Artificial Intelligence for Energy Management

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.

Table of Contents:
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 Controller 247 Sheema B. S. P., Peer Fathima A. and Stella Morris 12.1 Introduction 248 12.2 Methodology 250 12.3 Simulation Results and Discussions 257 12.4 Conclusion 261 13 Economic Aspects and Life Cycle Assessment in Energy Storage Systems 263 Pandiyan P., Senthil Kumar R., Saravanan S. and P. Balakumar 13.1 Introduction 264 13.2 Types of Energy Storage Systems 265 13.3 Life Cycle Assessment (LCA) in Energy Storage Systems 271 13.4 AI in Economic Optimization and Life Cycle Management (LCA) 277 13.5 Challenges and Future Directions 284 13.6 Conclusion 286 14 Energy Monitoring System Using Arduino and Blynk: Design and Simulation 291 Pilla Krishna Satwik, Samartha and Sritama Roy 14.1 Introduction 291 14.2 Motivations 293 14.3 System Architecture 295 14.4 Design and Implementation 297 14.5 Experimental Evaluation 302 14.6 Conclusion 304 15 Smart Home Energy Management System 307 A. R. Kalaiarasi, T. Deepa and S. Angalaeswari 15.1 Introduction 307 15.2 Arduino UNO 310 15.3 Bluetooth Module 310 15.4 Relay Module 311 15.5 Android Application 312 15.6 Software 313 15.7 Flow Diagram 313 15.8 Hardware Implementation 314 15.9 Results and Discussion 315 15.10 Conclusion 317 16 A Study to Analyze the Vulnerabilities and Threats Faced by the Power Sector 319 A. R. Kalaiarasi and Aishwarya G. P. 16.1 Introduction 319 16.2 Analyzing the Risk Index of Threats with Case Study 321 16.3 Cyber Vulnerabilities of Power System Case Study 326 16.4 Conclusion 332 17 Integrated Hybrid Energy Management to Reduce Standby Mode Power Consumption 335 N. Amuthan, N. Sivakumar and B. Gopal Samy 17.1 Introduction 336 17.2 Standby Power Regulations and Standards 338 17.3 Theoretical Framework for Standby Power Reduction 340 17.4 Energy Harvesting and Standby Power 342 17.5 Power Factor Correction (PFC) and Standby Power 344 17.6 Zero Standby Power Solutions 345 17.7 Control Strategies for Power Converters 347 17.8 Software Approaches to Standby Power Reduction 350 17.9 Electromagnetic Interference (EMI) and Standby Power 351 17.10 Cost-Benefit Analysis of Standby Power Reduction 353 17.11 Consumer Electronics and Standby Power 355 17.12 Integration of IoT Devices with Power Converters 357 17.13 Policy Implications and Advocacy for Standby Power Reduction 358 17.14 Educational Initiatives for Standby Power Awareness 360 17.15 Conclusion 362 18 Enhanced Reliability of Electrical Power Transmission in IEEE 24 DC Bus System Using Hybrid Optimization 371 Shereena Gaffoor and Mariamma Chacko 18.1 Introduction 372 18.2 Hybrid Optimization Model Combining GWO and GA 374 18.3 System Description and Model Implementation 375 18.4 Reliability Factors Considered 377 18.5 Conclusion 382 19 Impact of Renewable Energy Sources on Power System Inertia 385 M. Chethan, Ravi Kuppan, M. Dharani and M. Kalpana 19.1 Introduction 386 19.2 VSG: Integration, Modeling, and Controller Structure 389 19.3 Simulation Results and Discussion 392 19.4 Conclusion 395 20 Empowering India Toward Sustainability: An In-Depth Review of Wind Energy Utilization 399 Shibin Shaji John, Heyrin Ann Sony, Ahan Vincent Michael and Sitharthan Ramachandran 20.1 Introduction 400 20.2 Global Status of Wind Energy 401 20.3 Wind Energy Potential in India 404 20.4 Wind Energy Production Capacity in India 405 20.5 Indian Wind Energy Policy for Promoting Installation 411 20.6 Conclusion 412 References 412 About the Editors 415 Index 417

About the Author :
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.