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Artificial Intelligence for Next-Generation Energy Management

Edited by R. Senthil Kumar, V. Indragandhi, R. Selvamathi, and P. Balakumar
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394302987  |  Hardcover  |  
442 pages

One Line Description
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.

Audience
Energy engineers, energy managers, academics, policymakers, and researchers interested in energy management, power-related engineering disciplines, and the development and implementation of power generation technologies

Description
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 Next-Generation 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.

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Author / Editor Details
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.

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Table of Contents
Preface
1. Introduction to Next-Generation Energy Management and Need for AI Solutions
D. Gunapriya, P. Vinoth Kumar, G. Banu, S. Revathy, S. Giriprasad and N. Pushpalatha
1.1 Introduction
1.1.1 Challenges in Traditional Energy Management
1.1.2 Emergence of Next-Generation Energy Management
1.2 Application of AI in Energy Management Revolution
1.3 AI in Energy Sector
1.3.1 AI in Energy Optimization
1.3.2 Data Analytics and Predictive Maintenance
1.3.3 Intelligent Energy Storage and Demand Response
1.4 Role of AI in Energy Efficiency Improvement
1.4.1 Smart Building Management and Automation
1.4.2 AI-Driven Energy Analysis and Optimization
1.5 Role of AI in Demand Forecasting and Load Balancing
1.5.1 AI-Based Effective Forecasting of Energy Balancing
1.5.2 AI-Based Load Balancing
1.6 Enhanced Sustainability and Reduced Carbon Footprint
1.7 AI-Based Grid Stability Enhancement
1.7.1 AI-Driven Grid Monitoring and Control
1.7.2 AI-Based Intelligent Fault Detection
1.8 Predictive Maintenance and Asset Management
1.8.1 Role of AI in Predictive Maintenance
1.8.2 Optimizing Asset Management with AI
1.9 AI-Powered Energy Trading and Price Optimization
1.9.1 Revolutionizing Energy Trading with AI
1.9.2 Price Optimization Using AI for Energy Management
1.10 Ethical Considerations in AI-Powered Energy Management
1.10.1 Enhancing Energy Efficiency
1.10.2 Mitigating Environmental Impact
1.10.3 Empowering Consumers
1.10.4 Data Privacy and Security Concerns
1.10.5 Economic Implications
1.10.6 Ethical Considerations
1.10.7 Workforce Disruption and Reskilling
1.11 Challenges in Incorporating AI in EMS
1.12 Case Studies on Implementing AI for Future Energy Management
1.12.1 Case Study 1: Smart Grid Implementation in User’s Utility Company
1.12.2 Case Study 2: AI-Driven Energy Management in User’s Manufacturing Facility
1.12.3 Case Study 3: AI-Powered Demand Response Program in a Smart City
1.13 Future Research Directions
1.13.1 Track for Future Trends and Innovation
1.13.2 The Importance of Cooperation and Financial Contribution to AI Research and Development
1.13.3 Contribution of AI in Achieving Energy Transient Objective
1.13.4 Developments in Energy Management and AI
1.13.5 Rules and Guidelines for AI in Energy Management
1.13.6 Decision-Making Transparency and Accountability
1.13.7 AI’s Potential to Revolutionize the Power Sector
1.14 Conclusion
References
2. Overview of Innovative Next Generation Energy Storage Technologies
D. Magdalin Mary, G. Sophia Jasmine, V. Vanitha, C. Kumar and T. Dharma Raj
2.1 Introduction
2.2 Energy Storage Techniques
2.3 Mechanical Energy Storage System
2.4 Electrochemical Storage System
2.5 Thermal Storage System
2.6 Electrical Energy Storage System
2.7 Hydrogen Storage System (Power-to-Gas)
References
3. Battery Energy Storage Systems with AI
Ashadevi S. and Latha R.
3.1 Introduction
3.2 System for Managing Batteries
3.2.1 State Estimation
3.2.1.1 State of Charge
3.3 Demand Response Strategies
3.4 Battery Energy Storage System
3.5 Technical Overview of Battery Energy Storage System
3.5.1 WSN in Battery Energy Storage
3.5.2 IoT in Battery Energy Storage
3.5.3 Cloud Computing in Battery Energy Storage
3.5.4 Big Data in Battery Energy Storage
3.5.5 Artificial and Machine Learning Approaches in Battery Energy Storage
3.5.6 Taxonomy of Cyber Security in Energy Storage Systems
3.6 Conclusion and Future Scope
References
4. AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries
Sujith M., Pardeshi D.B., Krushna Lad, Pratiksha Ahire and Karun Pagetra
4.1 Introduction
4.2 Cathode Material
4.2.1 Sodium Iron Phosphate
4.3 Anode Material
4.3.1 Sodium Titanate (Na2Ti3O7)
4.4 Electrolyte
4.4.1 NaSICON (Sodium Super Ionic Conductor)
4.5 State of Discharge (SOD)
4.6 State of Health (SOH)
4.7 BMS Algorithm with AI for SOH
4.8 Conclusion
References
5. Design and Development of an Adaptive Battery Management System for E-Vehicles
Saravanan Palaniswamy, Anbuselvi Mathivanan, A. Siyan Ananth and Sonu R.
5.1 Introduction
5.2 Related Works
5.3 Simulation Design
5.4 System Design
5.5 Implementation
5.6 Experimental Results
5.7 Conclusion
Bibliography
6. Remaining Useful Life (RUL) Prediction for EV Batteries
Anbuselvi Mathivanan, Saravanan Palaniswamy and M. Arul Mozhi
6.1 Introduction
6.1.1 General Consensus
6.1.2 Understanding Battery Metrics
6.1.2.1 State of Current (SoC)
6.1.2.2 State of Health (SoH)
6.1.2.3 RUL
6.1.3 Objective of the Study
6.2 Related Works
6.3 Proposed Model
6.3.1 Dataset
6.3.2 SoC
6.3.2.1 Estimation Methods of SoC
6.3.2.2 EKF Architecture
6.3.2.3 EKF Implementation
6.3.3 SoH
6.3.3.1 Estimation of SoH
6.3.3.2 LSTM
6.4 Hardware Implementation
6.4.1 Raspberry Pi4
6.4.2 Arduino Uno
6.4.3 Current Sensor
6.4.4 DHT11 Sensor
6.4.5 Voltage Sensor
6.5 Outcomes and Analysis
6.5.1 Estimation of SoC
6.5.2 Analysis of SoH Estimation
6.5.3 Prediction of RUL
6.5.4 Hardware
6.6 Conclusion
References
7. Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System
K. Praharshitha, Varun S., Rithick Sarathi M.B. and V. Indragandhi
7.1 Introduction
7.2 Literature Survey
7.3 Technical Specification
7.4 Methodology
7.5 Project Demonstration
7.6 Results
Acknowledgement
References
8. An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques
V. Vanitha, G. Sophia Jasmine and D. Magdalin Mary
8.1 Introduction
8.2 Approaches in Charging Optimization
8.3 System Model
8.4 Proposed Methodology
8.4.1 Process of Proposed C-CObTMPC
8.4.2 Optimization with TMPC Model
8.4.3 Construction of Powertrain Architecture
8.4.4 Optimum Control Strategies
8.5 Results and Discussion
8.5.1 Stability Verification
8.5.2 Performance Analysis
8.5.2.1 Torque Analysis
8.5.2.2 Operating Time
8.5.2.3 Fuel Consumption
8.5.2.4 Cost Objective Function
8.5.3 Discussion
8.6 Conclusion
References
9. Machine Learning and Deep Learning Methods for Energy Management Systems
V. Manimegalai, P. Ravi Raaghav, V. Mohanapriya, T.R. Vashishsdh and S. Palaniappan
9.1 Introduction
9.2 Building Energy Management System
9.2.1 Roles of Deep Learning and Machine Learning
9.2.2 Future Scope
9.3 Grid Optimization
9.3.1 Role of ML and DL in Grid Optimization
9.3.2 Future Scope
9.3.3 Conclusion
9.4 Intelligent Energy Storage
9.4.1 Overview of Energy Storage Technologies
9.4.2 Roles of Machine Learning and Deep Learning
9.4.3 Energy Storage Optimization
9.4.4 Predictive Maintenance
9.4.5 Grid Optimization and Demand Response
9.4.6 Current Research
9.5 Roles of ML and DL
9.5.1 Energy Demand Forecasting
9.5.2 Future Scope
9.6 The Roles of Traditional Methods in Energy Management System
9.6.1 The Roles of DL And ML in Energy Management System
9.6.2 Future Scopes
9.7 Conclusion
References
10. Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance
Deepika Amudala and P. Buchibabu
10.1 Introduction
10.2 Modeling of Grid-Tied PV
10.3 MATLAB Simulation Design and Results
10.3.1 Simulations Results
10.4 Comparison of THD (Total Hormonic Distortion) Values Between PI and ANN
10.5 Conclusion
References
11. Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm
Karthik Nagarajan, Arul Rajagopalan and Priyadarshini Ramasubramanian
11.1 Introduction
11.2 Problem Formulation
11.2.1 Connected Microgrid Network
11.2.2 Mathematical Modeling of Demand Response
11.2.2.1 Cost Function for Customers
11.2.3 Model of Demand Response Integrated within a Grid-Connected Microgrid
11.3 Enhanced Crayfish Optimization Algorithm
11.4 Fuzzy Logic-Based Selection of Optimal Compromise Solution
11.5 Results and Discussion
11.6 Conclusion
References
12. Relative Investigation of Swarm Optimized Load Frequency Controller
Sheema B. S. P., Peer Fathima A. and Stella Morris
12.1 Introduction
12.1.1 Literature Review
12.1.2 Contribution
12.2 Methodology
12.2.1 Modeling of Two-Area Thermal Power Network
12.2.2 Particle Swarm Optimization Algorithm
12.2.3 PSO-PID Controller Design
12.3 Simulation Results and Discussions
12.4 Conclusion
References
13. Economic Aspects and Life Cycle Assessment in Energy Storage Systems
Pandiyan P., Senthil Kumar R., Saravanan S. and P. Balakumar
13.1 Introduction
13.2 Types of Energy Storage Systems
13.2.1 Mechanical Storage
13.2.1.1 Pumped Hydro Storage
13.2.1.2 Compressed Air Energy Storage (CAES)
13.2.1.3 Flywheel Energy Storage (FWES)
13.2.2 Electrochemical Storage
13.2.2.1 Lead-Acid (LA) Batteries
13.2.2.2 Sodium-Sulphur (NaS) Batteries
13.2.2.3 Lithium-Ion Batteries
13.2.2.4 Nickel-Cadmium (NiCd) Batteries
13.2.2.5 Zinc-Bromine (ZnBr) Batteries
13.2.2.6 Vanadium Redox (VR) Batteries
13.2.3 Hydrogen-Based Energy Storage (HES)
13.2.4 Thermal Energy Storage (TES)
13.2.5 Supercapacitor Energy Storage (SCES)
13.3 Life Cycle Assessment (LCA) in Energy Storage Systems
13.3.1 Life Cycle Sustainability Assessment (LCSA)
13.3.2 Life Cycle Assessment Framework
13.3.3 Life Cycle Inventory of the LRES and VRES
13.3.4 Impacts on Human Toxicity
13.3.5 Life Cycle Impact Assessment
13.4 AI in Economic Optimization and Life Cycle Management (LCA)
13.4.1 ANN Based Optimization
13.4.2 Optimization Algorithm
13.4.3 AI and Machine Learning in LCA
13.4.4 Collection of Data and Preprocessing
13.4.5 Development of AI/ML Models for LCA
13.4.6 Predictive Analysis and Optimization
13.5 Challenges and Future Directions
13.5.1 Battery Degradation
13.5.2 SOC Impact on Energy Storage Systems
13.5.3 Economies of Scale
13.5.4 Consistency in Cost Estimation
13.5.5 Need for Uncertainty Analysis
13.5.6 Emerging Energy Storage Technology LCA
13.6 Conclusion
References
14. Energy Monitoring System Using Arduino and Blynk: Design and Simulation
Pilla Krishna Satwik, Samartha and Sritama Roy
14.1 Introduction
14.2 Motivations
14.3 System Architecture
14.3.1 Overall System Overview
14.3.2 Hardware Components
14.3.3 Software Components
14.3.4 Communication Protocols
14.4 Design and Implementation
14.4.1 Sensor Interface and Data Acquisition
14.4.2 Arduino Microcontroller Programming
14.4.3 Blynk Mobile Application
14.4.4 VSPE Configuration
14.5 Experimental Evaluation
14.6 Conclusion
References
15. Smart Home Energy Management System
A. R. Kalaiarasi, T. Deepa and S. Angalaeswari
15.1 Introduction
15.2 Arduino UNO
15.2.1 Power Supply
15.2.2 Transmitter and Receiver
15.3 Bluetooth Module
15.4 Relay Module
15.5 Android Application
15.6 Software
15.7 Flow Diagram
15.8 Hardware Implementation
15.9 Results and Discussion
15.10 Conclusion
References
16. A Study to Analyze the Vulnerabilities and Threats Faced by the Power Sector
A. R. Kalaiarasi and Aishwarya G. P.
16.1 Introduction
16.2 Analyzing the Risk Index of Threats with Case Study
16.2.1 Natural Threats and Impacts
16.2.2 Technical Threats and Impact
16.2.3 Human Threats and Impacts
16.3 Cyber Vulnerabilities of Power System Case Study
16.3.1 Architecture of a SCADA System
16.3.2 Cyber-Attack Scenarios on SCADA System
16.3.2.1 Attacking the Substation
16.3.2.2 Malicious Codes
16.3.2.3 Accessing RTU by Breaking Protocol
16.3.2.4 Attacking the Corruption LAN and Gaining Access to the Substation
16.3.3 Methods to Promote Cyber Security
16.3.4 Proposed Solution for Threats or Vulnerabilities
16.4 Conclusion
References
17. Integrated Hybrid Energy Management to Reduce Standby Mode Power Consumption
N. Amuthan, N. Sivakumar and B. Gopal Samy
17.1 Introduction
17.2 Standby Power Regulations and Standards
17.2.1 International Efficiency Standards
17.2.2 Governmental Regulations on Standby Power
17.2.3 Compliance and Enforcement Mechanisms
17.3 Theoretical Framework for Standby Power Reduction
17.3.1 Principles of Power Conversion Efficiency
17.3.2 Theoretical Models for Standby Power Consumption
17.3.3 Predictive Analysis for Standby Power Reduction
17.4 Energy Harvesting and Standby Power
17.4.1 Utilizing Ambient Energy Sources
17.4.2 Integration with Renewable Energy Systems
17.4.3 Energy Storage and Standby Power Reduction
17.5 Power Factor Correction (PFC) and Standby Power
17.5.1 Basics of Power Factor and Its Importance
17.5.2 PFC Techniques for Reducing Standby Power
17.5.3 Impact of PFC on Overall Energy Consumption
17.6 Zero Standby Power Solutions
17.6.1 Concept and Feasibility of Zero Standby Power
17.6.2 Design Challenges for Zero Standby Power Converters
17.6.3 Case Studies of Zero Standby Power Applications
17.7 Control Strategies for Power Converters
17.7.1 Analog vs. Digital Control Methods
17.7.2 Predictive Control for Standby Power Reduction
17.7.3 Feedback Mechanisms and Efficiency
17.8 Software Approaches to Standby Power Reduction
17.8.1 Firmware Optimization for Power Converters
17.8.2 Algorithmic Solutions for Standby Power Management
17.8.3 Software-Based Monitoring and Control Systems
17.9 Electromagnetic Interference (EMI) and Standby Power
17.9.1 EMI in Power Converters
17.9.2 EMI Reduction Techniques and Standby Power
17.9.3 Standards and Regulations for EMI in Power Converters
17.10 Cost-Benefit Analysis of Standby Power Reduction
17.10.1 Initial Costs vs. Long-Term Savings
17.10.2 Payback Periods for Energy-Efficient Converters
17.10.3 Incentives and Rebates for Adopting Efficient Technologies
17.11 Consumer Electronics and Standby Power
17.11.1 Prevalence of Standby Power in Consumer Devices
17.11.2 Strategies for Consumer Awareness and Behavior Change
17.11.3 Industry Initiatives for Reducing Standby Power in Electronics
17.12 Integration of IoT Devices with Power Converters
17.12.1 IoT for Intelligent Power Management
17.12.2 Data Analytics for Standby Power Optimization
17.12.3 Security Concerns with IoT-Enabled Power Converters
17.13 Policy Implications and Advocacy for Standby Power Reduction
17.13.1 Role of Policymakers in Standby Power Reduction
17.13.2 Advocacy Groups and their Impact
17.13.3 Future Directions for Legislation and Standards
17.14 Educational Initiatives for Standby Power Awareness
17.14.1 Curriculum Development for Energy Efficiency
17.14.2 Public Outreach and Awareness Campaigns
17.14.3 Professional Development and Training Programs
17.15 Conclusion
17.15.1 Summary of Novel Methods for Reducing Standby Power
17.15.2 Implications for Future Research and Development
17.15.3 Final Thoughts on the Importance of Standby Power Reduction
References
18. Enhanced Reliability of Electrical Power Transmission in IEEE 24 DC Bus System Using Hybrid Optimization
Shereena Gaffoor and Mariamma Chacko
18.1 Introduction
18.2 Hybrid Optimization Model Combining GWO and GA
18.3 System Description and Model Implementation
18.4 Reliability Factors Considered
18.4.1 Implications for Future Research in Power System Reliability
18.5 Conclusion
References
19. Impact of Renewable Energy Sources on Power System Inertia
M. Chethan, Ravi Kuppan, M. Dharani and M. Kalpana
19.1 Introduction
19.2 VSG: Integration, Modeling, and Controller Structure
19.3 Simulation Results and Discussion
19.4 Conclusion
References
20. Empowering India Toward Sustainability: An In-Depth Review of Wind Energy Utilization
Shibin Shaji John, Heyrin Ann Sony, Ahan Vincent Michael and Sitharthan Ramachandran
20.1 Introduction
20.2 Global Status of Wind Energy
20.3 Wind Energy Potential in India
20.4 Wind Energy Production Capacity in India
20.4.1 Wind Energy Status in India
20.4.2 Sustained Growth in India’s Wind Energy Market
20.5 Indian Wind Energy Policy for Promoting Installation
20.6 Conclusion
References
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