Artificial Intelligence for Energy Management

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.

• Preface xvii1 Introduction to Next-Generation Energy Management and Need for AI Solutions 1D. Gunapriya, P. Vinoth Kumar, G. Banu, S. Revathy, S. Giriprasad and N. Pushpalatha1.1 Introduction 21.1.1 Challenges in Traditional Energy Management 31.1.2 Emergence of Next-Generation Energy Management 31.2 Application of AI in Energy Management Revolution 51.3 AI in Energy Sector 61.3.1 AI in Energy Optimization 61.3.2 Data Analytics and Predictive Maintenance 61.3.3 Intelligent Energy Storage and Demand Response 61.4 Role of AI in Energy Efficiency Improvement 71.4.1 Smart Building Management and Automation 71.4.2 AI-Driven Energy Analysis and Optimization 71.5 Role of AI in Demand Forecasting and Load Balancing 71.5.1 AI-Based Effective Forecasting of Energy Balancing 81.5.2 AI-Based Load Balancing 81.6 Enhanced Sustainability and Reduced Carbon Footprint 81.7 AI-Based Grid Stability Enhancement 81.7.1 AI-Driven Grid Monitoring and Control 81.7.2 AI-Based Intelligent Fault Detection 91.8 Predictive Maintenance and Asset Management 91.8.1 Role of AI in Predictive Maintenance 91.8.2 Optimizing Asset Management with AI 91.9 AI-Powered Energy Trading and Price Optimization 91.9.1 Revolutionizing Energy Trading with AI 91.9.2 Price Optimization Using AI for Energy Management 101.10 Ethical Considerations in AI-Powered Energy Management 101.10.1 Enhancing Energy Efficiency 101.10.2 Mitigating Environmental Impact 111.10.3 Empowering Consumers 111.10.4 Data Privacy and Security Concerns 111.10.5 Economic Implications 121.10.6 Ethical Considerations 121.10.7 Workforce Disruption and Reskilling 131.11 Challenges in Incorporating AI in EMS 131.12 Case Studies on Implementing AI for Future Energy Management 181.12.1 Case Study 1: Smart Grid Implementation in User’s Utility Company 181.12.2 Case Study 2: AI-Driven Energy Management in User’s Manufacturing Facility 191.12.3 Case Study 3: AI-Powered Demand Response Program in a Smart City 201.13 Future Research Directions 211.13.1 Track for Future Trends and Innovation 211.13.2 The Importance of Cooperation and Financial Contribution to AI Research and Development 211.13.3 Contribution of AI in Achieving Energy Transient Objective 221.13.4 Developments in Energy Management and AI 221.13.5 Rules and Guidelines for AI in Energy Management 221.13.6 Decision-Making Transparency and Accountability 221.13.7 AI’s Potential to Revolutionize the Power Sector 231.14 Conclusion 23References 232 Overview of Innovative Next Generation Energy Storage Technologies 27D. Magdalin Mary, G. Sophia Jasmine, V. Vanitha, C. Kumar and T. Dharma Raj2.1 Introduction 282.2 Energy Storage Techniques 292.3 Mechanical Energy Storage System 352.4 Electrochemical Storage System 352.5 Thermal Storage System 362.6 Electrical Energy Storage System 372.7 Hydrogen Storage System (Power-to-Gas) 37References 373 Battery Energy Storage Systems with AI 39Ashadevi S. and Latha R.3.1 Introduction 393.2 System for Managing Batteries 413.2.1 State Estimation 443.2.1.1 State of Charge 443.3 Demand Response Strategies 523.4 Battery Energy Storage System 533.5 Technical Overview of Battery Energy Storage System 543.5.1 WSN in Battery Energy Storage 543.5.2 IoT in Battery Energy Storage 563.5.3 Cloud Computing in Battery Energy Storage 563.5.4 Big Data in Battery Energy Storage 573.5.5 Artificial and Machine Learning Approaches in Battery Energy Storage 583.5.6 Taxonomy of Cyber Security in Energy Storage Systems 603.6 Conclusion and Future Scope 60References 614 AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries 65Sujith M., Pardeshi D.B., Krushna Lad, Pratiksha Ahire and Karun Pagetra4.1 Introduction 664.2 Cathode Material 684.2.1 Sodium Iron Phosphate 684.3 Anode Material 714.3.1 Sodium Titanate (Na2Ti3O7) 714.4 Electrolyte 734.4.1 NaSICON (Sodium Super Ionic Conductor) 734.5 State of Discharge (SOD) 754.6 State of Health (SOH) 764.7 BMS Algorithm with AI for SOH 774.8 Conclusion 79References 805 Design and Development of an Adaptive Battery Management System for E-Vehicles 83Saravanan Palaniswamy, Anbuselvi Mathivanan, A. Siyan Ananth and Sonu R.5.1 Introduction 845.2 Related Works 855.3 Simulation Design 875.4 System Design 895.5 Implementation 955.6 Experimental Results 965.7 Conclusion 98Bibliography 986 Remaining Useful Life (RUL) Prediction for EV Batteries 101Anbuselvi Mathivanan, Saravanan Palaniswamy and M. Arul Mozhi6.1 Introduction 1026.1.1 General Consensus 1026.1.2 Understanding Battery Metrics 1036.1.2.1 State of Current (SoC) 1036.1.2.2 State of Health (SoH) 1046.1.2.3 Rul 1046.1.3 Objective of the Study 1056.2 Related Works 1056.3 Proposed Model 1066.3.1 Dataset 1076.3.2 SoC 1086.3.2.1 Estimation Methods of SoC 1096.3.2.2 EKF Architecture 1106.3.2.3 EKF Implementation 1116.3.3 SoH 1126.3.3.1 Estimation of SoH 1126.3.3.2 Lstm 1136.4 Hardware Implementation 1156.4.1 Raspberry Pi 4 1156.4.2 Arduino Uno 1166.4.3 Current Sensor 1166.4.4 DHT11 Sensor 1186.4.5 Voltage Sensor 1196.5 Outcomes and Analysis 1206.5.1 Estimation of SoC 1206.5.2 Analysis of SoH Estimation 1216.5.3 Prediction of RUL 1226.5.4 Hardware 1236.6 Conclusion 124References 1257 Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System 129K. Praharshitha, Varun S., Rithick Sarathi M.B. and V. Indragandhi7.1 Introduction 1297.2 Literature Survey 1307.3 Technical Specification 1327.4 Methodology 1337.5 Project Demonstration 1337.6 Results 135Acknowledgement 138References 1388 An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques 141V. Vanitha, G. Sophia Jasmine and D. Magdalin Mary8.1 Introduction 1428.2 Approaches in Charging Optimization 1448.3 System Model 1458.4 Proposed Methodology 1468.4.1 Process of Proposed C-CObTMPC 1478.4.2 Optimization with TMPC Model 1498.4.3 Construction of Powertrain Architecture 1508.4.4 Optimum Control Strategies 1528.5 Results and Discussion 1538.5.1 Stability Verification 1548.5.2 Performance Analysis 1548.5.2.1 Torque Analysis 1558.5.2.2 Operating Time 1588.5.2.3 Fuel Consumption 1598.5.2.4 Cost Objective Function 1608.5.3 Discussion 1618.6 Conclusion 162References 1629 Machine Learning and Deep Learning Methods for Energy Management Systems 165V. Manimegalai, P. Ravi Raaghav, V. Mohanapriya, T.R. Vashishsdh and S. Palaniappan9.1 Introduction 1669.2 Building Energy Management System 1679.2.1 Roles of Deep Learning and Machine Learning 1699.2.2 Future Scope 1719.3 Grid Optimization 1739.3.1 Role of ML and DL in Grid Optimization 1749.3.2 Future Scope 1839.3.3 Conclusion 1849.4 Intelligent Energy Storage 1849.4.1 Overview of Energy Storage Technologies 1859.4.2 Roles of Machine Learning and Deep Learning 1879.4.3 Energy Storage Optimization 1909.4.4 Predictive Maintenance 1929.4.5 Grid Optimization and Demand Response 1949.4.6 Current Research 1979.5 Roles of ml and dl 1999.5.1 Energy Demand Forecasting 2009.5.2 Future Scope 2019.6 The Roles of Traditional Methods in Energy Management System 2049.6.1 The Roles of DL And ML in Energy Management System 2069.6.2 Future Scopes 2089.7 Conclusion 209References 21010 Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance 213Deepika Amudala and P. Buchibabu10.1 Introduction 21310.2 Modeling of Grid-Tied PV 21510.3 MATLAB Simulation Design and Results 21610.3.1 Simulations Results 21710.4 Comparison of THD (Total Hormonic Distortion) Values Between PI and ANN 22210.5 Conclusion 223References 22311 Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm 225Karthik Nagarajan, Arul Rajagopalan and Priyadarshini Ramasubramanian11.1 Introduction 22611.2 Problem Formulation 22711.2.1 Connected Microgrid Network 22811.2.2 Mathematical Modeling of Demand Response 23011.2.2.1 Cost Function for Customers 23111.2.3 Model of Demand Response Integrated within a Grid-Connected Microgrid 23311.3 Enhanced Crayfish Optimization Algorithm 23411.4 Fuzzy Logic-Based Selection of Optimal Compromise Solution 23911.5 Results and Discussion 24011.6 Conclusion 244References 24412 Relative Investigation of Swarm Optimized Load Frequency Controller 247Sheema B. S. P., Peer Fathima A. and Stella Morris12.1 Introduction 24812.1.1 Literature Review 24912.1.2 Contribution 25012.2 Methodology 25012.2.1 Modeling of Two-Area Thermal Power Network 25012.2.2 Particle Swarm Optimization Algorithm 25312.2.3 PSO-PID Controller Design 25412.3 Simulation Results and Discussions 25712.4 Conclusion 261References 26113 Economic Aspects and Life Cycle Assessment in Energy Storage Systems 263Pandiyan P., Senthil Kumar R., Saravanan S. and P. Balakumar13.1 Introduction 26413.2 Types of Energy Storage Systems 26513.2.1 Mechanical Storage 26613.2.1.1 Pumped Hydro Storage 26613.2.1.2 Compressed Air Energy Storage (CAES) 26713.2.1.3 Flywheel Energy Storage (FWES) 26713.2.2 Electrochemical Storage 26713.2.2.1 Lead-Acid (LA) Batteries 26813.2.2.2 Sodium-Sulphur (NaS) Batteries 26813.2.2.3 Lithium-Ion Batteries 26813.2.2.4 Nickel-Cadmium (NiCd) Batteries 26913.2.2.5 Zinc-Bromine (ZnBr) Batteries 26913.2.2.6 Vanadium Redox (VR) Batteries 27013.2.3 Hydrogen-Based Energy Storage (HES) 27013.2.4 Thermal Energy Storage (TES) 27013.2.5 Supercapacitor Energy Storage (SCES) 27113.3 Life Cycle Assessment (LCA) in Energy Storage Systems 27113.3.1 Life Cycle Sustainability Assessment (LCSA) 27313.3.2 Life Cycle Assessment Framework 27413.3.3 Life Cycle Inventory of the LRES and VRES 27513.3.4 Impacts on Human Toxicity 27613.3.5 Life Cycle Impact Assessment 27613.4 AI in Economic Optimization and Life Cycle Management (LCA) 27713.4.1 ANN Based Optimization 27813.4.2 Optimization Algorithm 27913.4.3 AI and Machine Learning in LCA 28013.4.4 Collection of Data and Preprocessing 28113.4.5 Development of AI/ML Models for LCA 28213.4.6 Predictive Analysis and Optimization 28313.5 Challenges and Future Directions 28413.5.1 Battery Degradation 28413.5.2 SOC Impact on Energy Storage Systems 28413.5.3 Economies of Scale 28413.5.4 Consistency in Cost Estimation 28513.5.5 Need for Uncertainty Analysis 28513.5.6 Emerging Energy Storage Technology LCA 28513.6 Conclusion 286References 28614 Energy Monitoring System Using Arduino and Blynk: Design and Simulation 291Pilla Krishna Satwik, Samartha and Sritama Roy14.1 Introduction 29114.2 Motivations 29314.3 System Architecture 29514.3.1 Overall System Overview 29514.3.2 Hardware Components 29514.3.3 Software Components 29514.3.4 Communication Protocols 29614.4 Design and Implementation 29714.4.1 Sensor Interface and Data Acquisition 29714.4.2 Arduino Microcontroller Programming 29814.4.3 Blynk Mobile Application 30014.4.4 VSPE Configuration 30114.5 Experimental Evaluation 30214.6 Conclusion 304References 30515 Smart Home Energy Management System 307A. R. Kalaiarasi, T. Deepa and S. Angalaeswari15.1 Introduction 30715.2 Arduino UNO 31015.2.1 Power Supply 31015.2.2 Transmitter and Receiver 31015.3 Bluetooth Module 31015.4 Relay Module 31115.5 Android Application 31215.6 Software 31315.7 Flow Diagram 31315.8 Hardware Implementation 31415.9 Results and Discussion 31515.10 Conclusion 317References 31716 A Study to Analyze the Vulnerabilities and Threats Faced by the Power Sector 319A. R. Kalaiarasi and Aishwarya G. P.16.1 Introduction 31916.2 Analyzing the Risk Index of Threats with Case Study 32116.2.1 Natural Threats and Impacts 32116.2.2 Technical Threats and Impact 32316.2.3 Human Threats and Impacts 32516.3 Cyber Vulnerabilities of Power System Case Study 32616.3.1 Architecture of a SCADA System 32816.3.2 Cyber-Attack Scenarios on SCADA System 32916.3.2.1 Attacking the Substation 32916.3.2.2 Malicious Codes 32916.3.2.3 Accessing RTU by Breaking Protocol 32916.3.2.4 Attacking the Corruption LAN and Gaining Access to the Substation 33016.3.3 Methods to Promote Cyber Security 33016.3.4 Proposed Solution for Threats or Vulnerabilities 33116.4 Conclusion 332References 33217 Integrated Hybrid Energy Management to Reduce Standby Mode Power Consumption 335N. Amuthan, N. Sivakumar and B. Gopal Samy17.1 Introduction 33617.2 Standby Power Regulations and Standards 33817.2.1 International Efficiency Standards 33817.2.2 Governmental Regulations on Standby Power 33917.2.3 Compliance and Enforcement Mechanisms 33917.3 Theoretical Framework for Standby Power Reduction 34017.3.1 Principles of Power Conversion Efficiency 34017.3.2 Theoretical Models for Standby Power Consumption 34117.3.3 Predictive Analysis for Standby Power Reduction 34117.4 Energy Harvesting and Standby Power 34217.4.1 Utilizing Ambient Energy Sources 34217.4.2 Integration with Renewable Energy Systems 34217.4.3 Energy Storage and Standby Power Reduction 34317.5 Power Factor Correction (PFC) and Standby Power 34417.5.1 Basics of Power Factor and Its Importance 34417.5.2 PFC Techniques for Reducing Standby Power 34417.5.3 Impact of PFC on Overall Energy Consumption 34517.6 Zero Standby Power Solutions 34517.6.1 Concept and Feasibility of Zero Standby Power 34617.6.2 Design Challenges for Zero Standby Power Converters 34617.6.3 Case Studies of Zero Standby Power Applications 34717.7 Control Strategies for Power Converters 34717.7.1 Analog vs. Digital Control Methods 34817.7.2 Predictive Control for Standby Power Reduction 34817.7.3 Feedback Mechanisms and Efficiency 34917.8 Software Approaches to Standby Power Reduction 35017.8.1 Firmware Optimization for Power Converters 35017.8.2 Algorithmic Solutions for Standby Power Management 35117.8.3 Software-Based Monitoring and Control Systems 35117.9 Electromagnetic Interference (EMI) and Standby Power 35117.9.1 EMI in Power Converters 35217.9.2 EMI Reduction Techniques and Standby Power 35217.9.3 Standards and Regulations for EMI in Power Converters 35317.10 Cost-Benefit Analysis of Standby Power Reduction 35317.10.1 Initial Costs vs. Long-Term Savings 35417.10.2 Payback Periods for Energy-Efficient Converters 35417.10.3 Incentives and Rebates for Adopting Efficient Technologies 35517.11 Consumer Electronics and Standby Power 35517.11.1 Prevalence of Standby Power in Consumer Devices 35517.11.2 Strategies for Consumer Awareness and Behavior Change 35617.11.3 Industry Initiatives for Reducing Standby Power in Electronics 35617.12 Integration of IoT Devices with Power Converters 35717.12.1 IoT for Intelligent Power Management 35717.12.2 Data Analytics for Standby Power Optimization 35817.12.3 Security Concerns with IoT-Enabled Power Converters 35817.13 Policy Implications and Advocacy for Standby Power Reduction 35817.13.1 Role of Policymakers in Standby Power Reduction 35917.13.2 Advocacy Groups and their Impact 35917.13.3 Future Directions for Legislation and Standards 36017.14 Educational Initiatives for Standby Power Awareness 36017.14.1 Curriculum Development for Energy Efficiency 36117.14.2 Public Outreach and Awareness Campaigns 36117.14.3 Professional Development and Training Programs 36217.15 Conclusion 36217.15.1 Summary of Novel Methods for Reducing Standby Power 36217.15.2 Implications for Future Research and Development 36317.15.3 Final Thoughts on the Importance of Standby Power Reduction 363References 36418 Enhanced Reliability of Electrical Power Transmission in IEEE 24 DC Bus System Using Hybrid Optimization 371Shereena Gaffoor and Mariamma Chacko18.1 Introduction 37218.2 Hybrid Optimization Model Combining GWO and GA 37418.3 System Description and Model Implementation 37518.4 Reliability Factors Considered 37718.4.1 Implications for Future Research in Power System Reliability 38118.5 Conclusion 382References 38219 Impact of Renewable Energy Sources on Power System Inertia 385M. Chethan, Ravi Kuppan, M. Dharani and M. Kalpana19.1 Introduction 38619.2 VSG: Integration, Modeling, and Controller Structure 38919.3 Simulation Results and Discussion 39219.4 Conclusion 395References 39620 Empowering India Toward Sustainability: An In-Depth Review of Wind Energy Utilization 399Shibin Shaji John, Heyrin Ann Sony, Ahan Vincent Michael and Sitharthan Ramachandran20.1 Introduction 40020.2 Global Status of Wind Energy 40120.3 Wind Energy Potential in India 40420.4 Wind Energy Production Capacity in India 40520.4.1 Wind Energy Status in India 40620.4.2 Sustained Growth in India’s Wind Energy Market 40920.5 Indian Wind Energy Policy for Promoting Installation 41120.6 Conclusion 412References 412About the Editors 415Index 417