Economic Dispatch and Generation Scheduling in Operation of Modern Power Systems
Optimization Techniques and Real-World Applications
- 1st Edition - July 1, 2026
- Latest edition
- Editors: K. P. Singh Parmar, Bhuvnesh Khokhar, Tripta Thakur, Sarika Khushalani Solanki
- Language: English
Economic Dispatch and Generation Scheduling in Operation of Modern Power Systems: Optimization Techniques and Real-World Applications is a comprehensive resource, bridging the ga… Read more
The book examines the rapid evolution of power systems, emphasizing the need for innovative approaches to address the complexities of energy supply and demand.
Case studies and real-world applications are included to provide readers with insights into practical implementations and contemporary challenges in the field. This is a valuable resource for professionals, students, and researchers in energy, enhancing understanding of how to optimize energy, reduce operational costs, and improve reliability in an evolving landscape.
- Presents solutions for optimizing the dispatch of distributed energy resources (DERs) using machine learning, artificial intelligence, and real-time control systems
- Explains complicated mathematical models and optimization approaches and algorithms for enhanced forecasting, scheduling, and dispatch that account for variations in renewable energy supply
- Demonstrates the use of optimization techniques through case studies, simulations, and practical applications
1. Introduction to Economic Dispatch, Security-Constrained Economic Dispatch, and Generation Scheduling
1.1 A Perspective of Modern Power Systems
1.1.1 Evolution of Traditional Power Systems to Smart Grids
1.1.2 Key Components: Generation, Transmission, and Distribution
1.1.3 Modern Power System Challenges: Integration of Renewable Energy, Energy Storage Systems, and Electric Vehicles
1.2 Concept of Economic Dispatch
1.2.1 Definition and Purpose of Economic Dispatch
1.2.2 Economic Dispatch Vs Unit Commitment
1.2.3 Historical Evolution and Importance in Power System Operation
1.3 Security-Constrained Economic Dispatch
1.3.1 Objectives and Benefits of SCED
1.3.2 Difference Between ED and SCED
1.3.3 Real-World Challenges in Maintaining System Security
1.4 Generation Scheduling
1.4.1 Importance and Objectives of Generation Scheduling in Power Systems
1.4.2 Types of Generation Resources (Conventional, Renewable, and Hybrid)
1.4.3 Constraints and Objectives
1.4.4 Multi-objective Generation Scheduling
1.4.5 Generation Scheduling in Competitive, Deregulated, and Regulated Markets
1.5 Role of Energy Storage and Electric Vehicles in Power Systems
1.5.1 Overview of Energy Storage Technologies and Their Role in Economic Dispatch
1.5.2 Integration of Electric Vehicles in Power Systems: Opportunities and Challenges
1.5.3 Impact of Energy Storage and EVs on System Flexibility and Grid Stability
1.6 Conclusion References
2. Mathematical Formulation of Economic Dispatch and Generation Scheduling
2.1 Classical Economic Dispatch Problem
2.1.1 Cost Function of Generation Units: Hydro and Thermal
2.1.2 Power Balance and Constraints on Generators
2.1.3 Inclusion of Transmission Losses and Other System Constraints
2.2 Dynamic Economic Dispatch
2.2.1 Time-Varying Load and Generation Requirements
2.2.2 Real-Time Economic Dispatch and Multi-Interval Dispatch
2.2.3 Constraints Due to Renewable Energy Integration
2.3 Generation Scheduling
2.3.1 Mixed-Integer Programming for Generation Scheduling
2.3.2 Minimum Up/Down Time, Ramp Rate Constraints
2.3.3 Scheduling with Renewable and Non-Renewable Generation Mixes
2.4 Security-Constrained Economic Dispatch
2.4.1 Mathematical Formulation for SCED with Contingency Analysis
2.4.2 N-1 Security Criteria and Voltage Stability
2.4.3 SCED for Congestion Management and System Reliability
2.5 Co-Optimization of Energy Storage and Generation Scheduling
2.5.1 Storage Models in Economic Dispatch
2.5.2 Charge/Discharge Constraints in Storage Integration
2.5.3 Coordinated Optimization of Generation and Storage
2.6 Incorporation of Electric Vehicles in Economic Dispatch
2.6.1 Modeling EV Charging and Discharging for Grid Services
2.6.2 Optimization of EV Charging for Load Shifting
2.6.3 Co-Optimization of Generation, Energy Storage, and EVs
2.7 Conclusion References
3. Optimization Techniques for Economic Dispatch, SCED, and Generation Scheduling
3.1 Classical Optimization Techniques
3.1.1 Linear Programming for Economic Dispatch
3.1.2 Nonlinear Programming and Quadratic Programming
3.1.3 Lambda Iteration and Lagrange Multiplier Methods
3.2 Heuristic and Metaheuristic Optimization Techniques
3.2.1 Genetic Algorithms
3.2.2 Particle Swarm Optimization
3.2.3 Differential Evolution
3.2.4 Ant Colony Optimization
3.3 Machine Learning and AI-Based Optimization
3.3.1 Neural Networks for Forecasting and Dispatch
3.3.2 Reinforcement Learning for Real-Time Dispatch Decisions
3.3.3 Deep Learning Models for Large-Scale Dispatch Problems
3.4 Hybrid Optimization Techniques
3.4.1 Combining Metaheuristics with Classical Approaches
3.4.2 Hybrid AI and Evolutionary Algorithms for SCED
3.4.3 Multi-Objective Optimization for Dispatch and Scheduling
3.5 Conclusion References
4. Economic Dispatch and SCED with Renewable Energy Integration
4.1 Characteristics of Renewable Energy Sources
4.1.1 Intermittency and Variability in Solar and Wind Energy
4.1.2 Challenges in Dispatch with High Renewable Penetration
4.1.3 Role of Forecasting in Renewable Dispatch
4.2 Economic Dispatch with Wind Energy
4.2.1 Stochastic Models for Wind Energy Dispatch
4.2.2 Wind Power Forecasting and Dispatch Optimization
4.2.3 Case Studies: Wind Power Integration in Power Systems
4.3 Economic Dispatch with Solar Energy
4.3.1 Modeling Solar Power Variability for Economic Dispatch
4.3.2 Real-Time Dispatch and Short-Term Forecasting
4.3.3 Case Studies: Solar Energy in Grid-Connected Systems
4.4 SCED with High Penetration of Renewables
4.4.1 Impact of Renewable Energy on Power System Security
4.4.2 SCED Models Incorporating Intermittent Renewables
4.4.3 Case Studies: SCED with High Renewable Penetration
4.5 Conclusion References
5. Energy Storage Systems in Economic Dispatch and SCED
5.1 Overview of Energy Storage Technologies
5.1.1 Battery Technologies: Lithium-Ion, Flow Batteries, and Other Emerging Technologies
5.1.2 Pumped Hydro Storage and Emerging Technologies
5.1.3 Grid-Scale Storage Solutions for Renewable Energy Support
5.2 Role of Energy Storage in Economic Dispatch
5.2.1 Load Shifting, Peak Shaving, and Arbitrage
5.2.2 Smoothing Renewable Output with Energy Storage
5.2.3 Storage for Contingency Reserve and Ancillary Services
5.3 Co-Optimization of Storage with Generation
5.3.1 Charge and Discharge Cycle Optimization
5.3.2 Optimal Sizing and Placement of Storage Systems
5.3.3 Case Studies: Grid Stability with Energy Storage Integration
5.4 Energy Storage in Security-Constrained Economic Dispatch
5.4.1 Incorporating Energy Storage in SCED Models
5.4.2 Storage for Enhancing System Resilience and Security
5.4.3 Case Studies: SCED with Battery Storage in Large Power Systems
5.5 Conclusion References
6. Electric Vehicles in Economic Dispatch and Power System Optimization
6.1 EVs in Modern Power Systems
6.1.1 Growing Role of EVs in Power System Operations
6.1.2 Vehicle-to-Grid (V2G) Technologies and Applications
6.1.3 Challenges in Integrating EVs with Power Systems
6.2 EV Charging Optimization for Economic Dispatch
6.2.1 Impact of EV Charging on Load Profiles
6.2.2 Optimal Charging Scheduling for Grid Balancing
6.2.3 Smart Charging Strategies and Real-Time Control
6.3 Vehicle-to-Grid (V2G) Applications in Grid Support
6.3.1 V2G for Frequency Regulation and Voltage Support
6.3.2 EVs as Distributed Energy Resources (DERs)
6.3.3 Case Studies: V2G Applications in Urban Grids
6.4 Co-Optimization of Generation, Energy Storage, and Electric Vehicles
6.4.1 Joint Optimization Models for Dispatch and V2G
6.4.2 Role of EV Fleets in Ancillary Services Markets
6.4.3 Case Studies: Optimizing EV Integration with Renewable Energy
6.5 Conclusion References
7. Smart Grids, Distributed Energy Resources, and Microgrids in Economic Dispatch
7.1 Smart Grid Technologies and Economic Dispatch
7.1.1 Features and Architecture of Smart Grids
7.1.2 Real-Time Monitoring and Control Systems for Dispatch
7.1.3 Impact of Smart Grids on Dispatch Optimization
7.2 Distributed Energy Resources and Microgrids
7.2.1 Integration of Solar, Wind, and Storage in Microgrids
7.2.2 Economic Dispatch in Microgrids and Distributed Systems
7.2.3 Coordination Between Centralized and Distributed Dispatch
7.3 Demand Response in Economic Dispatch
7.3.1 Role of Demand Response in Balancing Generation and Load
7.3.2 Price-Based Demand Response and Real-Time Optimization
7.3.3 Optimization of Demand Response Programs in Power Markets
7.4 Economic Dispatch in Virtual Power Plants
7.4.1 Concept and Operation of VPPs
7.4.2 Aggregation of Distributed Resources for Grid Support
7.4.3 Case Studies: Virtual Power Plants for Economic Dispatch
7.5 Conclusion References
8. Case Studies and Real-World Applications of Economic Dispatch, SCED, and Generation Scheduling
8.1 Large-Scale Applications of Economic Dispatch
8.1.1 Case Study: Economic Dispatch in North American Grids
8.1.2 Case Study: Economic Dispatch in European Power Systems
8.2 SCED with Renewable Energy and Storage Integration
8.2.1 Case Study: SCED in High-Renewable Power Systems
8.2.2 Case Study: Storage Integration in Economic Dispatch
8.3 Electric Vehicles and V2G in Real-World Systems
8.3.1 Case Study: EVs and V2G Integration in Californian Grid
8.3.2 Case Study: V2G Applications in Urban Power Networks
8.4 Microgrid and Smart Grid Applications
8.4.1 Case Study: Economic Dispatch in Islanded Microgrids
8.4.2 Case Study: Smart Grid Technologies in Modern Grids
8.5 Conclusion References
9. Challenges in Economic Dispatch, SCED, and Generation Scheduling in Modern Power Systems
9.1 Integration of Renewable Energy Sources
9.1.1 Variability and Uncertainty of Renewable Energy Generation
9.1.2 Balancing Reliability and Cost with Intermittent Resources
9.1.3 Adaptation of Economic Dispatch Models for Renewables
9.2 Technological Advancements and Their Implications
9.2.1 Influence of Smart Grid Technologies on Dispatch and Scheduling
9.2.2 Challenges in Implementing Artificial Intelligence and Machine Learning
9.2.3 Data Management Issues with New Technologies
9.3 Security and Reliability Concerns
9.3.1 Ensuring Grid Security and Resilience in Economic Dispatch
9.3.2 Cybersecurity Threats and Their Implications for Dispatch Processes
9.3.3 Addressing Natural Disasters and Extreme Weather Events
9.4 Economic Considerations
9.4.1 Challenges in Forecasting Fuel Prices and Supply Chain Stability
9.4.2 Investment and Financing Issues for Power System Upgrades
9.4.3 Economic Impacts of Global Energy Trends on Local Dispatch Strategies
9.5 Conclusion References
10. Future Trends in Economic Dispatch, SCED, and Power System Optimization
10.1 Role of AI and Machine Learning in Power Systems
10.1.1 AI for Economic Dispatch Optimization and Grid Management
10.1.2 Machine Learning for Forecasting and Decision Making
10.1.3 AI-Based Decision Support Systems for SCED
10.2 Advancements in Energy Storage and Electric Vehicles
10.2.1 Emerging Storage Technologies for Enhanced Grid Resilience
10.2.2 Next-Generation EVs and Grid Support Technologies
10.2.3 Synergies Between EVs, Storage, and Renewable Energy
10.3 Blockchain and Decentralized Power Markets
10.3.1 Blockchain for Peer-to-Peer Energy Trading
10.3.2 Decentralized Dispatch and Virtual Power Plants
10.3.3 Case Studies of Blockchain in Power Systems
10.4 Conclusion References
- Edition: 1
- Latest edition
- Published: July 1, 2026
- Language: English
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K. P. Singh Parmar
Dr. K. P. Singh Parmar is the Deputy Director (Tech) at the National Power Training Institute (NPTI), an apex body under the Ministry of Power, Government of India. He has also served as the Head of the Centre for Advanced Management and Power Studies (CAMPS) at NPTI. Previously, he worked as an Assistant Professor in the Department of Electrical Engineering at JSS Academy of Technical Education, Noida. He received his B.E. (Hons.) in Electrical Engineering from Government Engineering College, Rewa, the M.Tech. degree in Energy from IIT Delhi, and the Ph.D. degree in Electrical Engineering (Power Systems) from IIT Guwahati. With more than two decades of experience in teaching, training, consultancy, and research, Dr. Parmar has contributed to several major national and international consultancy projects, including a flagship assignment for establishing the National Power Academy in the Kingdom of Saudi Arabia. He also served as Project In-Charge for the underground HT/LT cable infrastructure development and automation of Ayodhya city. He has guided nine M.Tech. theses and two Ph.D. scholars, published over 50 research papers in reputed journals, and authored two books and several book chapters. Recently, he co-authored the book Load Frequency Control of Microgrids (CRC Press, 2024).
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Bhuvnesh Khokhar
Dr. Bhuvnesh Khokhar is an Associate Professor in the Department of Electrical Engineering at Galgotias College of Engineering and Technology (GCET), Greater Noida. He received B.E. degree in Electrical Engineering from CRSCE (now DCRUST), Murthal, M.Tech. degree in Power Systems from DCRUST, and the Ph.D. degree in Power Systems from DCRUST, Murthal. He has more than 13 years of teaching and research experience. His research interests include microgrids, renewable energy integration, electric vehicles, load frequency control, and optimization-based intelligent control techniques for modern power systems. He has published around 16 research papers in reputed international journals and is a coauthor of the book Load Frequency Control of Microgrids (CRC Press, 2024).
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Tripta Thakur
Dr. Tripta Thakur is the Vice Chancellor of Uttarakhand Technical University, Dehradun, India. She previously served as Director General of the National Power Training Institute (NPTI), Ministry of Power, Government of India, and earlier as Professor and Head of the Department of Electrical Engineering at MANIT Bhopal. She received the B.E. degree in Electrical Engineering, the M.Tech. degree in Power Electronics from IIT Kanpur, and the Ph.D. degree from IIT Delhi. With over 28 years of teaching, research, and leadership experience, she has nearly 100 publications and has led several national capacity-building initiatives and consultancy projects in the power sector. She is a recipient of the ISGF Innovation Award 2024 (Women in Power) and is the author of the book Load Frequency Control of Microgrid (CRC Press, 2024).
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Sarika Khushalani Solanki
Dr. Sarika Khushalani Solanki (Senior Member, IEEE) is an Associate Professor in Lane Department of Computer Science and Electrical Engineering. Her research interests include smart grids, modeling and analysis of distribution systems and microgrids, and data mining and AI applications to power systems. Solanki is the recipient of the prestigious NSF CAREER Award. She served as Chairman for IEEE PES Distribution Systems subcommittee and Power Engineering Career Promotion and Workforce Development Subcommittee. She has served as editor of transactions in smart grid and MDPI journal. She has delivered 20+ keynotes and IEEE distinguished lectures She is author of more than 50 technical publications including a book in preparation on Economic Dispatch and Generation Scheduling in Modern Power Systems. She has been a reviewer for NSF and DOE while also engaging in STEM outreach initiatives.