Applications of Modern Heuristic Optimization Methods in Power and Energy Systems

Reviews state-of-the-art technologies in modern heuristic optimization techniques and presents case studies showing how they have been applied in complex power and energy systems problems

Written by a team of international experts, this book describes the use of metaheuristic applications in the analysis and design of electric power systems. This includes a discussion of optimum energy and commitment of generation (nonrenewable & renewable) and load resources during day-to-day operations and control activities in regulated and competitive market structures, along with transmission and distribution systems.

Applications of Modern Heuristic Optimization Methods in Power and Energy Systems begins with an introduction and overview of applications in power and energy systems before moving on to planning and operation, control, and distribution. Further chapters cover the integration of renewable energy and the smart grid and electricity markets. The book finishes with final conclusions drawn by the editors.

Applications of Modern Heuristic Optimization Methods in Power and Energy Systems:

• Explains the application of differential evolution in electric power systems' active power multi-objective optimal dispatch

• Includes studies of optimization and stability in load frequency control in modern power systems

• Describes optimal compliance of reactive power requirements in near-shore wind power plants

• Features contributions from noted experts in the field

Ideal for power and energy systems designers, planners, operators, and consultants, Applications of Modern Heuristic Optimization Methods in Power and Energy Systems will also benefit engineers, software developers, researchers, academics, and students.

Auteur

KWANG Y. LEE, PhD, is a Professor and Chair of Electrical and Computer Engineering at Baylor University. He is active in the Intelligent Systems Subcommittee and Station Control Subcommittee of the IEEE Power and Energy Society. He served as Editor of IEEE Transactions on Energy Conversion and Associate Editor of IEEE Transactions on Neural Networks and IFAC Journal on Control Engineering Practice. ZITA A. VALE, PhD, is a Full Professor in the Electrical Engineering Department at the School of Engineering of the Polytechnic of Porto and Director of GECADResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development. She has published over 800 works, including more than 100 papers in international scientific journals.

Contenu
Preface xv

Contributors xvii

List of Figures xxi

List of Tables xxxiii

Chapter 1 Introduction 1

1.1 Background 1

1.2 Evolutionary Computation: A Successful Branch of CI 3

1.2.1 Genetic Algorithm 6

1.2.2 Non-dominated Sorting Genetic Algorithm II 8

1.2.3 Evolution Strategies and Evolutionary Programming 8

1.2.4 Simulated Annealing 9

1.2.5 Particle Swarm Optimization 10

1.2.6 Quantum Particle Swarm Optimization 10

1.2.7 Multi-objective Particle Swarm Optimization 11

1.2.8 Particle Swarm Optimization Variants 12

1.2.9 Artificial Bee Colony 13

1.2.10 Tabu Search 14

References 15

Chapter 2 Overview Of Applications In Power And Energy Systems 21

2.1 Applications to Power Systems 21

2.1.1 Unit Commitment 23

2.1.2 Economic Dispatch 24

2.1.3 Forecasting in Power Systems 25

2.1.4 Other Applications in Power Systems 27

2.2 Smart Grid Application Competition Series 28

2.2.1 Problem Description 29

2.2.2 Best Algorithms and Ranks 30

2.2.3 Further Information and How to Download 32

References 32

Chapter 3 Power System Planning And Operation 39

3.1 Introduction 39

3.2 Unit Commitment 40

3.2.1 Introduction 40

3.2.2 Problem Formulation 40

3.2.3 Advancement in UCP Formulations and Models 42

3.2.4 Solution Methodologies, State-of-the-Art, History, and Evolution 46

3.2.5 Conclusions 56

3.3 Economic Dispatch Based on Genetic Algorithms and Particle Swarm Optimization 56

3.3.1 Introduction 56

3.3.2 Fundamentals of Genetic Algorithms and Particle Swarm Optimization 58

3.3.3 Economic Dispatch Problem 60

3.3.4 GA Implementation to ED 63

3.3.5 PSO Implementation to ED 71

3.3.6 Numerical Example 79

3.3.7 Conclusions 87

3.4 Differential Evolution in Active Power Multi-Objective Optimal Dispatch 87

3.4.1 Introduction 87

3.4.2 Differential Evolution for Multi-Objective Optimization 88

3.4.3 Multi-Objective Model of Active Power Optimization for Wind Power Integrated Systems 97

3.4.4 Case Studies 100

3.4.5 Analyses of Dispatch Plan 105

3.4.6 Conclusions 106

3.5 Hydrothermal Coordination 106

3.5.1 Introduction 106

3.5.2 Hydrothermal Coordination Formulation 107

3.5.3 Problem Decomposition 110

3.5.4 Case Studies 111

3.5.5 Conclusions 114

3.6 Meta-Heuristic Method for Gms Based on Genetic Algorithm 115

3.6.1 History 115

3.6.2 Meta-heuristic Search Method 116

3.6.3 Flexible GMS 119

3.6.4 User-Friendly GMS System 131

3.6.5 Conclusion 141

3.7 Load Flow 143

3.7.1 Introduction 143

3.7.2 Load Flow Analysis in Electrical Power Systems 144

3.7.3 Particle Swarm Optimization and Mutation Operation 148

3.7.4 Load Flow Computation via Particle Swarm Optimization with Mutation Operation 150

3.7.5 Numerical Results 153

3.7.6 Conclusions 160

3.8 Artificial Bee Colony Algorithm for Solving Optimal Power Flow 161

3.8.1 Optimization in Power System Operation 162

3.8.2 The Optimal Power Flow Problem 162

3.8.3 Artificial Bee Colony 166

3.8.4 ABC for the OPF Problem 168

3.8.5 Case Studies 170

3.8.6 Conclusions 176

3.9 OPF Test Bed and Performance Evaluation of Modern Heuristic Optimization 176

3.9.1 Introduction 176

3.9.2 Problem Definition 177

3.9.3 OPF Test Systems 178

3.9.4 Differential Evolutionary Particle Swarm Optimization: DEEPSO 183

3.9.5 Enhanced Version of MeanVariance Mapping Optimization Algorithm: MVMO-PHM 187

3.9.6 Evaluation Results 193 3...