Autorentext

R. Senthil Kumar, PhD is an assistant professor in the School of Electrical Engineering at the Vellore Institute of Technology. He has published 48 research articles in various reputed international journals. His research interests include electric vehicle charging stations, battery swapping, fault diagnosis in AC drives, multiport converters, computational intelligence, hybrid microgrids, and advanced step-up converters. V. Indragandhi, PhD is an associate professor in the School of Electrical Engineering at the Vellore Institute of Technology with more than 12 years of research and teaching experience. She has authored more than 100 research articles in leading peer-reviewed international journals and filed three patents. Her research focuses on power electronics and renewable energy systems. R. Selvamathi, PhD is an associate professor in the Department of Electrical and Electronics Engineering at AMC Engineering College with more than 18 years of teaching experience. She has published more than 15 research articles in international journals of repute. Her research interests include power electronics and renewable energy systems. P. Balakumar, PhD is an assistant professor in the School of Electrical Engineering at the Vellore Institute of Technology's Chennai Campus. He has authored articles in leading peer-reviewed international journals with high impact factors. His research interests include dynamic analysis of AC/DC power systems, designing power converters for EV applications, enhancing power quality, and demand side management for smart grid systems using AI approaches.

Klappentext

Harness the future of sustainable energy with this essential volume, which provides a comprehensive guide to integrating artificial intelligence for efficient energy storage and management systems. To achieve a clean and sustainable energy future, renewable energy sources such as solar, hydropower, and wind must develop dependable and effective energy storage technologies. The growing need for intelligent energy storage systems is greater than ever, despite substantial advancements in sophisticated energy storage technology, especially for large-scale energy storage. This book aims to provide the most recent developments in the integration of artificial intelligence for energy storage and management systems by introducing energy systems, power generation, and power needs to reduce expenses associated with generation, power loss, and environmental impacts. It explores state-of-the-art methods and solutions, such as intelligent wind and solar energy systems, founded on current technology, offering a strong foundation to satisfy the requirements of both developed and developing nations. An extensive overview of the many kinds of storage options is included. Additionally, it examines how utilizing diverse storage types can enhance the administration of a power supply system while also considering the more significant opportunities that result from integrating multiple storage devices into a system. Artificial Intelligence for Energy Management is a collection of expert contributions encompassing new techniques, methods, algorithms, practical solutions, and models for renewable energy storage based on artificial intelligence.

Inhalt

Preface xvii

1 Introduction to Next-Generation Energy Management and Need for AI Solutions 1
D. Gunapriya, P. Vinoth Kumar, G. Banu, S. Revathy, S. Giriprasad and N. Pushpalatha

1.1 Introduction 2
1.2 Application of AI in Energy Management Revolution 5
1.3 AI in Energy Sector 6
1.4 Role of AI in Energy Efficiency Improvement 7
1.5 Role of AI in Demand Forecasting and Load Balancing 7
1.6 Enhanced Sustainability and Reduced Carbon Footprint 8
1.7 AI-Based Grid Stability Enhancement 8
1.8 Predictive Maintenance and Asset Management 9
1.9 AI-Powered Energy Trading and Price Optimization 9
1.10 Ethical Considerations in AI-Powered Energy Management 10
1.11 Challenges in Incorporating AI in EMS 13
1.12 Case Studies on Implementing AI for Future Energy Management 18
1.13 Future Research Directions 21
1.14 Conclusion 23

2 Overview of Innovative Next Generation Energy Storage Technologies 27
D. Magdalin Mary, G. Sophia Jasmine, V. Vanitha, C. Kumar and T. Dharma Raj

2.1 Introduction 28
2.2 Energy Storage Techniques 29
2.3 Mechanical Energy Storage System 35
2.4 Electrochemical Storage System 35
2.5 Thermal Storage System 36
2.6 Electrical Energy Storage System 37
2.7 Hydrogen Storage System (Power-to-Gas) 37

3 Battery Energy Storage Systems with AI 39
Ashadevi S. and Latha R.

3.1 Introduction 39
3.2 System for Managing Batteries 41
3.3 Demand Response Strategies 52
3.4 Battery Energy Storage System 53
3.5 Technical Overview of Battery Energy Storage System 54
3.6 Conclusion and Future Scope 60

4 AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries 65
Sujith M., Pardeshi D.B., Krushna Lad, Pratiksha Ahire and Karun Pagetra

4.1 Introduction 66
4.2 Cathode Material 68
4.3 Anode Material 71
4.4 Electrolyte 73
4.5 State of Discharge (SOD) 75
4.6 State of Health (SOH) 76
4.7 BMS Algorithm with AI for SOH 77
4.8 Conclusion 79

5 Design and Development of an Adaptive Battery Management System for E-Vehicles 83
Saravanan Palaniswamy, Anbuselvi Mathivanan, A. Siyan Ananth and Sonu R.

5.1 Introduction 84
5.2 Related Works 85
5.3 Simulation Design 87
5.4 System Design 89
5.5 Implementation 95
5.6 Experimental Results 96
5.7 Conclusion 98

6 Remaining Useful Life (RUL) Prediction for EV Batteries 101
Anbuselvi Mathivanan, Saravanan Palaniswamy and M. Arul Mozhi

6.1 Introduction 102
6.2 Related Works 105
6.3 Proposed Model 106
6.4 Hardware Implementation 115
6.5 Outcomes and Analysis 120
6.6 Conclusion 124

7 Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System 129
K. Praharshitha, Varun S., Rithick Sarathi M.B. and V. Indragandhi

7.1 Introduction 129
7.2 Literature Survey 130
7.3 Technical Specification 132
7.4 Methodology 133
7.5 Project Demonstration 133
7.6 Results 135

8 An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques 141
V. Vanitha, G. Sophia Jasmine and D. Magdalin Mary

8.1 Introduction 142
8.2 Approaches in Charging Optimization 144
8.3 System Model 145
8.4 Proposed Methodology 146
8.5 Results and Discussion 153
8.6 Conclusion 162

9 Machine Learning and Deep Learning Methods for Energy Management Systems 165
V. Manimegalai, P. Ravi Raaghav, V. Mohanapriya, T.R. Vashishsdh and S. Palaniappan

9.1 Introduction 166
9.2 Building Energy Management System 167
9.3 Grid Optimization 173
9.4 Intelligent Energy Storage 184
9.5 Roles of ML and DL 199
9.6 The Roles of Traditional Methods in Energy Management System 204
9.7 Conclusion 209

10 Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance 213
Deepika Amudala and P. Buchibabu

10.1 Introduction 213
10.2 Modeling of Grid-Tied PV 215
10.3 MATLAB Simulation Design and Results 216
10.3.1 Simulations Results 217
10.4 Comparison of THD (Total Hormonic Distortion) Values Between PI and ANN 222
10.5 Conclusion 223

11 Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm 225
Karthik Nagarajan, Arul Rajagopalan and Priyadarshini Ramasubramanian

11.1 Introduction 226
11.2 Problem Formulation 227
11.3 Enhanced Crayfish Optimization Algorithm 234
11.4 Fuzzy Logic-Based Selection of Optimal Compromise Solution 239
11.5 Results and Discussion 240
11.6 Conclusion 244

**12 Relative Investigation of Swarm Optimized Load Frequency …