Sandeep Dhundhara, PhD, is an assistant professor in the Department of Basic Engineering at CCS Haryana Agricultural University, Hisar, India. He has a total of eight years of teaching and research experience in electrical engineering. He has published several papers in various international journals and conferences, and he has published one book with Scrivener Publishing, Energy Storage for Modern Power System Operations. Yajvender Pal Verma, PhD, is a professor in the Department of Electrical and Electronics Engineering at Panjab University, Chandigarh, India. He has two books and more than 80 papers in various national and international journals and conferences to his credit. He has successfully executed eight national and international research and consultancy projects and has been granted one Indian patent. Ashwani Kumar, PhD, is a professor and Head of the Department of Electrical Engineering at the National Institute of Technology, Kurukshetra, India. He has more than 100 papers in scholarly and technical journals to his credit. He has organized several international conferences, including IEEE, and executed various research and consultancy projects.

• Preface xiii1 Overview of Current Development and Research Trends in Energy Storage Technologies 1O. Apata1.1 Introduction 11.2 The Technology of Energy Storage 41.3 Energy Storage and Smart Grids 141.4 Energy Storage and Micro-Grids 151.5 Energy Storage Policy Recommendations 171.6 Energy Storage: Challenges and Opportunities 181.7 Practical Implementations of Energy Storage Technologies 191.8 Conclusions 20References 202 A Comprehensive Review of the Li-Ion Batteries Fast-Charging Protocols 23Talal Mouais and Saeed Mian Qaisar2.1 Introduction 242.2 The Literature Review 272.2.1 Overview of Lithium-Ion Battery Working Principle 282.2.2 Principles of Battery Fast-Charging 312.2.3 Multi-Scale Design for Fast Charging 332.2.4 Electrode Materials 332.2.5 Fast-Charging Strategies 342.2.6 Types of Charging Protocols 342.2.7 Li-Ion Battery Degradation 402.2.8 Factors that Cause Battery Degradation 412.2.9 Degradation Mechanism of the Li-Ion Battery 442.2.10 Electrode Degradation in Lithium-Ion Batteries 482.2.11 The Battery Management System 502.2.12 Battery Technology Gap Assessment for Fast-Charging 532.2.13 Developmental Needs 552.3 Materials and Methods 562.4 Discussion 582.5 Conclusion 63Acknowledgements 65References 653 Development of Sustainable High‑Performance Supercapacitor Electrodes from Biochar-Based Material 71Kriti Shrivastava and Ankur Jain3.1 Introduction 723.2 Role of Energy Storage Systems in Grid Modernization 733.3 Overview of Current Developments of Supercapacitor-Based Electrical Energy Storage Technologies 783.4 Potential of Biochar as High-Performance Sustainable Material 803.5 Overview of Recent Developments in Biochar-Based EDLC Supercapacitor 833.5.1 Wood & Plant Residues as Biochar Precursor for Supercapacitor Applications 843.5.2 Biochar-Based Supercapacitors from Waste Biomass 893.5.3 Carbon-Based Supercapacitors from Other Methods 913.6 Current Challenges and Future Potential of Biochar-Based Supercapacitor 933.7 Conclusion 99References 1014 Energy Storage Units for Frequency Management in Nuclear Generators-Based Power System 105Boopathi D., Jagatheesan K., Sourav Samanta, Anand B. and Satheeshkumar R.4.1 Introduction 1054.1.1 Structure of the Chapter 1104.1.2 Objective of the Chapter 1104.2 Investigated System Modeling 1114.2.1 Battery Energy Storage System (BESS) Model 1124.2.2 Fuel Cell (FC) Model 1134.2.3 Redox Flow Battery (RFB) Model 1134.2.4 Proton Exchange Membrane (PEM) Based FC Model 1144.2.5 Ultra-Capacitor (UC) Model 1154.2.6 Supercapacitor Energy Storage (SCES) Model 1164.3 Controller and Cost Function 1164.4 Optimization Methodology 1184.5 Impact Analysis of Energy Storage Units 1194.5.1 Impact of BESS 1194.5.2 Impact of FC 1214.5.3 Impact of RFB 1224.5.4 Impact Analysis of the PEM-FC 1234.5.5 Impact Analysis of UC 1254.5.6 Impact Analysis of SCES 1274.6 Result and Discussion 1284.7 Conclusion 130Appendix 132References 1325 Detailed Comparative Analysis and Performance of Fuel Cells 135Tejinder Singh Saggu and Arvind Dhingra5.1 Introduction 1355.2 Classification of Fuel Cells 1365.2.1 Based on Fuel-Oxidizer Electrolyte 1385.2.1.1 Direct Fuel Cell 1385.2.1.2 Regenerative FC 1395.2.1.3 Indirect Fuel Cells 1435.2.2 Based on the State of Aggregation of Reactants 1445.2.2.1 Solid Fuel Cells 1445.2.2.2 Gaseous Fuel Cells 1455.2.2.3 Liquid Fuel Cells 1475.2.3 Based on Electrolyte Temperature 1485.2.3.1 Proton Exchange Membrane 1485.2.3.2 Direct Methanol 1505.2.3.3 Alkaline 1505.2.3.4 Phosphoric Acid 1515.2.3.5 Molten Carbonate 1525.2.3.6 Solid Oxide 1535.3 Cost of Different Fuel Cell Technologies 1545.4 Conclusion 155References 1556 Machine Learning–Based SoC Estimation: A Recent Advancement in Battery Energy Storage System 159Prerana Mohapatra, Venkata Ramana Naik N. and Anup Kumar Panda6.1 Introduction 1606.2 SoC Estimation Techniques 1636.2.1 Coulomb Counting Approach 1646.2.2 Look-Up Table Method 1646.2.3 Model-Based Methods 1646.2.3.1 Electrochemical Model 1646.2.3.2 Equivalent Circuit Model 1656.2.4 Data-Driven Methods 1656.2.5 Machine Learning–Based Methods 1666.2.5.1 Support Vector Regression 1666.2.5.2 Ridged Extreme Learning Machine (RELM) 1686.3 BESS Description 1716.4 Results and Discussion 1716.5 Conclusion 175References 1777 Dual-Energy Storage System for Optimal Operation of Grid‑Connected Microgrid System 181Deepak Kumar and Sandeep Dhundhara7.1 Introduction 1827.2 System Mathematical Modelling 1887.2.1 Modelling of Wind Turbine Power Generator 1897.2.2 Modelling of Solar Power Plant 1897.2.3 Modelling of Conventional Diesel Power Generator 1897.2.4 Modelling of Combined Heat and Power (CHP) and Boiler Plant 1907.2.5 Modelling of Dual Energy Storage System 1907.2.5.1 Battery Bank Storage System 1907.2.5.2 Pump Hydro Storage System 1917.2.6 Modelling of Power Transfer Capability 1917.3 Objective Function and Problem Formulations 1927.3.1 Operational and Technical Constraints 1927.4 Simulation Results and Discussion 1957.5 Conclusion 208References 2098 Applications of Energy Storage in Modern Power System through Demand-Side Management 213Preeti Gupta and Yajvender Pal Verma8.1 Introduction to Demand-Side Management 2148.1.1 Demand-Side Management Techniques 2148.1.1.1 Energy Efficiency 2148.1.1.2 Demand Response 2158.1.2 Demand-Side Management Approaches 2178.2 Operational Aspects of DR 2188.3 DSM Challenges 2218.4 Demand Response Resources 2238.5 Role of Battery Energy Storage in DSM 2248.5.1 Case Study I: Peak Load and PAR Reduction 2258.5.1.1 Problem Formulation 2258.5.1.2 Energy Storage Dispatch Modelling 2268.5.2 Case Study II: Minimizing Load Profile Variations 2298.5.2.1 Problem Formulation 2298.5.2.2 SPV System Modelling 2308.5.3 Results and Discussions 2318.5.3.1 Case Study I: Peak Load and PAR Reduction Using Batteries with DR 2318.5.3.2 Case Study II: Minimizing Load Profile Variations Using Batteries with DR 2328.6 Conclusion 234References 2349 Impact of Battery Energy Storage Systems and Demand Response Program on Locational Marginal Prices in Distribution System 239Saikrishna Varikunta and Ashwani Kumar9.1 Introduction 2409.1.1 Battery Energy Storage System (BESS) 2409.1.2 Demand Response Program 2429.2 Problem Formulation and Solution Using GAMS 2449.2.1 Objective Functions for Case Studies: Case 1 to Case 5 2459.2.1.1 Case 1: Is Minimization of the Active Power Production Cost 2459.2.1.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2469.2.1.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along with Capacitor Placement 2469.2.1.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost 2479.2.1.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost and Taking the Impact of Demand Response Program 2489.2.2 Real and Reactive Power Equality Constraints 2499.2.2.1 Equality Constraints 2499.2.2.2 Inequality Constraints: (at any bus i): Voltage, Power Generation, Line Flow, SOC, Battery Energy Storage Power 2509.2.3 Modified Lagrangian Function 2519.2.4 Generator Economics Calculations 2529.3 Case Study: Numerical Computation 2549.4 Results and Discussions 2579.4.1 Case 1: Minimization of the Active Power Production Cost 2579.4.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2609.4.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along 2629.4.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost 2669.4.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost 2699.5 Conclusions 279References 28010 Cost-Benefit Analysis with Optimal DG Allocation and Energy Storage System Incorporating Demand Response Technique 283Rohit Kandpal, Ashwani Kumar, Sandeep Dhundhara and Yajvender Pal Verma10.1 Introduction 28410.2 Distribution Generation and Energy Storage System 28510.2.1 Renewable Energy in India 28610.2.2 Different Types of Energy Storage and their Opportunities 28710.2.3 Distributed Generation 29010.2.3.1 Solar Photovoltaic Panel-Based DG (PVDG) 29010.2.3.2 Wind Turbine–Based DG (WTDG) 29110.2.3.3 Load Model and Load Profile 29310.2.4 Demand Response Program 29410.2.5 Electric Vehicles 29710.2.6 Modeling of Energy Storage System 29910.2.7 Problem Formulation 30010.2.8 Distribution Location Marginal Pricing 30110.3 Grey Wolf Optimization 30210.4 Numerical Simulation and Results 30410.5 Conclusions 312References 31311 Energy Storage Systems and Charging Stations Mechanism for Electric Vehicles 317Saurabh Ratra, Kanwardeep Singh and Derminder Singh11.1 Introduction to Electric Vehicles 31811.1.1 Role of Electric Vehicles in Modern Power System 31811.1.2 Various Storage Technologies 31911.1.3 Electric Vehicle Charging Structure 32211.2 Introduction to Electric Vehicle Charging Station 32311.2.1 Types of Charging Station 32311.2.2 Charging Levels 32411.2.3 EV Charging 32411.2.4 Charging Period 32711.3 Modern System Efficient Approches 32811.3.1 Smart Grid Technology 32811.3.2 Renewable Energy Technology 32911.3.3 V2G Technology 32911.3.4 Smart Transport System 32911.4 Battery Charging Techniques 33011.4.1 Electric Vehicle Charging Station in Modern Power System 33111.5 Indian Scenario 33211.6 Energy Storage System Evaluation for EV Applications 33311.7 ESS Concerns and Experiments in EV Solicitations 33411.7.1 Raw Materials 33511.7.2 Interfacing by Power Electronics 33511.7.3 Energy Management 33511.7.4 Environmental Impact 33611.7.5 Safety 33611.8 Conclusion 336References 337Index 341

Sandeep Dhundhara, PhD, is an assistant professor in the Department of Basic Engineering at CCS Haryana Agricultural University, Hisar, India. He has a total of eight years of teaching and research experience in electrical engineering. He has published several papers in various international journals and conferences, and he has published one book with Scrivener Publishing, Energy Storage for Modern Power System Operations. Yajvender Pal Verma, PhD, is a professor in the Department of Electrical and Electronics Engineering at Panjab University, Chandigarh, India. He has two books and more than 80 papers in various national and international journals and conferences to his credit. He has successfully executed eight national and international research and consultancy projects and has been granted one Indian patent. Ashwani Kumar, PhD, is a professor and Head of the Department of Electrical Engineering at the National Institute of Technology, Kurukshetra, India. He has more than 100 papers in scholarly and technical journals to his credit. He has organized several international conferences, including IEEE, and executed various research and consultancy projects.

• Preface xiii1 Overview of Current Development and Research Trends in Energy Storage Technologies 1O. Apata1.1 Introduction 11.2 The Technology of Energy Storage 41.3 Energy Storage and Smart Grids 141.4 Energy Storage and Micro-Grids 151.5 Energy Storage Policy Recommendations 171.6 Energy Storage: Challenges and Opportunities 181.7 Practical Implementations of Energy Storage Technologies 191.8 Conclusions 20References 202 A Comprehensive Review of the Li-Ion Batteries Fast-Charging Protocols 23Talal Mouais and Saeed Mian Qaisar2.1 Introduction 242.2 The Literature Review 272.2.1 Overview of Lithium-Ion Battery Working Principle 282.2.2 Principles of Battery Fast-Charging 312.2.3 Multi-Scale Design for Fast Charging 332.2.4 Electrode Materials 332.2.5 Fast-Charging Strategies 342.2.6 Types of Charging Protocols 342.2.7 Li-Ion Battery Degradation 402.2.8 Factors that Cause Battery Degradation 412.2.9 Degradation Mechanism of the Li-Ion Battery 442.2.10 Electrode Degradation in Lithium-Ion Batteries 482.2.11 The Battery Management System 502.2.12 Battery Technology Gap Assessment for Fast-Charging 532.2.13 Developmental Needs 552.3 Materials and Methods 562.4 Discussion 582.5 Conclusion 63Acknowledgements 65References 653 Development of Sustainable High‑Performance Supercapacitor Electrodes from Biochar-Based Material 71Kriti Shrivastava and Ankur Jain3.1 Introduction 723.2 Role of Energy Storage Systems in Grid Modernization 733.3 Overview of Current Developments of Supercapacitor-Based Electrical Energy Storage Technologies 783.4 Potential of Biochar as High-Performance Sustainable Material 803.5 Overview of Recent Developments in Biochar-Based EDLC Supercapacitor 833.5.1 Wood & Plant Residues as Biochar Precursor for Supercapacitor Applications 843.5.2 Biochar-Based Supercapacitors from Waste Biomass 893.5.3 Carbon-Based Supercapacitors from Other Methods 913.6 Current Challenges and Future Potential of Biochar-Based Supercapacitor 933.7 Conclusion 99References 1014 Energy Storage Units for Frequency Management in Nuclear Generators-Based Power System 105Boopathi D., Jagatheesan K., Sourav Samanta, Anand B. and Satheeshkumar R.4.1 Introduction 1054.1.1 Structure of the Chapter 1104.1.2 Objective of the Chapter 1104.2 Investigated System Modeling 1114.2.1 Battery Energy Storage System (BESS) Model 1124.2.2 Fuel Cell (FC) Model 1134.2.3 Redox Flow Battery (RFB) Model 1134.2.4 Proton Exchange Membrane (PEM) Based FC Model 1144.2.5 Ultra-Capacitor (UC) Model 1154.2.6 Supercapacitor Energy Storage (SCES) Model 1164.3 Controller and Cost Function 1164.4 Optimization Methodology 1184.5 Impact Analysis of Energy Storage Units 1194.5.1 Impact of BESS 1194.5.2 Impact of FC 1214.5.3 Impact of RFB 1224.5.4 Impact Analysis of the PEM-FC 1234.5.5 Impact Analysis of UC 1254.5.6 Impact Analysis of SCES 1274.6 Result and Discussion 1284.7 Conclusion 130Appendix 132References 1325 Detailed Comparative Analysis and Performance of Fuel Cells 135Tejinder Singh Saggu and Arvind Dhingra5.1 Introduction 1355.2 Classification of Fuel Cells 1365.2.1 Based on Fuel-Oxidizer Electrolyte 1385.2.1.1 Direct Fuel Cell 1385.2.1.2 Regenerative FC 1395.2.1.3 Indirect Fuel Cells 1435.2.2 Based on the State of Aggregation of Reactants 1445.2.2.1 Solid Fuel Cells 1445.2.2.2 Gaseous Fuel Cells 1455.2.2.3 Liquid Fuel Cells 1475.2.3 Based on Electrolyte Temperature 1485.2.3.1 Proton Exchange Membrane 1485.2.3.2 Direct Methanol 1505.2.3.3 Alkaline 1505.2.3.4 Phosphoric Acid 1515.2.3.5 Molten Carbonate 1525.2.3.6 Solid Oxide 1535.3 Cost of Different Fuel Cell Technologies 1545.4 Conclusion 155References 1556 Machine Learning–Based SoC Estimation: A Recent Advancement in Battery Energy Storage System 159Prerana Mohapatra, Venkata Ramana Naik N. and Anup Kumar Panda6.1 Introduction 1606.2 SoC Estimation Techniques 1636.2.1 Coulomb Counting Approach 1646.2.2 Look-Up Table Method 1646.2.3 Model-Based Methods 1646.2.3.1 Electrochemical Model 1646.2.3.2 Equivalent Circuit Model 1656.2.4 Data-Driven Methods 1656.2.5 Machine Learning–Based Methods 1666.2.5.1 Support Vector Regression 1666.2.5.2 Ridged Extreme Learning Machine (RELM) 1686.3 BESS Description 1716.4 Results and Discussion 1716.5 Conclusion 175References 1777 Dual-Energy Storage System for Optimal Operation of Grid‑Connected Microgrid System 181Deepak Kumar and Sandeep Dhundhara7.1 Introduction 1827.2 System Mathematical Modelling 1887.2.1 Modelling of Wind Turbine Power Generator 1897.2.2 Modelling of Solar Power Plant 1897.2.3 Modelling of Conventional Diesel Power Generator 1897.2.4 Modelling of Combined Heat and Power (CHP) and Boiler Plant 1907.2.5 Modelling of Dual Energy Storage System 1907.2.5.1 Battery Bank Storage System 1907.2.5.2 Pump Hydro Storage System 1917.2.6 Modelling of Power Transfer Capability 1917.3 Objective Function and Problem Formulations 1927.3.1 Operational and Technical Constraints 1927.4 Simulation Results and Discussion 1957.5 Conclusion 208References 2098 Applications of Energy Storage in Modern Power System through Demand-Side Management 213Preeti Gupta and Yajvender Pal Verma8.1 Introduction to Demand-Side Management 2148.1.1 Demand-Side Management Techniques 2148.1.1.1 Energy Efficiency 2148.1.1.2 Demand Response 2158.1.2 Demand-Side Management Approaches 2178.2 Operational Aspects of DR 2188.3 DSM Challenges 2218.4 Demand Response Resources 2238.5 Role of Battery Energy Storage in DSM 2248.5.1 Case Study I: Peak Load and PAR Reduction 2258.5.1.1 Problem Formulation 2258.5.1.2 Energy Storage Dispatch Modelling 2268.5.2 Case Study II: Minimizing Load Profile Variations 2298.5.2.1 Problem Formulation 2298.5.2.2 SPV System Modelling 2308.5.3 Results and Discussions 2318.5.3.1 Case Study I: Peak Load and PAR Reduction Using Batteries with DR 2318.5.3.2 Case Study II: Minimizing Load Profile Variations Using Batteries with DR 2328.6 Conclusion 234References 2349 Impact of Battery Energy Storage Systems and Demand Response Program on Locational Marginal Prices in Distribution System 239Saikrishna Varikunta and Ashwani Kumar9.1 Introduction 2409.1.1 Battery Energy Storage System (BESS) 2409.1.2 Demand Response Program 2429.2 Problem Formulation and Solution Using GAMS 2449.2.1 Objective Functions for Case Studies: Case 1 to Case 5 2459.2.1.1 Case 1: Is Minimization of the Active Power Production Cost 2459.2.1.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2469.2.1.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along with Capacitor Placement 2469.2.1.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost 2479.2.1.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost and Taking the Impact of Demand Response Program 2489.2.2 Real and Reactive Power Equality Constraints 2499.2.2.1 Equality Constraints 2499.2.2.2 Inequality Constraints: (at any bus i): Voltage, Power Generation, Line Flow, SOC, Battery Energy Storage Power 2509.2.3 Modified Lagrangian Function 2519.2.4 Generator Economics Calculations 2529.3 Case Study: Numerical Computation 2549.4 Results and Discussions 2579.4.1 Case 1: Minimization of the Active Power Production Cost 2579.4.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2609.4.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along 2629.4.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost 2669.4.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost 2699.5 Conclusions 279References 28010 Cost-Benefit Analysis with Optimal DG Allocation and Energy Storage System Incorporating Demand Response Technique 283Rohit Kandpal, Ashwani Kumar, Sandeep Dhundhara and Yajvender Pal Verma10.1 Introduction 28410.2 Distribution Generation and Energy Storage System 28510.2.1 Renewable Energy in India 28610.2.2 Different Types of Energy Storage and their Opportunities 28710.2.3 Distributed Generation 29010.2.3.1 Solar Photovoltaic Panel-Based DG (PVDG) 29010.2.3.2 Wind Turbine–Based DG (WTDG) 29110.2.3.3 Load Model and Load Profile 29310.2.4 Demand Response Program 29410.2.5 Electric Vehicles 29710.2.6 Modeling of Energy Storage System 29910.2.7 Problem Formulation 30010.2.8 Distribution Location Marginal Pricing 30110.3 Grey Wolf Optimization 30210.4 Numerical Simulation and Results 30410.5 Conclusions 312References 31311 Energy Storage Systems and Charging Stations Mechanism for Electric Vehicles 317Saurabh Ratra, Kanwardeep Singh and Derminder Singh11.1 Introduction to Electric Vehicles 31811.1.1 Role of Electric Vehicles in Modern Power System 31811.1.2 Various Storage Technologies 31911.1.3 Electric Vehicle Charging Structure 32211.2 Introduction to Electric Vehicle Charging Station 32311.2.1 Types of Charging Station 32311.2.2 Charging Levels 32411.2.3 EV Charging 32411.2.4 Charging Period 32711.3 Modern System Efficient Approches 32811.3.1 Smart Grid Technology 32811.3.2 Renewable Energy Technology 32911.3.3 V2G Technology 32911.3.4 Smart Transport System 32911.4 Battery Charging Techniques 33011.4.1 Electric Vehicle Charging Station in Modern Power System 33111.5 Indian Scenario 33211.6 Energy Storage System Evaluation for EV Applications 33311.7 ESS Concerns and Experiments in EV Solicitations 33411.7.1 Raw Materials 33511.7.2 Interfacing by Power Electronics 33511.7.3 Energy Management 33511.7.4 Environmental Impact 33611.7.5 Safety 33611.8 Conclusion 336References 337Index 341

Sandeep Dhundhara, PhD, is an assistant professor in the Department of Basic Engineering at CCS Haryana Agricultural University, Hisar, India. He has a total of eight years of teaching and research experience in electrical engineering. He has published several papers in various international journals and conferences, and he has published one book with Scrivener Publishing, Energy Storage for Modern Power System Operations. Yajvender Pal Verma, PhD, is a professor in the Department of Electrical and Electronics Engineering at Panjab University, Chandigarh, India. He has two books and more than 80 papers in various national and international journals and conferences to his credit. He has successfully executed eight national and international research and consultancy projects and has been granted one Indian patent. Ashwani Kumar, PhD, is a professor and Head of the Department of Electrical Engineering at the National Institute of Technology, Kurukshetra, India. He has more than 100 papers in scholarly and technical journals to his credit. He has organized several international conferences, including IEEE, and executed various research and consultancy projects.

• Preface xiii1 Overview of Current Development and Research Trends in Energy Storage Technologies 1O. Apata1.1 Introduction 11.2 The Technology of Energy Storage 41.3 Energy Storage and Smart Grids 141.4 Energy Storage and Micro-Grids 151.5 Energy Storage Policy Recommendations 171.6 Energy Storage: Challenges and Opportunities 181.7 Practical Implementations of Energy Storage Technologies 191.8 Conclusions 20References 202 A Comprehensive Review of the Li-Ion Batteries Fast-Charging Protocols 23Talal Mouais and Saeed Mian Qaisar2.1 Introduction 242.2 The Literature Review 272.2.1 Overview of Lithium-Ion Battery Working Principle 282.2.2 Principles of Battery Fast-Charging 312.2.3 Multi-Scale Design for Fast Charging 332.2.4 Electrode Materials 332.2.5 Fast-Charging Strategies 342.2.6 Types of Charging Protocols 342.2.7 Li-Ion Battery Degradation 402.2.8 Factors that Cause Battery Degradation 412.2.9 Degradation Mechanism of the Li-Ion Battery 442.2.10 Electrode Degradation in Lithium-Ion Batteries 482.2.11 The Battery Management System 502.2.12 Battery Technology Gap Assessment for Fast-Charging 532.2.13 Developmental Needs 552.3 Materials and Methods 562.4 Discussion 582.5 Conclusion 63Acknowledgements 65References 653 Development of Sustainable High‑Performance Supercapacitor Electrodes from Biochar-Based Material 71Kriti Shrivastava and Ankur Jain3.1 Introduction 723.2 Role of Energy Storage Systems in Grid Modernization 733.3 Overview of Current Developments of Supercapacitor-Based Electrical Energy Storage Technologies 783.4 Potential of Biochar as High-Performance Sustainable Material 803.5 Overview of Recent Developments in Biochar-Based EDLC Supercapacitor 833.5.1 Wood & Plant Residues as Biochar Precursor for Supercapacitor Applications 843.5.2 Biochar-Based Supercapacitors from Waste Biomass 893.5.3 Carbon-Based Supercapacitors from Other Methods 913.6 Current Challenges and Future Potential of Biochar-Based Supercapacitor 933.7 Conclusion 99References 1014 Energy Storage Units for Frequency Management in Nuclear Generators-Based Power System 105Boopathi D., Jagatheesan K., Sourav Samanta, Anand B. and Satheeshkumar R.4.1 Introduction 1054.1.1 Structure of the Chapter 1104.1.2 Objective of the Chapter 1104.2 Investigated System Modeling 1114.2.1 Battery Energy Storage System (BESS) Model 1124.2.2 Fuel Cell (FC) Model 1134.2.3 Redox Flow Battery (RFB) Model 1134.2.4 Proton Exchange Membrane (PEM) Based FC Model 1144.2.5 Ultra-Capacitor (UC) Model 1154.2.6 Supercapacitor Energy Storage (SCES) Model 1164.3 Controller and Cost Function 1164.4 Optimization Methodology 1184.5 Impact Analysis of Energy Storage Units 1194.5.1 Impact of BESS 1194.5.2 Impact of FC 1214.5.3 Impact of RFB 1224.5.4 Impact Analysis of the PEM-FC 1234.5.5 Impact Analysis of UC 1254.5.6 Impact Analysis of SCES 1274.6 Result and Discussion 1284.7 Conclusion 130Appendix 132References 1325 Detailed Comparative Analysis and Performance of Fuel Cells 135Tejinder Singh Saggu and Arvind Dhingra5.1 Introduction 1355.2 Classification of Fuel Cells 1365.2.1 Based on Fuel-Oxidizer Electrolyte 1385.2.1.1 Direct Fuel Cell 1385.2.1.2 Regenerative FC 1395.2.1.3 Indirect Fuel Cells 1435.2.2 Based on the State of Aggregation of Reactants 1445.2.2.1 Solid Fuel Cells 1445.2.2.2 Gaseous Fuel Cells 1455.2.2.3 Liquid Fuel Cells 1475.2.3 Based on Electrolyte Temperature 1485.2.3.1 Proton Exchange Membrane 1485.2.3.2 Direct Methanol 1505.2.3.3 Alkaline 1505.2.3.4 Phosphoric Acid 1515.2.3.5 Molten Carbonate 1525.2.3.6 Solid Oxide 1535.3 Cost of Different Fuel Cell Technologies 1545.4 Conclusion 155References 1556 Machine Learning–Based SoC Estimation: A Recent Advancement in Battery Energy Storage System 159Prerana Mohapatra, Venkata Ramana Naik N. and Anup Kumar Panda6.1 Introduction 1606.2 SoC Estimation Techniques 1636.2.1 Coulomb Counting Approach 1646.2.2 Look-Up Table Method 1646.2.3 Model-Based Methods 1646.2.3.1 Electrochemical Model 1646.2.3.2 Equivalent Circuit Model 1656.2.4 Data-Driven Methods 1656.2.5 Machine Learning–Based Methods 1666.2.5.1 Support Vector Regression 1666.2.5.2 Ridged Extreme Learning Machine (RELM) 1686.3 BESS Description 1716.4 Results and Discussion 1716.5 Conclusion 175References 1777 Dual-Energy Storage System for Optimal Operation of Grid‑Connected Microgrid System 181Deepak Kumar and Sandeep Dhundhara7.1 Introduction 1827.2 System Mathematical Modelling 1887.2.1 Modelling of Wind Turbine Power Generator 1897.2.2 Modelling of Solar Power Plant 1897.2.3 Modelling of Conventional Diesel Power Generator 1897.2.4 Modelling of Combined Heat and Power (CHP) and Boiler Plant 1907.2.5 Modelling of Dual Energy Storage System 1907.2.5.1 Battery Bank Storage System 1907.2.5.2 Pump Hydro Storage System 1917.2.6 Modelling of Power Transfer Capability 1917.3 Objective Function and Problem Formulations 1927.3.1 Operational and Technical Constraints 1927.4 Simulation Results and Discussion 1957.5 Conclusion 208References 2098 Applications of Energy Storage in Modern Power System through Demand-Side Management 213Preeti Gupta and Yajvender Pal Verma8.1 Introduction to Demand-Side Management 2148.1.1 Demand-Side Management Techniques 2148.1.1.1 Energy Efficiency 2148.1.1.2 Demand Response 2158.1.2 Demand-Side Management Approaches 2178.2 Operational Aspects of DR 2188.3 DSM Challenges 2218.4 Demand Response Resources 2238.5 Role of Battery Energy Storage in DSM 2248.5.1 Case Study I: Peak Load and PAR Reduction 2258.5.1.1 Problem Formulation 2258.5.1.2 Energy Storage Dispatch Modelling 2268.5.2 Case Study II: Minimizing Load Profile Variations 2298.5.2.1 Problem Formulation 2298.5.2.2 SPV System Modelling 2308.5.3 Results and Discussions 2318.5.3.1 Case Study I: Peak Load and PAR Reduction Using Batteries with DR 2318.5.3.2 Case Study II: Minimizing Load Profile Variations Using Batteries with DR 2328.6 Conclusion 234References 2349 Impact of Battery Energy Storage Systems and Demand Response Program on Locational Marginal Prices in Distribution System 239Saikrishna Varikunta and Ashwani Kumar9.1 Introduction 2409.1.1 Battery Energy Storage System (BESS) 2409.1.2 Demand Response Program 2429.2 Problem Formulation and Solution Using GAMS 2449.2.1 Objective Functions for Case Studies: Case 1 to Case 5 2459.2.1.1 Case 1: Is Minimization of the Active Power Production Cost 2459.2.1.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2469.2.1.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along with Capacitor Placement 2469.2.1.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost 2479.2.1.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost Including Capacitor and BESS Cost and Taking the Impact of Demand Response Program 2489.2.2 Real and Reactive Power Equality Constraints 2499.2.2.1 Equality Constraints 2499.2.2.2 Inequality Constraints: (at any bus i): Voltage, Power Generation, Line Flow, SOC, Battery Energy Storage Power 2509.2.3 Modified Lagrangian Function 2519.2.4 Generator Economics Calculations 2529.3 Case Study: Numerical Computation 2549.4 Results and Discussions 2579.4.1 Case 1: Minimization of the Active Power Production Cost 2579.4.2 Case 2: Minimization of the Active Power Production and Reactive Power Production Cost 2609.4.3 Case 3: Minimization of the Active Power Production and Reactive Power Production Cost Along 2629.4.4 Case 4: Minimization of the Active Power Production and Reactive Power Production Cost 2669.4.5 Case 5: Minimization of the Active Power Production and Reactive Power Production Cost 2699.5 Conclusions 279References 28010 Cost-Benefit Analysis with Optimal DG Allocation and Energy Storage System Incorporating Demand Response Technique 283Rohit Kandpal, Ashwani Kumar, Sandeep Dhundhara and Yajvender Pal Verma10.1 Introduction 28410.2 Distribution Generation and Energy Storage System 28510.2.1 Renewable Energy in India 28610.2.2 Different Types of Energy Storage and their Opportunities 28710.2.3 Distributed Generation 29010.2.3.1 Solar Photovoltaic Panel-Based DG (PVDG) 29010.2.3.2 Wind Turbine–Based DG (WTDG) 29110.2.3.3 Load Model and Load Profile 29310.2.4 Demand Response Program 29410.2.5 Electric Vehicles 29710.2.6 Modeling of Energy Storage System 29910.2.7 Problem Formulation 30010.2.8 Distribution Location Marginal Pricing 30110.3 Grey Wolf Optimization 30210.4 Numerical Simulation and Results 30410.5 Conclusions 312References 31311 Energy Storage Systems and Charging Stations Mechanism for Electric Vehicles 317Saurabh Ratra, Kanwardeep Singh and Derminder Singh11.1 Introduction to Electric Vehicles 31811.1.1 Role of Electric Vehicles in Modern Power System 31811.1.2 Various Storage Technologies 31911.1.3 Electric Vehicle Charging Structure 32211.2 Introduction to Electric Vehicle Charging Station 32311.2.1 Types of Charging Station 32311.2.2 Charging Levels 32411.2.3 EV Charging 32411.2.4 Charging Period 32711.3 Modern System Efficient Approches 32811.3.1 Smart Grid Technology 32811.3.2 Renewable Energy Technology 32911.3.3 V2G Technology 32911.3.4 Smart Transport System 32911.4 Battery Charging Techniques 33011.4.1 Electric Vehicle Charging Station in Modern Power System 33111.5 Indian Scenario 33211.6 Energy Storage System Evaluation for EV Applications 33311.7 ESS Concerns and Experiments in EV Solicitations 33411.7.1 Raw Materials 33511.7.2 Interfacing by Power Electronics 33511.7.3 Energy Management 33511.7.4 Environmental Impact 33611.7.5 Safety 33611.8 Conclusion 336References 337Index 341