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Modern Heuristic Optimization Techniques
Theory and Applications to Power Systems
Kwang Y. Lee (Edited by), KY Lee (Author), Mohamed A. El-Sharkawi (Edited by)
9780471457114, Wiley
Hardback, published 11 March 2008
624 pages, Charts: 55 B&W, 0 Color; Drawings: 40 B&W, 0 Color; Screen captures: 15 B&W, 0 Color; Tables: 15 B&W, 0 Color; Graphs: 85 B&W, 0 Color
24.4 x 16.5 x 3.6 cm, 1.002 kg
This text provides excellent, expert level, treatment of a very important systems engineering topic that will benefit students and practicing engineers. (IEEE Power Electronics Society Newsletter, 3rd Quarter, 2008)
Provides power system engineers with basic knowledge of heuristic optimization techniques Several heuristic tools have evolved in the last decade that facilitate solving optimization problems that were previously extremely challenging or even impossible to solve. Now, based on a successful tutorial given by the editors at IEEE Power Engineering Society conferences in New York and Toronto, Modern Heuristic Optimization Techniques explores how developing solutions with these tools offers two major advantages: shortened development time and more robust systems. Composed of two parts, the book begins with an overview of modern heuristic techniques, including the fundamentals of evolutionary computation, genetic algorithms, evolutionary programming and strategies, particle swarm optimization, ant colony search algorithm, differential evolution, simulated annealing, tabu search, and hybrid systems of evolutionary computation. Next, it covers specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power plant control, power system control, and hybrid systems of heuristic methods. Complemented with scores of drawings, charts, graphs, and tables that help bring the material to life, Modern Heuristic Optimization Techniques is the only book of its kind to provide a comprehensive treatment of the subject in a manner that is accessible to students and practitioners alike.
Preface xxi Part 1 Theory of Modern Heuristic Optimization 1 1 Introduction to Evolutionary Computation 3 1.1 Introduction 3 2 Fundamentals of Genetic Algorithms 25 2.1 Introduction 25 3 Fundamentals of Evolution Strategies and Evolutionary Programming 43 3.1 Introduction 43 4 Fundamentals of Particle Swarm Optimization Techniques 71 4.1 Introduction 71 5 Fundamentals of Ant Colony Search Algorithms 89 5.1 Introduction 89 6 Fundamentals of Tabu Search 101 6.1 Introduction 101 7 Fundamentals of Simulated Annealing 123 7.1 Introduction 123 8 Fuzzy Systems 147 8.1 Motivation and Definitions 147 9 Differential Evolution, an Alternative Approach to Evolutionary Algorithm 171 9.1 Introduction 171 10 Pareto Multiobjective Optimization 189 10.1 Introduction 189 11 Trust-Tech Paradigm for Computing High-Quality Optimal Solutions: Method and Theory 209 11.1 Introduction 209 Part 2 Selected Applications of Modern Heuristic Optimization In Power Systems 235 12 Overview of Applications in Power Systems 237 12.1 Introduction 237 13 Application of Evolutionary Technique to Power System Vulnerability Assessment 261 13.1 Introduction 261 14 Applications to System Planning 285 14.1 Introduction 285 15 Applications to Power System Scheduling 337 15.1 Introduction 337 16 Power System Controls 403 16.1 Introduction 403 17 Genetic Algorithms for Solving Optimal Power Flow Problems 471 17.1 Introduction 471 18 An Interactive Compromise Programming-Based Multiobjective Approach to FACTS Control 501 18.1 Introduction 501 19 Hybrid Systems 525 19.1 Introduction 525 References 560
Contributors xxvii
David B. Fogel
1.2 Advantages of Evolutionary Computation 4
1.3 Current Developments 12
1.4 Conclusions 19
Alexandre P. Alves da Silva and Djalma M. Falcao
2.2 Modern Heuristic Search Techniques 25
2.3 Introduction to GAs 27
2.4 Encoding 28
2.5 Fitness Function 30
2.6 Basic Operators 33
2.7 Niching Methods 38
2.8 Parallel Genetic Algorithms 39
2.9 Final Comments 40
Vladimiro Miranda
3.2 Evolution Strategies 46
3.3 Evolutionary Programming 60
3.4 Common Features 63
3.5 Conclusions 68
Yoshikazu Fukuyama
4.2 Basic Particle Swarm Optimization 72
4.3 Variations of Particle Swarm Optimization 76
4.4 Research Areas and Applications 82
4.5 Conclusions 83
Yong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I. K. Yu
5.2 Ant Colony Search Algorithm 90
5.3 Conclusions 99
Alcir J. Monticelli, Rubén Romero, and Eduardo Nobuhiro Asada
6.2 Functions and Strategies in Tabu Search 110
6.3 Applications of Tabu Search 119
6.4 Conclusions 120
Alcir J. Monticelli, Rubén Romero, and Eduardo Nobuhiro Asada
7.2 Basic Principles 125
7.3 Cooling Schedule 127
7.4 SA Algorithm for the Traveling Salesman Problem 131
7.5 SA for Transmission Network Expansion Problem 134
7.6 Parallel Simulated Annealing 140
7.7 Applications of Simulated Annealing 143
7.8 Conclusions 144
Germano Lambert-Torres
8.2 Integration of Fuzzy Systems with Evolutionary Techniques 150
8.3 An Illustrative Example of a Hybrid System 152
8.4 Conclusions 167
Kit Po Wong and ZhaoYang Dong
9.2 Evolutionary Algorithms 172
9.3 Differential Evolution 176
9.4 Key Operators for Differential Evolution 181
9.5 An Optimization Example 184
9.6 Conclusions 186
Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi
10.2 Basic Principles 190
10.3 Solution Approaches 194
10.4 Performance Analysis 202
10.5 Conclusions 205
Hsiao-Dong Chiang and Jaewook Lee
11.2 Problem Preliminaries 210
11.3 A Trust-Tech Paradigm 213
11.4 Theoretical Analysis of Trust-Tech Method 218
11.5 A Numerical Trust-Tech Method 221
11.6 Hybrid Trust-Tech Methods 225
11.7 Numerical Schemes 227
11.8 Numerical Studies 228
11.9 Conclusions Remarks 231
Alexandre P. Alves da Silva, Djalma M. Falcão, and Kwang Y. Lee
12.2 Optimization 237
12.3 Power System Applications 238
12.4 Model Identification 239
12.5 Control 242
12.6 Distribution System Applications 244
12.7 Conclusions 249
Mingoo Kim, Mohamed A. El-Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis
13.2 Vulnerability Assessment and Control 263
13.3 Vulnerability Assessment Challenges 264
13.4 Conclusions 281
Eduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong-Bae Park, Rubén Romero, and Yong-Hua Song
14.2 Generation Expansion 286
14.3 Transmission Network Expansion 297
14.4 Distribution Network Expansion 311
14.5 Reactive Power Planning at Generation–Transmission Level 320
14.6 Reactive Power Planning at Distribution Level 326
14.7 Conclusions 330
Koay Chin Aik, Loi Lei Lai, Kwang Y. Lee, Haiyan Lu, Jong-Bae Park, Yong-Hua Song, Dipti Srinivasan, John G. Vlachogiannis, and I. K. Yu
15.2 Economic Dispatch 337
15.3 Maintenance Scheduling 354
15.4 Cogeneration Scheduling 366
15.5 Short-Term Generation Scheduling of Thermal Units 380
15.6 Constrained Load Flow Problem 385
Yoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen-Ching Liu, Yong-Hua Song, and Ying Xiao
16.2 Power System Controls: Particle Swarm Technique 404
16.3 Power Plant Controller Design with GA 417
16.4 Evolutionary Programming Optimizer and Application in Intelligent Predictive Control 427
16.5 An Interactive Compromise Programming-Based MO Approach to FACTS Control 444
Loi Lei Lai and Nidul Sinha
17.2 Genetic Algorithms 473
17.3 Load Flow Problem 478
17.4 Optimal Power Flow Problem 483
17.5 OPF with FACTS Devices 488
17.6 Conclusions 499
Ying Xiao, Yong-Hua Song, and Chen-Ching Liu
18.2 Review of Multiobjective Optimization Techniques 503
18.3 Formulated MO Optimization Model 506
18.4 Proposed Interactive Displaced Worst Compromise Programming Method 511
18.5 Proposed Interactive Procedure with WC Displacement 513
18.6 Implementation 516
18.7 Numerical Results 516
18.8 Conclusions 521
Vladimiro Miranda
19.2 Capacitor Sizing and Location and Analytical Sensitivities 527
19.3 Unit Commitment Fuzzy Sets and Cleverer Chromosomes 538
19.4 Voltage/Var Control and Loss Reduction in Distribution Networks with an Evolutionary Self-Adaptive Particle Swarm Optimization Algorithm: EPSO 550
19.5 Conclusions 559
Index 563
Subject Areas: Electronics & communications engineering [TJ]
