Artificial Intelligence for Next-Generation Energy Management -

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Résumé :

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

1.1.1 Challenges in Traditional Energy Management 3

1.1.2 Emergence of Next-Generation Energy Management 3

1.2 Application of AI in Energy Management Revolution 5

1.3 AI in Energy Sector 6

1.3.1 AI in Energy Optimization 6

1.3.2 Data Analytics and Predictive Maintenance 6

1.3.3 Intelligent Energy Storage and Demand Response 6

1.4 Role of AI in Energy Efficiency Improvement 7

1.4.1 Smart Building Management and Automation 7

1.4.2 AI-Driven Energy Analysis and Optimization 7

1.5 Role of AI in Demand Forecasting and Load Balancing 7

1.5.1 AI-Based Effective Forecasting of Energy Balancing 8

1.5.2 AI-Based Load Balancing 8

1.6 Enhanced Sustainability and Reduced Carbon Footprint 8

1.7 AI-Based Grid Stability Enhancement 8

1.7.1 AI-Driven Grid Monitoring and Control 8

1.7.2 AI-Based Intelligent Fault Detection 9

1.8 Predictive Maintenance and Asset Management 9

1.8.1 Role of AI in Predictive Maintenance 9

1.8.2 Optimizing Asset Management with AI 9

1.9 AI-Powered Energy Trading and Price Optimization 9

1.9.1 Revolutionizing Energy Trading with AI 9

1.9.2 Price Optimization Using AI for Energy Management 10

1.10 Ethical Considerations in AI-Powered Energy Management 10

1.10.1 Enhancing Energy Efficiency 10

1.10.2 Mitigating Environmental Impact 11

1.10.3 Empowering Consumers 11

1.10.4 Data Privacy and Security Concerns 11

1.10.5 Economic Implications 12

1.10.6 Ethical Considerations 12

1.10.7 Workforce Disruption and Reskilling 13

1.11 Challenges in Incorporating AI in EMS 13

1.12 Case Studies on Implementing AI for Future Energy Management 18

1.12.1 Case Study 1: Smart Grid Implementation in User's Utility Company 18

1.12.2 Case Study 2: AI-Driven Energy Management in User's Manufacturing Facility 19

1.12.3 Case Study 3: AI-Powered Demand Response Program in a Smart City 20

1.13 Future Research Directions 21

1.13.1 Track for Future Trends and Innovation 21

1.13.2 The Importance of Cooperation and Financial Contribution to AI Research and Development 21

1.13.3 Contribution of AI in Achieving Energy Transient Objective 22

1.13.4 Developments in Energy Management and AI 22

1.13.5 Rules and Guidelines for AI in Energy Management 22

1.13.6 Decision-Making Transparency and Accountability 22

1.13.7 AI's Potential to Revolutionize the Power Sector 23

1.14 Conclusion 23

References 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

References 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

3.2.1 State Estimation 44

3.2.1.1 State of Charge 44

3.3 Demand Response Strategies 52

3.4 Battery Energy Storage System 53

3.5 Technical Overview of Battery Energy Storage System 54

3.5.1 WSN in Battery Energy Storage 54

3.5.2 ...

Biographie:

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....

Sommaire:

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...