Artificial Intelligence for Energy Management

Chapter 1: Introduction to Next-Generation Energy Management and Need for AI Solutions

• • 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

Chapter 2: Overview of Innovative Next Generation Energy Storage Technologies

• • 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

Chapter 3: Battery Energy Storage Systems with AI

• • 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

Chapter 4: AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries

• • 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

Chapter 5: Design and Development of an Adaptive Battery Management System for E-Vehicles

• • 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

Chapter 6: Remaining Useful Life (RUL) Prediction for EV Batteries

• • 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 Pi 4
• • 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

Chapter 7: Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System

• • 7.1 Introduction
• • 7.2 Literature Survey
• • 7.3 Technical Specification
• • 7.4 Methodology
• • 7.5 Project Demonstration
• • 7.6 Results
• • Acknowledgement
• • References

Chapter 8: An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques

• • 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

Chapter 9: Machine Learning and Deep Learning Methods for Energy Management Systems

• • 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

Chapter 10: Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance

• • 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

Chapter 11: Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm

• • 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

Chapter 12: Relative Investigation of Swarm Optimized Load Frequency Controller

• • 12.1 Introduction