks as it depends on numerous parameters which can have a negative effect on model forecasting performance if all parameters are not set correctly. Furthermore, SVM has a very long computation time [37, 41]. To estimate wind power output, a hybrid prediction model is proposed in [42] integrating wavelet decomposition, SVM, and an enhanced atom search technique. To improve the position update equation, a dynamic sinusoidal wave adaptive weighting is included, and crossover and mutation operations are added at the end of each iteration to boost the searchability of the atomic search algorithm. To lower the forecasting error due to data fluctuation, wavelet decomposition on the original wind power data is performed. This decomposes the non-stationary signal into many detailed sequences of different frequencies and approximate sequences to extract the essential wind power properties. Actual wind farm data prediction findings show that the suggested approach has significant advantages in predicting performance. MAE, MAPE and RMSE all have reduced significantly. The proposed strategy is conducive to reducing wind curtailment and increasing the economic benefits of the wind farm [42]. Another SVR-based scheme is proposed in [43] in which in which the hyper-parameters of the SVR were optimized using a

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hybrid improved cuckoo search arithmetic (HICS), to estimate short-term wind power output (HICS-SVR). The model was validated using data from French wind farms, and wind energy data randomly selected to build the training and test sets of the algorithm. HICS-SVR greatly increases the accuracy of prediction and stability of output results [43]. In [44], wind power generation forecasting for Ireland is done and it was found that NN is a flexible approach. This is a non-parametric method, which means that the algorithm relies on the data to make fewer assumptions. Also, it performed well throughout the seasons for short-term generation forecasting in a microgrid. Unlike other techniques, temperature does not affect inaccuracy. This also highlights the adaptability of the model. In [41], a study is conducted for hour-ahead wind power forecasting and discovered that irrelevant data affects NN. As the number of features in the model grows, the model performance suffers. Furthermore, the study shows that if the various parameters of the NN are not fine-tuned, they can reduce the forecasting performance. The study done in [39] shows that RF is an excellent strategy for dealing with both low and high wind speeds. Furthermore, it also depicts that several features used in RF have a significant impact on the results. RF forecasting error drops faster than KNN and SVM with an increase in the number of features. A study on a US wind farm that makes short-term predictions shows that having irrelevant inputs does not affect RF [41, 45]. When new features are introduced to RF, the MAPE is lower; whereas, when the same input is given to NN, MAPE increases because the model is perturbed by irrelevant input [41]. The potential of well-known ensemble techniques boosted trees, RF, and generalized RF to produce an accurate short-term wind power prediction is investigated in [46]. The performance of these ensemble models was compared to that of two commonly used prediction methods i.e., GPR and SVR. The prediction efficiency of the examined models is tested using experimental measurements taken every 10 minutes from actual wind turbines in France and Turkey. In comparison to standalone models, experimental results reveal that ensemble approach outperforms SVR and GPR in terms of performance and can accurately estimate wind power generation [46]. In [47], employing optimization on the input dataset, a 48-hour ahead forecasting of wind power using a GBRT is proposed. The scheme focuses on the input data to extract as much knowledge as possible from the source and eliminate as much inaccuracy as feasible. In contrast to other research, which employs a fixed learning dataset, the proposed approach is unique as the model constantly retrains to provide the most up-to-date information. When

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compared to the other techniques, the entire methodology aids the model in anticipating wind prediction inaccuracy and seasonal tendencies, resulting in high prediction accuracy [47]. In [48], a modified LSTM model is used to forecast wind power. This model is based on the K-means-LSTM, which can explain time dependencies in time series data with greater precision. When compared to back-propagation NN, SVR, and LSTM models, it exhibited better prediction performance. ARIMA models have been used in [49] and [50] for short-term wind power forecasting. In [49], the proposed ARIMA model outperformed other models used for one-hour forecast. Also in [50], a slightly modified ARIMA scheme is proposed which is capable of dealing with unsymmetric fluctuations and data-trailing issues. The proposed model uses logarithmic process and conditional heteroscedasticity and the results show high accuracy.

Power Generation Forecasting Horizons The solar and wind power predictions are being done in various horizons which will correlate to the different needs of decision-making processes for grid operations and its management. Very short and short-term forecasting horizons are critical and beneficial for the operation of PV/wind power generating stations, storage control, real-time unit commitment, and electricity sale in the context of microgrid and smart grid energy management. Thus, the majority of research focuses on developing an enhanced model for short-term power forecasting [3].

Forecasting Horizons Figure 9.1 shows various time horizons in which power prediction is done with their intended need.

Very-Short-Term Forecasting This forecasting is done for a few minutes ahead (say 30 minutes). This forecasting is done for real-time grid operations and taking regulatory actions. Further, forecasting is done based on meteorological data and is useful for PV storage control and energy marketing.

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Figure 9.1. Forecasting horizon and their intended operation [28].

Short-Term Forecasting This forecasting is done for a few hours ahead (say 30 minutes to 6 hours) for planning economic load dispatch and load increment or decrement decision making. The forecasting is done based on meteorological data. Medium-Term Forecasting This forecasting is done for a day-ahead say (6 hours to 1 day ahead) for planning generator shut down and starting decision making, operational security in the electricity market, and reserve capacity requirement planning. The forecasting is done based on real-time wind power generation data from all turbines and meteorological data of wind speed in that region. Long-Term Forecasting This forecasting is done multiple days ahead (say for one day to 1 week ahead) for decision-making regarding generator shut down and startup, spinning reserve capacity, unit commitment and dispatch. The forecasting is done based on data from of national weather forecast and wind power generation for different wind speeds. Very-Long-Term Forecasting This forecasting is done multiple weeks ahead (say for few weeks to several months or years ahead) for optimizing operating costs, operational

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management, and scheduling maintenance. The forecasting is done based on a wide range of data for each season from national weather forecast and wind power generation for different wind speeds [4, 7, 33, 34].

Forecasting Methodologies Various forecasting methodologies are listed below:

Physical Method This is a deterministic technique based on weather forecast data such as pressure, temperature, and numerical weather prediction thereby wind speed information is collected from the meteorological agency and this data is used to drive wind turbines on a wind farm. Statistical Method This is based on a large amount of past/historical data and time series analysis methodologies are used without taking weather conditions into account. Hybrid Method In this weather forecast, data and time series analysis are used extensively in the hybrid technique, which blends physical and statistical methodologies [33, 34].

Demonstration of Forecasting Framework In this section, one-year wind power output data is taken from the KAGGLE dataset repository to demonstrate the time series forecasting framework. After preliminary data analysis, it was found that there is seasonality in the dataset. Therefore, the SARIMA model has been used to forecast power output for different time horizons. SARIMA model is an extension of the ARIMA model, especially for the seasonal dataset.

Data Visualization First we visualize the time series wind power dataset as shown in Figure 9.2, which has been converted from time stamp form to time series form. Then the

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dataset is tested whether it is stationary or not. If not stationary, we convert it to stationary and find the best parameters to build a seasonal model using grid search approach. We used the SARIMA model and searched for its best parameter for forecasting.

Figure 9.2. Time series wind power output data for 1 year.

Testing Stationary From the plot of our time series dataset, we can get a sense of the overall seasonality and trend of the series from the plot. Then, we use the decomposition method to evaluate the trend and seasonality of the dataset. If trend and seasonality are found in the dataset, remove it from the series to convert the non-stationary dataset to stationary, and the residuals are further examined. When shifted in time, a stationary process has an unconditional joint probability distribution that does not change. Consequently, parameters such as mean and variance do not change over time, if they are present.

Figure 9.3. Time series data decomposition for period=1 (hourly basis).

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Non-stationary data are often converted to become stationary, as stationarity is an assumption in many statistical processes used in time series analysis. Figure 9.3 shows the original, trend, seasonality, and residuals of the original signal after decomposition. This shows that the original signal that contained power generation data on an hourly basis has no inherent seasonality and residuals. However, when the same original data is decomposed over different time intervals or periods say at 168 hours (weekly basis), and 720 hours (monthly basis) as shown in Figures 9.4 and 9.5 respectively, shows that the data does have seasonality and residuals.

Figure 9.4. Time series data decomposition for period=168 (weekly basis).

Figure 9.5. Time series data decomposition for period=720 (monthly basis).

Grid Search Using the graphical method to discover the ideal parameters for ARIMA models is not easy and time-consuming. We used the grid search

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(hyperparameter optimization) method, to find the best parameter values. The grid search iteratively explores different parameter combinations. We fitted a new seasonal ARIMA model with the SARIMAX function (SARIMA with exogenous input as wind speed) available in the stats model module for each set of parameters and evaluated its overall quality. Our optimal set of parameters will be the one that gives the best performance for the criteria of interest to us after traversing the entire landscape of parameters. The (p, d, q) parameters are specified by the order argument, whereas the (P, D, Q, S) parameters are seasonal components specified by the seasonal order argument of seasonal ARIMA model. For the given case study, the final value chosen after grid search is order as (1, 0, 1) and seasonal order as (0, 1, 1, 12).

Validating Model Predictions After finding the best parameter values, we fit the SARIMA model and then validate the model to check the fitting performance of the model. We have validated the performance of the model for different time horizons such as three months, two months, one month, one week, one day and one hour and the obtained RMSE is obtained as 273.71, 282.37, 314.56, 355.89, 470.59 and 403.18, respectively which are reported in Table 9.1. Table 9.1. SARIMA and LSTM model comparison for different time horizons Time Horizon 3 months 2 months 1 month 1 week 24 hours

LSTM (RMSE) 290.30 294.21 386.38 364.23 ---

SARIMA (RMSE) 273.71 282.37 314.56 355.89 470.75

Result and Discussion From the survey of forecasters, it was found that interest in time series forecasting is now increasing due to its simplicity and better performance. Thus, the chapter uses the SARIMA model to perform time series forecasting. The prediction performance is evaluated for three months, two months, one month, one week and 24 hours. The performance of the SARIMA model has

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been compared with the LSTM model and the results obtained show that the SARIMA model performed better as tabulated in Table 9.1. Comparisons are also shown pictorially in Figures 9.6 to 9.10 for different time horizons. From the 24-hour forecast plot of figure 10, it can be seen that the LSTM cannot make predictions for the last 15 steps because the lookup value is set to 15 to train the LSTM model, therefore, prediction stopped prior to set lookup steps.

Figure 9.6. LSTM and SARIMA model prediction comparison for three months.

Figure 9.7. LSTM and SARIMA model prediction comparison for two months.

Figure 9.8. LSTM and SARIMA model prediction comparison for one month.

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Figure 9.9. LSTM and SARIMA model prediction comparison for one week.

Figure 9.10. LSTM and SARIMA model prediction comparison for 24-hours.

Figure 9.11. Flowchart of LSTM based time series forecasting.

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Various algorithms have been used in the literature for solar and wind energy forecasting in the past, which are tabulated in Table 9.2. These have also been discussed in the previous sections. It can be concluded from the table that SARIMA model has not been used for forecasting solar PV power and wind power. Also, it can be seen that LSTM has been used by many researchers recently, so in this chapter we compared the SARIMA model with the LSTM model. Also the flowcharts as illustrated in Figure 9.11 and 9.12 represent the algorithms for using LSTM and SARIMA models for time series forecasting. Table 9.2. Algorithms used for solar and wind power forecasting Solar Power Forecasting Reference Year [20] 2017 [22] 2020 -----

[20] [21] [22] [25] [26] [27] [31]

2017 2014 2020 2012 2015 2016 2014

[22] [23] [24] [27] [31] [30] [30] [21] [23] [21] [22]

2020 2011 2015 2016 2014 2021 2021 2014 2011 2014 2020

--[10] [28] [29] [30] [32] [31]

--2020 2020 2021 2021 2009 2014

Wind Power Forecasting Reference Year -----

Algorithms

[36] [37] [38] [39] --[37] [39] [40] [41] [42] [43] [46] [48] [41] [48]

2017 2016 2016 2019 --2016 2019 2017 2017 2020 2021 2020 2019 2017 2019

KNN

----[47]

----2018

RNN GRU GBRT

[39] [41] [45] [46] [46] [48]

2019 2017 2018 2020 2020 2019

RF

--[49] [50]

--2011 2019

AR ARIMA

LR

RT SVR

NN

GPR LSTM

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Figure 9.12. Flowchart of SARIMA based time series forecasting.

Conclusion RE sources are considered to be the most important and promising technologies for future power generation. This chapter has presented a comprehensive assessment of the issues in RE integration, understanding the importance and need of RE-based power generation. Solar and wind power generation and its integration challenges are discussed separately. It is also discussed how accurate solar and wind power forecasts can increase the penetration level of these sources into the grid. In addition, a literature review for machine learning-based solar and wind power forecasting for different time horizons and their intended beneficial application in grid management is also discussed. The literature review has been consolidated with a table for quick reference of the algorithms used. Also, a brief introduction of ML is provided with emphasis on time series forecasting and its implementation framework. Finally, we have applied the SARIMA and LSTM models for wind power forecasting to demonstrate the

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forecasting procedure. The obtained forecasting results have established that the SARIMA model outperforms the LSTM for data with seasonality components.

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Chapter 10

Technological and Communicational Advancements in the Energy Grid: A Review Sandhya Shrivastava*, Anam Tariq, Kirti Verma and Ankit Kushwaha Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, India

Abstract The incorporation of renewable and unconventional energy sources into the current grid system has become crucially necessary as the demand for energy rises and the supply of conventional energy decreases. In this chapter, in-depth information about the energy grid and smart control and monitoring technology is presented. The smart controller enables switching between conventional, non-conventional, and energy storage devices based on their availability status by employing a smart switching algorithm. The notion of the “smart grid” has emerged as a result of several technological developments aimed at enhancing sustainability, real-time monitoring, security, flexibility, reliability, and efficiency. The use of the Internet of Things (IoT), machine learning (ML), deep learning, artificial intelligence (AI), and numerous meta heuristic techniques has led to breakthroughs in the smart grid, which are also covered in this chapter.

Keywords: Artificial Intelligence, Energy Grid, IoT, Machine Learning, PLC, Renewable Energy, Smart Grid, Smart Switching Algorithm, Smart Controller, WiMAX, ZigBee

*

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Introduction In the modern period, there is already a significant imbalance between the supply and demand for electrical energy, and if it is not addressed, it will only widen. This results in a perilous situation where not only are the conventional resources running out but also the ecosystem is not being preserved. The degradation of the environment is brought on by the conventional generation systems’ release of greenhouse gases and other damaging substances. Due to the ongoing exploitation of the raw resources needed for the traditional generating and grid system functioning, numerous ecosystems have also been devastated. As a result, the conventional grid system was able to incorporate renewable energy sources. A hybrid grid is a system that combines nonrenewable and renewable energy sources. It strives to safeguard the environment by preserving traditional resources while supplying constant power. The fluctuating nature of renewable energy supplies makes the utilization of both types of resources necessary. The incorporation of renewable energy sources depends on their accessibility and availability. In this case, solar and wind energy are more widely used than other resources since they are simpler to harvest. For developing nations located in tropical regions, solar energy proves to be a superior alternative, while advances in communication devices and technology pave the way for a grid with better features. The key issue that constantly arises is how to build an extremely sustainable and efficient power system that can deliver power continuously while utilizing new current technology. Smart sensors, new technologies like machine learning, the internet of things, and artificial intelligence (AI) have all been integrated into the development of an energy grid. A smart grid was produced as a result. Clever sensors use a smart switching algorithm to alternate between conventional, renewable, and energy storage technologies. The algorithm makes it easier to implement hybrid power grids. To ensure a constant supply of electricity, the algorithm is created depending on the availability of renewable energy. Other technologies aid in enhancing the effectiveness and dependability of the current grid, allowing us to use energy more effectively and affordably. With the aid of cutting-edge and quick communicational tools, it enables customers to actively engage in the process and lower their own costs. Communication technologies are required for full real-time monitoring, real-time data transmission, control setup, and problem alarm.

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The development of conventional grid systems with the aid of various technologies is thoroughly examined in this chapter. The history of the conventional grid is explained, and technological advancements in a variety of communication channels utilized for monitoring and control are chronologically followed.

Technological Advancement and Energy Grid System The conventional method of centralised power grids has been modified to lessen the cost inefficiency of conventional power generation techniques, which utilise a lot of non-renewable fuels and produce greenhouse gas emissions. An integrated-hybrid grid was developed as a result of the ongoing rise in energy demand and the depletion of traditional energy sources. This problem is addressed by converting a “standard diesel-powered generator” to “variable speed diesel generator” [1, 2]. This system was coupled with solar and wind energy sources to create a hybrid energy grid system (see Figure 10.1) [1].

Figure 10.1. Hybrid energy grid system [1].

A permanent magnet generator and accompanying control technique are used in a variable speed diesel generator set to achieve variable speed generation, allowing the diesel engine to run at speeds below and above synchronous speed in response to load demand [3]. For better remote control of the module, a computer system connected by a bus is used. Maximum power point tracking (MPPT) and multi power controllers, which provide operational management and cost savings, aid in the control [4-6]. MPPT may employ different techniques in addition to the fuzzy logic algorithm to track maximum power. When compared to a typical system, the MPPT shows the peak curve of each system, assisting in better multi-control of peak power in

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the energy grid, which lowers carbon emissions and boosts dependability, improved storage, stability, and resource efficiency. Non-conventional power sources can be employed to attain zero carbon emissions and are dependable long-term power sources. A hybrid system [7] combines wind, hydro, and solar energy for household usage. Compared to independent power plants, a cascaded power generation system has fewer downsides. The hybrid system enables system switching based on the weather, making it ideal in all-weather circumstances [8]. Combining the right modules results in a solar cell array that can produce 0.5 kilowatt of electricity every hour. When facing the sun’s rays on a sunny day, the output can be raised by 30%. Water is poured into an above water tank in a hydropower producing facility, where the water that falls into the tank has kinetic energy that is utilized to turn the alternator turbine sets blades and induce an EMF in the generator’s armature. For the purpose of producing wind energy, wind turbines transform wind kinetic energy into usable electrical energy (see Figure 10.2) [7, 9].

Figure 10.2. Integration of solar, wind, hydro power sources [7].

The modeling and circuit simulation of a hybrid solar-wind power plant, including pitch control and synchronous generator control, are made possible using the MATLAB/Simulink program [10–13]. Multiple systems, including both conventional and unconventional energy sources, are integrated into a hybrid energy system [14]. It produces voltage and frequency with varying values as an output. The output is smoothed using a power converter that uses photovoltaic (PV) sources, a dc-dc boost converter, an inverter, and wind turbines. The system’s efficiency declines and the risk of overheating rises as a result of the variable voltage and frequency settings. With more devices being employed to smooth the output for consumption, the configuration costs more money. It has become crucial to obtain energy that renewable energy sources are incorporated into the current grid system [15]. The challenge of rising fuel and coal prices for energy production in the conventional grid

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system will be made easier by this. The traditional grid model is compared to the smart grid model, with the smart grid model outperforming the traditional grid model in terms of decentralized generation, high efficiency, minimal environmental effect, and self-monitoring features [16, 17]. As a standby energy source for the main power grid system, renewable energy technologies can be incorporated into the smart energy grid model. A solar smart energy grid system was suggested in this study [18] because it is the most accessible source of electricity. Different models were created for customers to synchronise grid and PV power supply [19, 20], but they were not economically viable in regions where frequency and voltage variations, as well as grid cut-off, are frequent occurrences. For the emerging nations, it has grown to be a significant issue. Smart controllers for solar hybrid grid systems have been created to address these issues and maximise the use of solar energy [21]. This article explains how to build a smart switching sensor with a microcontroller that can switch between grid, battery, and photovoltaic power based on availability (see Figure 10.3) [21].

Figure 10.3. Switching the load between grid and solar PV/battery [21].

A 16-bit AVR microprocessor manages it. This microcontroller receives a smart switching algorithm, and generates an output signal as a result. The scenarios in Table 1 [21] are used to build the algorithm. For rapid switching, a static relay with a 5V DC rating is used. Real-time monitoring is done of the grid’s condition, battery health, and solar power. In a similar manner, a switching algorithm was proposed for a wind-solar power generation system that switched by comparing battery charge levels. Figure 10.4 shows the configuration, which includes an ATmega-2560 microcontroller, a solar panel (20W, 12-30V), a selector board with 8 relays, a wind turbine, and a visual studio interface for remote system control and monitoring [22–24].

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Table 10.1. Various power sources with different operational cases Power Supply Cases When battery is fully charged and grid is active When battery is fully charged and grid is inactive When battery is insufficiently charged and grid is active When battery is insufficiently charged and grid is inactive

Figure 10.4. Control algorithm [22].

Power Supply Source Solar feed is used to meet the requirement and additional requirement is met from grid. Solar and battery feed supply to the load. The load is fed from the grid and the entire solar feed is used to charge the battery. The loads are fed by solar and batteries.

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Light emitting diodes (LEDs) are used to indicate the source powering the inverter at a given time in the Visual Studio interface, and this has a communication port (COM) to select the communication port. Included is the section of the Visual Studio user interface that shows current, voltage, and power. This prototype demonstrates an efficient technique for overcoming each energy source’s drawbacks when utilized alone. A certain system may have more energy sources since the supply system now has more ports [25, 26]. This shows that in order to guarantee remote wireless connectivity, internet of things (IoT) must be added to a hybrid architecture. An ArduinoUno microcontroller and a relay rated for 5V DC were suggested for a related variant. Smart switching controllers are employed to reduce energy consumption and improve grid efficiency (see Figure 10.5) [27–30]. Real-time monitoring of battery and AC voltage consumption was made possible with the aid of IoT. (Figure 10.6 [27]). The data was shared to the online server “ThinkSpeak.com” using the ESP8266 module.

Figure 10.5. Smart Switching Controller Algorithm [27].

Figure 10.6. Block diagram of the switching algorithm for hybrid solar-grid system [27].

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Another piece of literature [31] focuses on supplying grid technology and renewable energy to street lights. It makes use of a smart controller prototype for a hybrid grid system that also uses solar power. This model uses the least amount of energy and complies with highway laminating regulations. The current optimization of standalone street lighting systems with hybrid systems has received support for a number of techniques [32, 33]. The main grid, a battery, and hybrid systems all contribute to effective energy management [34]. Through the use of a charge controller, the battery is charged from solar energy while the voltage is continually monitored for scaling. The scaleddown voltage is inputted into pin RB0 of the microcontroller PIC16F877A, and the relay is linked to the output pin. The source of the LED lamp load is the main grid when the battery is low and the battery is used when the output is high. The maximum depth of discharge (DOD) in this instance has been set at 50%. When the battery reaches 50 percent, it is unplugged, and the main grid takes over to supply the demand for street lights (Figure 10.7). The entire configuration is being optimised and put into use with DIALux software [35].

Figure 10.7. Switching action for supply selection [31].

As was previously said, many electric operators around the world are concerned about and having problems with the widespread usage of renewable energy. This is as a result of the anxiety of instability it causes. The hybrid grid system effectively addresses this issue. However, efforts were made to make the current grid self-sufficient as various technologies advanced. The electrical grid, automation, communication, and IT systems are all integrated. Within networked systems, it offers real-time monitoring, analysis, and control. As a result, energy consumption is reduced, and a dependable, selfsustaining, and two-way networked system has been created. A reliable, secure, demand side management system, micro grid, integrated renewable resource use, and self-healing smart grid should all be included. It makes sure the smart grid is effective, delivers a steady supply of electricity, is safe from cyberattacks, finds and fixes defects, and allows two-way communication

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between the generating station and the consumer (Figure 10.8). Table 10.2 lists more developments in smart grids.

Figure 10.8. Smart grid architecture [36].

Table 10.2. Stages on evolution of smart grid

Metering

Transmission Grid

Distribution Network

Integration

Elementary Stage

Evolutionary Stage

Largely manual metering. Some automated meters for large industrial users. Zero automation of distribution network including substation & circuit breakers. Manual fault localization. Zero automation of distribution network including substations & circuit breakers. Manual fault localisation

100% smart meters with automated meter reading and real time display.

Fully Integrated Smart Grid Advanced metering allowing real time rate changes and remote ON/OFF capability.

Ongoing automation of high voltage (HV) system and substations.

Full automation of HV systems and substations switches and remote control methods

Partly automated switches & circuit breakers along medium voltage (MV) lines for fault identification. Online monitoring of flows in the transmission grid and ability to dispatch balance system.

Fully remotely automated distribution network with remote sensing and voltage control capability

Basic communication between grid components. Limited ability to control.

Total integration of supply and use of electricity. Ability to control dispatch and usage remotely.

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One of the technologies used to create smart grids is IoT. It is frequently used to enhance self-healing, disaster recovery, and grid monitoring. It is used for smart billing because it can gather a lot of data. The condition of numerous devices used to build the system’s resilience can be updated and reported on by IoT sensors [37]. Additionally, it has been used to remotely monitor the grid. But there are several obstacles to overcome when integrating IoT into smart grids. These include the possibility of cyberattacks, preserving the conditions necessary for IoT sensors to operate properly, and limiting data loss when transferring from one location to another (Figure 10.9) [37].

Figure 10.9. IoT with its connections and related entities [37].

The method offers a model for energy redirection based on the Internet of Things. The ESP8266 Wi-Fi module, along with manual and remote controls, are used to control the system [38]. The strategy discussed alternating between several resources, including solar, wind, and batteries. The ON/OFF switch allows for manual control, and the computer or smartphone system allows for remote monitoring and control of the system [39, 40]. This is done using the Arduino-IDE and the ESP8266 Wi-Fi module (Figure 10.10) [41, 42]. It is a dependable and economical technology that offers regular failure maintenance and hybrid system check-ups. This prototype has a bright future even if it is still in the early stages of development.

Figure 10.10. Receiver section of smart controlling system [36].

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Additionally, WOT (Web of Things) integration is necessary for hybrid grid systems to utilize renewable energy sources to their full potential. The LPC1768 processor is connected to Ethernet ports on the ARM Cortex M3 processor-based device, which connects to port [43, 44] via the LwIP protocol suite. In order to convey real-time data from smart meters installed at various sites, a special web page was developed [45, 46]. Data is transmitted between the embedded device and the web interface using the MODBUS protocol. Any user can access the offered services by entering their user ID and password. The user can schedule their power usage based on their demands, switch between sources, and manage or monitor the load remotely in addition to doing so. This strategy minimizes carbon emissions because it brings the model to the household level, enables individual households to monitor their usage on their own, and allows for individual household tracking. The model [47] utilized WOT similarly to [44], but added automatic fan switching using thermal sensors and a billing mechanism that exempted customers from paying for the energy generated by the PV panels installed in their homes (Figure 10.11). This lowers their electricity costs even more because they can now monitor their usage while simultaneously saving money by switching to a sustainable energy source [48–53].

Figure 10.11. Block diagram of smart grid system using WoT [48].

Smart grids have explored a wide range of artificial intelligence (AI) to offer strong technical support for digital power networks. Current power systems are being advanced by the employment of some of its methodologies, including Fuzzy Logic (FL), Artificial Neural Networks (ANN), Expert Systems (ES), and Genetic Algorithms (GA). These methods aid in managing

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and running the power system. It also addresses the intricate architecture of power systems. They also serve as a useful tool for enhancing the security and dependability of the power system by offering tools for forecasting and fault diagnosis. Over the past few decades, AI has advanced incredibly quickly [54]. The automated design of contemporary wind production systems and its health monitoring are just two examples of the many applications of artificial intelligence that may be seen. Figure 10.12 depicts the automated design of a contemporary wind generation system using an expert system (ES). ES are intelligent computer programmes that use boolean logic to simulate the problem-solving and decision-making processes of human experts. In order to find a solution to an issue, ES uses data and heuristics. The ES shell in Figure 10.12 serves as a platform for the ES program’s development. All the data, which serves as the system’s database, is included in the ES shell.

Figure 10.12. ES-based automated design, simulation, and controller tuning of the wind generation system [54].

The Adaptive Neuro-Fuzzy Inference System allows for the health monitoring of wind generation systems (ANFIS). Fuzzy logic and neural networks are combined in ANFIS. As a result, it has the benefit of combining both of their advantages into a single framework. A series of fuzzy if-then

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rules that can roughly mimic nonlinear functions makes up the inference system. The Takagi Sugeno fuzzy inference system is the foundation of ANFIS. PV and other systems’ health can be tracked using ES and ANFISlike technologies.

Figure 10.13. ES-based master controller of smart grid supported by the supercomputer-based real-time simulator [54].

The detection of space vector fault patterns using neural mapping in smart grids is another application of AI. The type of system fault can be identified using the smart grid’s voltage and current wave data. To locate system flaws, the space vectors of the three-phase line voltage and current signals are calculated, observed, and then examined. Smart grid master control employing supercomputer-based real-time monitoring simulators is another significant application of AI. The system’s ES-based database relies on previous analysis of the entire system to function. The master control of the smart grid utilising a supercomputer-based real-time monitoring simulator is shown in Figure 10.13. Although the integration of AI in smart grids is a boon, it still faces some challenges; some of them are:

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Integration of renewable energy poses the challenge due to its variability and unpredictable nature. Cyber risk due to neural network protocol operating system and physical devices is also a major challenge in the current market system. Big data storage for smart grids is also a concern for improving the performance of existing grid systems.

Future iterations of the smart grid system may include a variety of technologies to create a fully self-learning, highly personalized, and responsive system. Cloud computing, fog computing, transfer learning, consumer behavior prediction, etc. are some of these strategies. [55]. A kind of artificial intelligence known as machine learning (ML) can be used to increase the stability, effectiveness, security, and responsiveness of smart grids. It is the research of algorithms for enhancing systems by gathering data sets. The integration of machine learning (ML) into smart grids enables them to react to abrupt changes in demand, power shortages, and changes in the production of renewable energy. It also aids in forecasting and the prediction of consumption patterns. Additional ML integration can be helpful for determining transformer life expectancy, identifying power quality problems and defects, enhancing electricity market operations, and identifying weaknesses and dangers in smart grids. ML is an effective method for supplying cyber security in smart grids because it can be used to identify and stop cyber threats. ML is tremendously advantageous for the reasons listed above, but there are several difficulties that must be overcome when putting it into practice. Finding quickly labelled data is one of them, as is the growth of new cyberattack kinds, abrupt changes in fault patterns, poor memory, and the processing capacity of smart meters [56, 57]. Although AI and ML have shown to be excellent tools for advancing smart grids, deep learning (DL) is now being implemented into smart grids for future advancements, handling large amounts of data, and circumstances (where ML fails to manage the data). Neural networks are used in this ML domain to extract multi-dimensional patterns from massive volumes of data (Figure 10.14 [58]). The smart grid uses the DL algorithm effectively to extract accurate information about power usage from the massive amounts of data that are collected continuously across the system from diverse sources. Data analysis, bettering energy efficiency, and managing power system demand all benefit greatly from this [58].

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Figure 10.14. Illustration of AI, ML and DL [58].

Traditional grids are being replaced with smart grids, which has increased the usage of big data analysis. Big data analysis is essential for improving the smart grid’s efficiency and decision-making abilities. Big data is not only used by smart grids to extract intelligence from raw data and build that intelligence, but it is also used to support other business-based applications for smart grids. The smart grid generates a large amount of data, which is then categorized based on structures. It has the added benefit of dealing with unstructured, semi-structured, and query able structured data in addition to structured data. Additionally, it uses a number of analytical techniques, including correlation, factor, and cluster analysis. These methods are all employed for static purposes. Huge amounts of data are produced by smart grids from a variety of sources, including user electricity consumption patterns, geographic data, smart metering infrastructure, energy consumption data, etc. It is possible to discover locations with high electrical loads, regions with high power outage frequencies, or transmission lines with a high failure probability by evaluating the data provided in the grid, making it possible to identify grid upgrading and maintenance areas.

Communicational Advancements in Energy Grid System As communication is the key to today’s smart grids, the development of smart grids has accelerated with the advent of cutting-edge communication technology. Control, monitoring, and visualization are some of the major features of a smart grid that depend on the communication technology, which might be wired or wireless (Figure 10.15). Hybrid communication

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technologies are used by current electric utility systems to enable a variety of applications. It begins with domain-based communication since it refers to the particular collection of information that is communicated to the appropriate audience in accordance with the requirements and task level. These top priorities include sophisticated metering infrastructure, distribution business management, cyber security, renewable energy integration, and many others.

Figure 10.15. Smart Grid architecture [59].

The different communicational advancements are as follows: •

One of the earliest forms of automation in the electrical grid was power line communication (PLC). For two-way communication, this also uses a modulated carrier signal over an existing connection. It is divided into two groups: broadband and narrowband (3-5 kHz) (2250 MHz). Broadband technology is best for end-user entertainment and internet, whereas narrowband is better for sensing and communication. Medium and low voltage grids and substations are ideally suited for PLC. The major challenge was low data rate communication and involved location, isolation, and path restoration. The most reliable smart grid technology is thought to be PLC. When compared to other current and suggested solutions, the solution’s

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overall cost for implementation and upgrade is much lower. With forward error correction, data interleaving, and data transfer under challenging circumstances, orthogonal frequency-division multiplexing (OFDM) technology has improved robustness. In the event that communication is broken, this can be decreased by switches, sectionalizes, and reclosers [60]. The PLC also includes 128-bit advanced authentication encryption standards for data integrity and authentication, adding to its security characteristics. Narrowband PLCs are more straightforward to install and upgrade [61]. Another well-known, low-power wireless communication technology is ZigBee, which is built on the media access control layer and physical layer. With data rates as low as 300 kbps, the ZigBee runs on the ISM (industrial, scientific, and medical) radio bands of 868, 915 MHz, and 24 GHz adaptive direct sequence spread spectrum (ADSS). Devices can be enabled by ZigBee in two different ways: fully functionally and minimally. Coordinator, router, and end device are the three separate nodes, and any frequency division duplex (FDD) or radio frequency identification (RFID) device can be an end device. In comparison to other technologies, ZigBee network nodes have the ability to be low powered and energy efficient. ZigBee can be used as a kind of wireless communication since it uses a channel mixture and spread spectrum modulation strategy to prevent interference, as well as mesh networking technology to aid in routing repairs and enhance the robustness of the network architecture [62, 63]. In graphical user interface (GUI) based monitoring and control setups, it is an excellent technology to assist with data collecting, energy savings, safety protection, carbon reduction, metering, and load computation. The most widely used wireless technology is wireless local area network (WLAN), often known as Wi-Fi, which provides for operation in the 15-meter band of 2.4 GHz and DSSS with data ranges up to 11 Mbps and wireless ranges up to 30 to 40 meters using OFDM. Up to 54 Mbps can be added to the range. This extensive range enables choices for sending and receiving data from point to point and point to many points. With the addition of electrical equipment data, it is very successful [64–67]. Orthogonal frequency-division multiple access (OFDMA) with a multiple-input multiple-output (MIMO) based antenna is used in the

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physical layer of global interoperability for microwave access (WiMAX) to enable non-line of sight capabilities. It operates at a frequency of 11-66 GHz for line of sight and 2-11 GHz for various non-line of sight applications [68, 69]. For security, it makes use of the designated employer representative (DER) and the data encryption standard (DES). WiMAX is specifically made for pointto-multipoint data sharing, with a range of 50 km and a maximum data throughput of 70 Mbps. It works well for long-distance communication and is a feasible choice for remote monitoring of the entire power plant, the distribution area, and the end server. It is very secure, with quality of service (QoS) oriented WiMAX acting as a third party communication provider between service provider and the consumer [70, 71]. With frequencies spanning from 900 MHz to 1800 MHz and data rates up to 270 kbps, the most widely used cellular networks globally are the Global System for Mobile Communication (GSM) and General Packet Radio Service (GPRS). Mobile headsets, base station subsystems, network switching substations, and operator support substations make up the fundamental parts of GSM and GPRS. With SMS warnings and emergency controls, they are particularly successful at monitoring, controlling, and automating household loads in smart grids. The service provider is in charge of the communication [72]. The 150/IEC 1800-7 standard is used by the new wireless communication technology known as Dash-7. It is designed to operate at 133 MHz for active radio frequency identification devices (RFID). With data speeds of up to 28-200 kbps, it has a range of up to 250 meters and can be extended up to 25 kilometers. Because it uses little power, the battery lasts a long time. They have a 30-60 microwatt power draw and a latency of 2.5–5 seconds. It transfers data using the simple local alignment search tool (BLAST) approach, which is a simple to administer and reasonably priced solution for node deployment. Real-time data packing benefited greatly from this, and academic research is still being done on the remaining aspects of this communication technology [73]. The demand for high range coverage communication has increased as a result of the introduction of numerous smart technologies such as smart meters (SM), phaser management units (PMU), and electric drives (EDs). Due to the improved effectiveness and low cost of

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satellite communication (SATCOM), it has emerged as a viable technology to utilize and is also very successful in distant and terrestrial locations. While SATCOM is being utilized in the commercial sector, boosting its profitability and raising everyone’s quality of service. It is a dependable source to use because there are numerous satellites that cover the entire world in all weather conditions. With a variety of parametric communicational arrangements, such as video surveillance, mobile workspaces, transmission and distribution monitoring, and remote substation monitoring, the smart grid is capable of carrying out several tasks at various levels (Figure 10.16). It is a whole solution for efficiently monitoring and managing smart grids at all levels, from generation to consumer [74]. Additionally, it is crucial in assisting with data linkage, intricate calculations, and data representation.

Figure 10.16. Domains involved in smart grid applications [74].

Conclusion The chapter presents a systematic examination of the communications and technological advancements made in hybrid grid and smart grid systems. Discussions of various arrangements’ features, applications, and advantages

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are included. Microcontroller advancements facilitated the creation of systems with superior smart switching sensors and algorithms. Different models have been put up to improve hybrid systems’ output. IoT integration has given customers and utility providers access to cutting-edge capabilities like realtime system monitoring and remote system control, which has helped to increase system productivity and better system maintenance. By incorporating fault diagnosis and forecasting features, advanced technologies like artificial intelligence, machine learning, and deep learning have helped to improve the security, reliability, and responsiveness of smart grid systems. The addition of big data analysis has further improved the decision-making capability of smart grid systems. These technologies have been incorporated, which has resulted in the creation of an intelligent system that is self-sufficient and able to address the problems with conventional grids. This chapter also covers how communication evolves over time, which is a key component of contemporary smart grids. Future smart grid systems could combine several approaches to become fully self-learning, incredibly adaptive, and responsive systems. Cloud computing, fog computing, transfer learning, consumer behavior prediction and other approaches are among them.

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Chapter 11

Renewable Energy and Energy Storage Systems Hemlal Bhattarai Jigme Namgyel Engineering College, Dewathang, Bhutan

Abstract Renewable energy sources are currently becoming the most promising sources for electricity generation as the focus on mitigating climate change is gaining momentum across the globe. Control measure in the usage of fossil fuel-based power generation and utilizations are proving to be very costly to people’s health and the environment. As pressure mount on the issues of addressing climate change, the scales of renewable energy penetration is becoming more significant. Renewable energy sources like solar and wind show promising potential but these sources are weather and season dependent. This dependence require the use of effective energy storage systems. Reliable energy storage systems have thus become a core considerations when there is a need for reliable, high quality power supply. The development and advancement of energy storage systems is essential when working with both stand-alone and grid-connected renewable power system. Advancements in science and technology have produced many energy storage systems options, each with their own merits and drawbacks. However continued research and development into energy storage systems is crucial if the realization of renewable power for meeting energy demand is to be successful.

Keywords: battery energy storage, climate change, energy storage system, renewable energy 

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Introduction The need for energy in day-to-day life is quite significant. Energy consumption has been increasing at a very rapid pace in the recent decades due to both population increase and accelerating. One energy source that has significant impacts in all walks of life is electrical energy. Electrical energy has become crucial for day-to-day life as well all become more dependant on advancements technology. Electrical Energy has the advantages of ease of usage and the flexibility to move from one place to another but has a key challenges when it comes to the storage of larger quantities [1].

Figure 11.1. Trends of global primary energy consumption by source [2].

The key objectives of this chapter are to understand the energy systems that are currently employed in power sectors, the key roles of the renewable energy system in power sectors, the requirements of energy storage technologies, and the appropriate application of energy storage in realizing stable and reliable power system through integration of renewable energy. The historical trend of global electric energy consumption is shown in Figure 11.1 which clearly shows the overall growth of primary energy consumption using different sources of energy. It is evident from Figure 11.1 that the energy consumption across the globe is still fossil fuel dominant with coal and oil as the major contributors. Global CO2 emissions from fossil fuel were recorded at 34.8 GtCO2 in 2020 compared to 36.7 GtCO2 in 2019 [3]. The decrease in

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2020 could certainly be due to the impacts of the COVID-19 pandemic and it is expected to grow in the coming years. Furthermore, it is always good to know the prevailing trends and forecasts on global primary energy consumption. Such understanding is useful in considering possible interventions. Figure 11.2 depicts the historical trends and the projection of the global primary energy consumption by 2050.

Figure 11.2. Trends and projections of global primary energy consumptions by various energy sources (in quadrillion British Thermal Units (BTUs)) [4].

It is evident from Figure 11.2 that there is an increase in renewable energy consumption and the forecast trends are quite promising too. However, the projection of fossil fuel trends is also growing in years to come and this is something we all have to be concerned about.

Renewable Energy and Its Prospects The increase in energy demand in power sectors for meeting growing needs is faced with stiff challenges. The power sectors which are mostly fossil fueldriven are under great pressure due to global climate change issues. The option that work in line with combating climate change is to shift from fossil fuel to renewable energy. Even though renewable energy is the most talked about and sought after energy source option in recent time, it does have its share of issues.

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Figure 11.3 shows the variation in consumption from different energy sources, including renewable energy sources, over the last few years. It is clear from Figure 11.3 that despite the recent growth of renewable energy, the dependence on fossil fuels for energy demand remains significant and the contribution from renewable sources is very small. The figure also shows that renewable energy sources like hydro have seen at growth of 1.9% per year, while other renewable sources have grown by 13.8% per year in the given time frame. The same research findings also highlighted the critical concern related to global annual fossil fuel-related CO2 emissions along with statistics of major countries as shown in Figure 11.4.

Figure 11.3. The trend of global energy consumption by sources [5].

Taking account of the dependence on fossil fuels for energy requirements and also realizing the potential of global CO2 emissions from fossil fuel usage, it is clear that actions against climate change can be strongly addressed in the energy sector. The power sector is one of the main contributors to global CO2 emission, and the need for stronger policies and regulations in combating climate change is important [6, 7]. A report from Statista [8] listed major countries with greater than 2 million inhabitants that have growing emissions.

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Figure 11.4. Trends of global annual fossil CO2 emissions in last decade [5].

It is clear from Figure 11.5 that the CO2 emissions growth (in tons per capita) is from 4.99 to as high as 13.31 which is quite significant to note. Figure 11.5 also shows that in the given period, the growth is as high as 242.8% for China, 125% for Oman, and 123.3% for South Korea. These growth rates make it clear that strong interventions that facilitate and promote the shift towards renewable energy sources for energy needs is needed. Figure 11.6 shows the pattern of growth in renewable and non renewable energy in last two decades. From Figure 11.6 it is clear that the energy transition in recent times is more in the form of renewable energy. At the same time the generation capacity of non-renewable sources is declining. This is something promising when the concern of climate change and its mitigation measures are progressively growing threats.

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Figure 11.5. Countries having significant CO2 emissions in recent times [8].

Figure 11.6. Renewable and non-renewable capacity addition and energy transition [9].

While talking about renewable energy, it is important to note that each form has its own share of merits and drawbacks. On one hand, it is clean energy which we should always strive for but on the other hand, there is a

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higher investment in terms of resources and time needed for initial setup. This might explain why renewable energy sources are sometimes not seen to be taken seriously. To complicate the situations even more, most renewable energy sources (i.e., solar and wind power system) are heavily dependent on an energy storage system for reliable operation as their capacity fluctuates with time and season. Solar irradiances are irregular, wind speeds do fluctuate and rainfall patterns heavily impact hydro generation.

Energy Storage Systems An energy storage system (ESS) is one of the core components of a renewable energy systems. The primary role of ESS is to provide reliability in energy supply through generation and storage during favorable weather/duration/time and to meet the need for a continuous energy flow. A better ESS will facilitate large-scale renewable energy generation and will always supplement the energy requirements which otherwise have to be met from fossil fuels. An International Renewable Energy Agency (IRENA) report highlighted that energy storage (ES) will be the backbone of power transition along with crucial roles of decarbonization in the energy market [10]. There are several types of energy storage technologies, including electrochemical, mechanical, and electrical/magnetic fields as shown in Figure 11.7.

Figure 11.7. Energy storage technologies [11, 12].

As energy needs are continuous and generation is subjected to multiple factors, the demand in energy storage has been quite significant while dealing with two critical renewable energy sources (i.e., wind and solar power).

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Researchers have noted that in power system networks, excess energy can be used to charge energy storage that can then be used safely after sunset, obviating the need for grid-connected utility power demand [13].

Roles of Energy Storage (ES) Technologies One study has found that when intermittent renewable energy technologies are unable to generate power, backup power is required (to maintain grid stability); and that energy management services that enable system operators to predict when they will be able to use electricity generated by intermittent renewable energy sources is useful [14]. With the integration of renewable sources (like solar, wind) into power system networks, grid imbalance issues become pertinent. An energy storage system (ESS) can be regarded as a critical solution for compensating for such imbalances and improving the reliability and stability of the power system. Also, while integrating renewable energy sources into the power system network, ESS can be employed to reduce associated difficulties. As a result, an ESS works to reduce greenhouse gas (GHG) emissions by effectively integrating additional renewable energy sources into the grid [15, 16]. Figure 11.8 shows the major impacts of energy storage services in boosting renewable energy [17]. The role of energy storage services is thus crucial in realizing the impactful contribution of renewable energy. Some of the key benefits of energy storage systems include [18]: •

• •



Saving Money: By storing low-cost energy and using it later, during peak periods when electricity rates are higher, energy storage can minimize the cost of providing frequency regulation and spinning reserve services, as well as balance consumer costs. Enhancing Reliability and Resilience: An ESS provides backup thus maintaining an uninterrupted power supply. Integrating Energy Sources: An ESS will facilitate the integration of energy sources like the solar, wind in the system and will also provide flexibility during fluctuation in power demand. Lowering Environmental Impacts: An ESS can facilitate safe energy and can be used when needed.

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Figure 11.8. The range of services that can be provided by electricity storage [15, 16].

Critical Parameters of an Energy Storage Device While dealing with energy storage devices there are multiple aspects to be aware of. Some of the critical parameters that are essential while exploring energy storage device are [19]: •

• •

Power Capacity: The greatest instantaneous output of an energy storage device, commonly measured in kilowatts (kW) or megawatts (MW). Energy Storage Capacity: Tthequantity of electrical energy that a device can store in kilowatt-hours (kWh) or megawatt-hours (MWh). Efficiency: The amount of electricity that can be recovered as a percentage of the total amount of electricity needed to charge the item.

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• •

Response Time: The time taken for the storage device to begin discharging power. Round-Trip Efficiency: The amount of energy that can be recovered as a proportion of the total energy needed to charge and discharge the device.

Classification of Electrical Energy Storage Technology When applying energy storage, the critical understanding of energy storage becomes most vital. Accordingly, the classification of electrical energy storage technologies has to be explored. The classification of electrical energy storage technologies is as shown in Figure 11.9.

Figure 11.9. Energy storage technologies [20].

Six typical classifications are shown in Figure 11.9 with the scopes of its incorporation. This will provide the user the required background of choosing the best technologies for the given applications. Energy storage technologies that have penetrated power systems must be critical and efficient to achieve the intended advantages based on its application. The energy storage

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technologies for various power sectors are shown in Figure 11.10. It is clear from Figure 11.10 that multiple options for energy storage technologies can be carefully chosen for the types and scopes of each power sector. The options should certainly be explored based on required qualities, measures as well as intended effectiveness in the intended application. The role of users thus is crucial in identifying what are available options and what are the requirements of the system as a whole. Short-term assistance is required for solar photovoltaic (PV) plants during clouding, load shedding, and short-circuits. Energy storage with high power capabilities, rather than high energy capabilities, can give this support [21]. Along with that, the preference for a faster discharge rate ESS is preferred where the modern ESS such as flywheels, ultracapacitors, compressed air energy storage, and superconducting magnets are designed with such characteristics [22, 23].

Figure 11.10. Energy storage technologies across the power sectors [24].

A Comparative analysis of ESS in terms of energy density and power density is shown in Figure 11.11 where it can be seen that battery technologies have higher energy densities compared with other options. In power system applications where consideration of frequency regulation is vital, the ESS with higher power density is the preferred choice. Figure 11.12 shows the comparison of power and discharge time for various energy storage technologies. There is a need to comprehend the various possibilities for energy storage systems that are already in place, as well as their strengths and weaknesses.

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Figure 11.11. Plot between energy density and power density of various energy storage technologies [25].

Figure 11.12. Comparison of power and discharge time for various energy storage technologies [26].

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Benefits of Energy Storage System Advanced energy storage offers an integrated solution that includes electric grid modernization, reliability, and resilience, as well as sustainable mobility, flexibility for a diverse and secure electricity generation portfolio, and improved economic competitiveness for remote communities and targeted micro-grid solutions.

Figure 11.13. Benefits of energy storage systems [27].

The most noted benefits from energy storage systems, as shown in Figure 11.13 [27], are renewable energy integration, increasing system flexibility, enhancing the reliability as well as the resilience of the grid, and optimized utilization of the resources. Such aspects are crucial when integrating renewable energy into the power system and working towards secure and reliable power system networks.

Key Grid Energy Storage Technologies Several technologies have been devise which will provide the scope of energy storage. The followings are a few of those [27]: •

Batteries: Electrochemical battery types include lithium-ion, sodiumsulfur, lead-acid, and flow batteries. These batteries vary in energy density, power performance, lifetime charging capabilities, safety, and cost.

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Pumped Hydroelectric Storage: Water pumped from a low reservoir to a high one is later released through a hydroelectric turbine to generate electricity as needed. Compressed Air Energy Storage: Compressed air is stored in an underground cavern until it is heated and expanded in a turbine to generate electricity. Thermal Storage: Heat is captured and stored in water, molten salts, or other working fluids for later use in generating electricity, particularly when intermittent resources (e.g., solar) are unavailable. Hydrogen: Hydrogen can be stored and used later in fuel cells, engines, or gas turbines to generate electricity without harmful emissions. Flywheels: Electric energy is stored as kinetic energy by spinning a rotor in a frictionless enclosure. Flywheels are useful for applications such as power management.

Battery Energy Storage System (BESS) Batteries have shown their capabilities as an ESS and are one of the most sought-after technologies. Battery energy storage systems (BESS) are modular systems that can be standardized, but the associated high cost and low round trip efficiencies limit their mass application. However, the use of lithium-ion batteries in many applications areas has increased their production. This has reduced the cost of these systems by a greater margin and this is expected to continue. Hence, BESS has boosted its growth in various scales of applications including grid-level deployments. The application of BESS thus overcomes challenges related to the integration of renewable energy to the power grid as batteries have advantages of better frequency regulation and better value for network expansion. Also, it can be the best and cheapest solution for peak demand requirements facilitating the export of surplus power where possible. In any case, the financial viability of a BESS project for renewable integration will depend on the cost-benefit analysis of the intended application [28]. Figure 11.14 shows the key components of a BESS, which includes battery packs, a battery management system, a power conversion system and supervisory control system [29]. The system is connected with required converters and control as well as a data acquisition system (SCADA). This as

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a whole constitutes a complete package of a BESS. Furthermore, a BESS can be classified into primary and secondary systems which constitute different types of batteries for realizing the needs of intended applications as shown in Figure 11.15 [30].

BMD = battery management system, SOH - state of health, SOC = state of charge, T = temperature. Source: Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications. Figure 11.14. Overview of battery energy storage system components [29].

Figure 11.15. Classification of battery energy storage system [30].

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It is also important to understand the price of each kind of storage, as well as the total cost impact of energy production, which are crucial economic metrics. Figure 11.16 compares investment costs for some of the key energy storages technologies per unit of energy. It depicts the cost of various options of energy storage devices with options for its selection for appropriate applications. Cost per cycle is a useful metric for determining the cost of a storage system that is intended for repeated charging and discharging cycles. Figure 11.17 depicts the cost of various technologies when their durability and efficiency are taken into account. It highlights the investment costs of storage technologies for each charge-discharge cycle.

Figure 11.16. Investment costs of storage devices [11].

Figure 11.17. Investment costs of storage technologies for each charge-discharge cycle [11].

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The cost aspects need to be critically analyzed and proper actions need to be taken when investing in renewable energy along with the necessary ESS. The overall investment thus would be justified if proper analyses are always tagged with adopted options of investment plan for given actions.

Applications of Energy Storage System Numerous benefits can be realized with the use of an ESS in various applications in the power sectors. Some of the crucial advantages of ESS as shared by researchers are worth understanding whenever planning for actions to have ESS [31]. Figure 11.18 shows the benefits associated with the usages of ESS for power system objectives in different application services.

Figure 11.18. Application of energy storage system [31].

Discussion This chapter has pointed out that there is significant growth happening in power sectors in recent decades as energy demand has been increasing regularly. The growth in such demand has further encouraged the usage of fossil fuels to meet the growing electrical energy demand. It is also noted that although renewable energies are picking up in power sectors, there are issues

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related to the requirements of energy storage systems since most of the renewable energy sources (i.e., solar and wind) are seasonal/weather dependent. The development of different energy storages technologies has been pointed out in this chapter with battery energy storage system as one of the most used options for energy storage. Finally, this chapter realized that the types of energy technologies can be selected on the basis of scope, requirements and cost which can be realized according to the demand of a given application.

Conclusion The role of renewable energy sources has been the most sought-after solution for the energy transition in current times when the entire globe is bracing with the impacts of climate change. As the energy sectors happen to be one of the major consumers of fossil fuels and emittors of greenhouse gases, there is a need for clear policies and regulations to discourage the use of fossil fuels and to shift dependence to renewable sources for energy requirements. The challenges and issues pertaining to renewable energy growth and its transmission have to be well addressed with necessary support mechanisms where necessary. One of the crucial interventions needs to be a focus on energy storage systems which can be instrumental in realizing the practical implementation of renewable solutions for standalone as well as gridconnected applications. Such measures require critical analysis and understanding with need-based experimentation for the practical size of an EES as well as determining the best possible ESS options for an application. As a result, the deeper research in exploring the best options for ESS will be the key aspect that will realize the potential growth of renewable energy usage and its transitions in years to come.

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Chapter 12

Review of Energy Storage System Technologies in Microgrid Applications: Characteristics, Issues and Challenges Hannan Ahmad Khan1, Mohd. Zuhaib1,*, Mohd. Rihan1 and Anil Kumar2 1Zakir

Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India 2Ministry of New and Renewable Energy, New Delhi, India

Abstract The problems caused by the large-scale integration of renewable energy sources (RES) into the grid have attracted the attention of power system researchers to transform the traditional power system to a more adaptive and flexible one. The overall power generation scenario is shifting towards environment friendly renewable energy sources such as solar photovoltaic systems, wind power generation, and hydropower. The intermittent nature of renewable energy sources requires some energy storage systems (ESSs) to minimize their effect of intermittency on the grid. This chapter critically examines energy storage technologies such as electrochemical and battery energy storage, flywheel energy storage, compressed air energy storage, pump energy storage, magnetic energy storage, etc. It provides an in-depth review of energy storage systems considering the state-of-the-art technology, characteristics, challenges, applications, global status and economic analysis. It also includes recent research on new energy storage types as well as important achievements and discoveries in energy storage. *

Corresponding Author’s Email: [email protected].

In: Energy Conversion Editors: Saurabh Mani Tripathi and Asheesh Kumar Singh ISBN: 979-8-88697-370-9 © 2023 Nova Science Publishers, Inc.

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Keywords: energy storage systems, energy management, ESS technologies, distributed generation, microgrid, renewable energy system

Introduction Renewable energy exists in the form of solar, wind etc. to meet the energy requirement of the nation. Renewable energy integration is so attractive that all countries are taking interest in it [1, 2]. Total grid-interactive renewable capacity as of 31st March 2019 (excluding large hydro plants) was 77.64 GW with the share of wind 35.626 GW, solar 28.181 GW, biomass 9.103 GW, and small hydro 4.593 GW. Expansion in renewable energy generation is encouraging but creates technical challenges and issues in the power industry [3–5]. It was acknowledged in the literature that grid-connected renewable energy sources (RES) would pose power quality challenges in the grid. They are weather dependent or intermittent and are non-dispatchable in nature. The large penetration of renewable energy into the grid will further exacerbate power quality issues [6–8]. On the grid side, contingencies such as voltage dip and swells, harmonic distortion, and frequency variations due to mismatch in output and consumption will cascade along the RES, causing serious problems in the grid [9–12]. The current power system cannot handle large amounts of intermittent RES without revamping the grid. It is unanimously accepted that a RES penetration of more than 20% affects grid stability. However, large-scale ESS will be helpful in improving the reliability and stability of the grid and increasing the percentage of penetration for safe operation [13, 14]. ESSs are beneficial for decoupling of generation/load and allow for decentralized storage, which improves the security of the grid [15]. Grid integrated RES can use a combination of advanced control systems and ESS to improve stability, reliability and power quality. In general, the ESS operation is divided into the following categories: •



Charging period: During off-peak intervals, when electricity is available at reduced prices, the charging can be used with network electrical energy. Discharging period: The stored energy of the ESS is used up during the peak period. It should be noted that network electrical energy is more expensive, making the use of distributed generation (DG) more

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cost-effective. This justifies the use of ESS in reducing or eliminating the uncertainties of renewable DGs. In a smart grid context, several ESS technologies, their classification and technical assessment based on characteristics and applications are all covered in this review. As a result, this analysis lays out key information for installing ESS and integrating them into future smart grid technology.

Status, Characteristics and Applications of Energy Storage Systems The global installed capacity of ESS is approximately 202 GW, of which 135 GW comes from pumped hydro and 65 GW is UPS systems. There are many ESSs installed throughout the world for many purposes such as power quality improvement. Some of them are listed below for frequency regulation service with their characteristics in Table 12.1. Characteristic features such as power rating and discharge time for some energy storage systems are presented in Figure 12.1. These energy storage systems provide support to the grid for long and short periods with fast and slow responses. Table 12.1. Frequency regulation energy storage projects [16] Project Name

Location

Storage Type

PJM Regulation Services Project

Pennsylvania, Lyon Station (United States) Ontario, Minto, Harriston/ Canada Hebei, Zhangbei/China

Battery

Rated Power(MW) 3

Flywheel

2

Battery, lithium iron phosphate

6

Alaska, Metlakatla/United States Atacama, Copiapo/Chile

Battery, lead-acid

1

Battery, lithiumion Battery, advanced lead-acid Battery, lithiumion Flywheel

12

Flywheel

20

Battery, lithiumion

20

NRStor Minto Flywheel Energy Storage Project National Wind and Solar Energy Storage and Transmission Demonstration Project (I) Metlakatla BESS Los Andes Kauai Island Utility Cooperative Johnson City Beacon New York Flywheel Energy Storage Plant Beacon Hazle Township Pennsylvania Plant Angamos

Hawaii, Koloa/United States New York, Johnson City/United States New York, Stephentown/ United States Pennsylvania, Hazle Township/United States Mejillones, Antofagasta/ Chile

1.5 8 20

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Figure 12.1. Discharge time of some energy storage system with their power rating [17].

Figure 12.2. Services provided by the grid-connected ESSs [18].

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Storages with more “parasitic losses”, such as flywheels and superconductors, are more advantageous for use with very short interval in power quality and regulation. Devices with low parasitic loss are more suitable for long term energy management. Figure 12.2 shows some of the services provided by a grid-connected ESS. Table 12.2 shows energy storage applications based on storage duration and services provided by ESS. Table 12.2. Application-based categorization of the energy storage system with storage interval [16, 19] Categories

Power quality and Regulations

Spanning Power

Energy Management

Utilization Fluctuation Mitigation Dynamic Power Response Low Voltage Ride Through Frequency Stability Uninterruptable Power Supply Voltage Curbing Reactive Power compensation Oscillation Suppression Stability Restoration Spinning Reserves Ramping Emergency Backup Load Following Power Smoothing Peak Shaving/Generation/Time Shifting Transmission Cut Energy Business Transmission and Distribution Rescheduling Line Restoration Weather Intermittency Suppression Unit Commitment demand Flattening Capacity Setting Grid tied-Backup Cyclical Storage Yearly Levelling

Storage Interval

≤1 min

1 min–1 h

1–10 h

5–12 h

Hours–days

≥4 months

Energy Storage Technologies All energy storage technologies are in the stage of maturity or development. The US Department of Energy classified these storage systems as either

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deployed or underdeveloped. They can be grouped into four categories based on the mechanism used as follows:

Mechanical Storage Mechanical energy storage systems have been used for centuries. According to many research articles, the three most popular and best options for mechanical energy storage are pumped hydroelectric, flywheel and compressed air. The major part of the energy comes from pumped hydro storage. However, each technique has its advantages and disadvantages, and they are discussed in detail below.

Pumped Hydroelectric Energy Storage Pumped hydroelectric energy storage (PHES) systems are the most widely used system today and commercially available for long-duration bulk energy storage. The potential energy of water stored at higher levels is used in PHES [20]. Water is released to produce electric power during times of high power demand and intermittent renewable generation reduction [21]. Typical PHES with solar PV and wind systems is shown in Figure 12.3. Pumped storage is a promising technology to boost renewable energy penetration into the grid [22– 24]. Pumped hydroelectric system (PHS) is classified into two categories, conventional and underground, depending on the reservoir used. These systems are classified on the basis of their rated power into large, small, micro and pico [25, 26]. About 25 countries depend on hydropower to meet their electricity demands. A comprehensive review of the PHS includes addressing technical challenges and trends in technology. Water evaporation losses are very common with pumped hydro, leading to reduced inefficiency. Pumped hydro response times can vary from minutes to hours. This property is used to smooth out changes in load and demand-supply. This is important for frequency regulation and power management. The PHS is highly flexible, reliable, and can do power regulation and frequency balancing. The rapid onset of PHS makes it convenient for frequency regulation [27, 28].

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Figure 12.3. Typical pumped hydroelectric energy storage with solar PV and wind energy systems [29].

Compressed Air Energy Storage (CAES) This technology has the second rank in the storage system after PHS [30]. These storages are useful for short and long durations. CAES consists of three steps: compression, storage, and expansion [31–33]. In CAES, a pressure tank is utilized for small applications (