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 Khushlani Solanki
- Language: English
As global electricity demand rises and renewable energy sources like wind and solar become more prevalent, optimizing power generation and dispatch has become crucial in modern… Read more
As global electricity demand rises and renewable energy sources like wind and solar become more prevalent, optimizing power generation and dispatch has become crucial in modern power system management. Economic Dispatch and Scheduling in Modern Power Systems: Optimization Techniques and Real-World Applications is a comprehensive resource, bridging the gap between traditional power generation methodologies and advanced optimization techniques. It examines the rapid evolution of power systems, emphasizing the need for innovative approaches to address the complexities of energy supply and demand. Chapters review foundational principles of economic dispatch and generation scheduling, then demonstrate mathematical models, optimization techniques, and the impact of renewable energy integration. The role of cutting edge methods such as Artificial Intelligence and machine learning in dispatch, energy storage systems and electric vehicles, and advancements in smart grid technologies are also examined. Case studies and real-world applications are included to provide readers with insights into practical implementations and contemporary challenges in the field. Economic Dispatch and Scheduling in Modern Power Systems: Optimization Techniques and Real-World Applications is valuable to 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 use of optimization techniques through case studies, simulations, and practical applications
Power System Engineers, Researchers and Practitioners in Power Systems. Graduate and Postgraduate Students in Electrical Engineering, Energy Systems, and Power Systems, Power System Operators and Planners, Professionals working in Renewable Energy and Energy Storage Integration
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
KP
K. P. Singh Parmar
Dr. K. P. Singh Parmar is currently serving as the Head of Department-CAMPS & Deputy Director (Technical) at National Power Training Institute (NPTI), apex body of Ministry of Power, Government of India. Before joining NPTI, he worked as an Assistant Professor in the
Electrical Engineering Department at JSS Academy of Technical Education, NOIDA, UttarvPradesh. He earned his B.E. (Honors) in Electrical Engineering from Government Engineering College, Rewa, M.P. He completed his M.Tech. in Energy from IIT Delhi, followed by the Ph.D. in Electrical (Power Systems) from IIT Guwahati. He played a key role in the consultancy project for setting up the National Power Academy in the Kingdom of Saudi Arabia (KSA), a project that received significant recognition. He also served as the project in-charge for the Detailed Project Report (DPR) for converting HT/LT overhead lines to underground cables, automation, and modern infrastructure development for Ayodhya city, successfully submitted to MVVNL. Dr. Parmar has guided nine M.Tech. dissertations and two Ph.D. scholars. He has contributed to numerous publications from Elsevier, Taylor & Francis, Springer, and IEEE.
Affiliations and expertise
National Power Training Institute (NPTI), apex body of Ministry of Power, Government of IndiaBK
Bhuvnesh Khokhar
Dr. Bhuvnesh Khokhar is currently working as an Associate Professor, Department of Electrical Engineering, Galgotias College of Engineering and Technology, Greater Noida. He has over 11 years of teaching experience He holds a Ph.D. in Power Systems from Deenbandhu Chhotu Ram University of Science & Technology (DCRUST), Murthal, Sonipat, and a M.Tech in Power Systems from DCRUST, Murthal, Sonipat and a B.E. in Electrical Engineering from Chhotu Ram State College of Engineering CRSCE), Murthal, Sonipat now DCRUST. Dr. Khokhar's research interests focus on advanced areas of power systems, including microgrid operation, renewable energy integration, electric vehicles, load frequency control,
and optimization algorithms. His notable contributions are in the field of intelligent control approaches for power systems, which aim to improve the stability and efficiency of modern power systems. He contributed to international journals from Springer, Elsevier, Taylor & Francis, and IEEE.
Affiliations and expertise
Department of Electrical Engineering, Galgotias College of Engineering and Technology, Greater NoidaTT
Tripta Thakur
Dr. Tripta Thakur is Director General, National Power Training Institute (NPTI), apex body of Ministry of Power, Government of India. She was earlier Head and Professor, Electrical Engineering Department at the National Institute of Technology, MANIT-Bhopal, India. She received her degree in Electrical Engineering and Master’s degree in Power Electronics from IIT Kanpur, and has a PhD from IIT-Delhi. She has received awards such as Commonwealth Research Scholar at University of Dundee (2005-2008), UK, Commonwealth Academic fellow at Durham University Business School (2014), UK, COFUND Senior researcher at Durham University Business School (2016), Visiting Faculty at Asian Institute of Technology, Bangkok (2010), technical member for International Electrotechnical Commission (IEC), SEG4 Group, ISGF (MoP) working group member, etc. Recently, she received the ISGF Innovation Awards 2024 for the Category “Women in Power”. She has teaching and research experience of 28 years and has numerous publications. She has also been a Consultant for evolving a possible Common South Asian Electricity Markets, and worked with distribution companies in India.
Affiliations and expertise
National Power Training Institute (NPTI), IndiaSS
Sarika Khushlani Solanki
Dr. Sarika Khushalani Solanki is an Associate Professor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University (WVU), where she
has been teaching since 2009. With over two decades of experience in academia and industry, she is a nationally recognized expert in power systems engineering. Her research spans across smart grids, microgrids, and the integration of renewable energy into distribution systems, with an emphasis on the application of AI and data mining in power systems. Dr. Solanki has been published in high impact international journals. Dr. Solanki has mentored numerous students, successfully guiding several master’s and Ph.D. students who have gone on to work with leading energy companies, including Dominion, Energy and Entergy. She has been instrumental in fostering research and career growth among her students, many of whom have received prestigious awards for their contributions to the field through posters and papers.Affiliations and expertise
Lane Department of Computer Science and Electrical Engineering, West Virginia University (WVU), USA