Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems

This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover 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 system control, and hybrid systems of heuristic methods.

Table of Contents:
Preface xxi Contributors xxvii Part 1 Theory of Modern Heuristic Optimization 1 1 Introduction to Evolutionary Computation 3 David B. Fogel 1.1 Introduction 3 1.2 Advantages of Evolutionary Computation 4 1.2.1 Conceptual Simplicity 4 1.2.2 Broad Applicability 6 1.2.3 Outperform Classic Methods on Real Problems 7 1.2.4 Potential to Use Knowledge and Hybridize with Other Methods 8 1.2.5 Parallelism 8 1.2.6 Robust to Dynamic Changes 9 1.2.7 Capability for Self-Optimization 10 1.2.8 Able to Solve Problems That Have No Known Solutions 11 1.3 Current Developments 12 1.3.1 Review of Some Historical Theory in Evolutionary Computation 12 1.3.2 No Free Lunch Theorem 12 1.3.3 Computational Equivalence of Representations 14 1.3.4 Schema Theorem in the Presence of Random Variation 16 1.3.5 Two-Armed Bandits and the Optimal Allocation of Trials 17 1.4 Conclusions 19 Acknowledgments 20 References 20 2 Fundamentals of Genetic Algorithms 25 Alexandre P. Alves da Silva and Djalma M. Falcao 2.1 Introduction 25 2.2 Modern Heuristic Search Techniques 25 2.3 Introduction to GAs 27 2.4 Encoding 28 2.5 Fitness Function 30 2.5.1 Premature Convergence 32 2.5.2 Slow Finishing 32 2.6 Basic Operators 33 2.6.1 Selection 33 2.6.2 Crossover 36 2.6.3 Mutation 38 2.6.4 Control Parameters Estimation 38 2.7 Niching Methods 38 2.8 Parallel Genetic Algorithms 39 2.9 Final Comments 40 Acknowledgments 41 References 41 3 Fundamentals of Evolution Strategies and Evolutionary Programming 43 Vladimiro Miranda 3.1 Introduction 43 3.2 Evolution Strategies 46 3.2.1 The General (µ, κ, λ, ρ) Evolution Strategies Scheme 47 3.2.2 Some More Basic Concepts 50 3.2.3 The Early (1 + 1)ES and the 1/5 Rule 51 3.2.4 Focusing on the Optimum 53 3.2.5 The (1, λ)ES and σSA Self-Adaptation 54 3.2.6 How to Choose a Value for the Learning Parameter? 56 3.2.7 The (µ, l)ES as an Extension of (1, λ)ES 57 3.2.8 Self-Adaptation in (µ, λ)ES 58 3.3 Evolutionary Programming 60 3.3.1 The (µ + λ) Bridge to ES 60 3.3.2 A Scheme for Evolutionary Programming 61 3.3.3 Other Evolutionary Programming Variants 63 3.4 Common Features 63 3.4.1 Enhancing the Mutation Process 63 3.4.2 Recombination as a Major Factor 65 3.4.3 Handling Constraints 67 3.4.4 Starting Point 67 3.4.5 Fitness Function 67 3.4.6 Computing 68 3.5 Conclusions 68 References 69 4 Fundamentals of Particle Swarm Optimization Techniques 71 Yoshikazu Fukuyama 4.1 Introduction 71 4.2 Basic Particle Swarm Optimization 72 4.2.1 Background of Particle Swarm Optimization 72 4.2.2 Original PSO 72 4.3 Variations of Particle Swarm Optimization 76 4.3.1 Discrete PSO 76 4.3.2 PSO for MINLPs 77 4.3.3 Constriction Factor Approach (CFA) 77 4.3.4 Hybrid PSO (HPSO) 78 4.3.5 Lbest Model 79 4.3.6 Adaptive PSO (APSO) 79 4.3.7 Evolutionary PSO (EPSO) 81 4.4 Research Areas and Applications 82 4.5 Conclusions 83 References 83 5 Fundamentals of Ant Colony Search Algorithms 89 Yong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I. K. Yu 5.1 Introduction 89 5.2 Ant Colony Search Algorithm 90 5.2.1 Behavior of Real Ants 90 5.2.2 Ant Colony Algorithms 91 5.2.3 Major Characteristics of Ant Colony Search Algorithms 98 5.3 Conclusions 99 References 99 6 Fundamentals of Tabu Search 101 Alcir J. Monticelli, Rubén Romero, and Eduardo Nobuhiro Asada 6.1 Introduction 101 6.1.1 Overview of the Tabu Search Approach 101 6.1.2 Problem Formulation 103 6.1.3 Coding and Representation 104 6.1.4 Neighborhood Structure 105 6.1.5 Characterization of the Neighborhood 108 6.2 Functions and Strategies in Tabu Search 110 6.2.1 Recency-Based Tabu Search 110 6.2.2 Basic Tabu Search Algorithm 112 6.2.3 The Use of Long-Term Memory in Tabu Search 115 6.3 Applications of Tabu Search 119 6.4 Conclusions 120 References 120 7 Fundamentals of Simulated Annealing 123 Alcir J. Monticelli, Rubén Romero, and Eduardo Nobuhiro Asada 7.1 Introduction 123 7.2 Basic Principles 125 7.2.1 Metropolis Algorithm 125 7.2.2 Simulated Annealing Algorithm 126 7.3 Cooling Schedule 127 7.3.1 Determination of the Initial Temperature T0 128 7.3.2 Determination of Nk 129 7.3.3 Determination of Cooling Rate 130 7.3.4 Stopping Criterion 130 7.4 SA Algorithm for the Traveling Salesman Problem 131 7.4.1 Problem Coding 131 7.4.2 Evaluation of the Cost Function 132 7.4.3 Cooling Schedule 133 7.4.4 Comments on the Results for the TSP 134 7.5 SA for Transmission Network Expansion Problem 134 7.5.1 Problem Coding 136 7.5.2 Determination of the Initial Solution 136 7.5.3 Neighborhood Structure 138 7.5.4 Variation of the Objective Function 139 7.5.5 Cooling Schedule 140 7.6 Parallel Simulated Annealing 140 7.6.1 Division Algorithm 141 7.6.2 Clustering Algorithm 142 7.7 Applications of Simulated Annealing 143 7.8 Conclusions 144 References 144 8 Fuzzy Systems 147 Germano Lambert-Torres 8.1 Motivation and Definitions 147 8.1.1 Introduction 147 8.1.2 Typical Actions in Fuzzy Systems 148 8.2 Integration of Fuzzy Systems with Evolutionary Techniques 150 8.2.1 Integration Types of Hybrid Systems 150 8.2.2 Hybrid Systems in Evolutionary Techniques 151 8.2.3 Evolutionary Algorithms and Fuzzy Logic 152 8.3 An Illustrative Example of a Hybrid System 152 8.3.1 Parking Conditions 153 8.3.2 Creation of the Fuzzy Control 154 8.3.3 First Simulations 156 8.3.4 Problem Presentation 156 8.3.5 Genetic Training Modulus Description 158 8.3.6 The Option to Define the Starting Positions 158 8.3.7 The Option Genetic Training 158 8.3.8 Tests 163 8.4 Conclusions 167 References 168 9 Differential Evolution, an Alternative Approach to Evolutionary Algorithm 171 Kit Po Wong and ZhaoYang Dong 9.1 Introduction 171 9.2 Evolutionary Algorithms 172 9.2.1 Basic EAs 172 9.2.2 Virtual Population-Based Acceleration Techniques 174 9.3 Differential Evolution 176 9.3.1 Function Optimization Formulation 176 9.3.2 DE Fundamentals 177 9.4 Key Operators for Differential Evolution 181 9.4.1 Encoding 181 9.4.2 Mutation 181 9.4.3 Crossover 183 9.4.4 Other Operators 183 9.5 An Optimization Example 184 9.6 Conclusions 186 Acknowledgments 186 References 186 10 Pareto Multiobjective Optimization 189 Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi 10.1 Introduction 189 10.2 Basic Principles 190 10.2.1 Generic Formulation of MO Problems 191 10.2.2 Pareto Optimality Concepts 191 10.2.3 Objectives of Multiobjective Optimization 193 10.3 Solution Approaches 194 10.3.1 Classic Methods 194 10.3.2 Intelligent Methods 196 10.4 Performance Analysis 202 10.4.1 Objective of Performance Assessment 202 10.4.2 Comparison Methodologies 203 10.5 Conclusions 205 Acknowledgments 205 References 205 11 Trust-Tech Paradigm for Computing High-Quality Optimal Solutions: Method and Theory 209 Hsiao-Dong Chiang and Jaewook Lee 11.1 Introduction 209 11.2 Problem Preliminaries 210 11.3 A Trust-Tech Paradigm 213 11.3.1 Phase I 213 11.3.2 Phase II 214 11.4 Theoretical Analysis of Trust-Tech Method 218 11.5 A Numerical Trust-Tech Method 221 11.5.1 Computing Another Local Optimal Solution 222 11.5.2 Computing Tier-One Local Optimal Solutions 223 11.5.3 Computing Tier-N Solutions 224 11.6 Hybrid Trust-Tech Methods 225 11.7 Numerical Schemes 227 11.8 Numerical Studies 228 11.9 Conclusions Remarks 231 References 232 Part 2 Selected Applications of Modern Heuristic Optimization In Power Systems 235 12 Overview of Applications in Power Systems 237 Alexandre P. Alves da Silva, Djalma M. Falcão, and Kwang Y. Lee 12.1 Introduction 237 12.2 Optimization 237 12.3 Power System Applications 238 12.4 Model Identification 239 12.4.1 Dynamic Load Modeling 239 12.4.2 Short-Term Load Forecasting 240 12.4.3 Neural Network Training 241 12.5 Control 242 12.5.1 Examples 243 12.6 Distribution System Applications 244 12.6.1 Network Reconfiguration for Loss Reduction 245 12.6.2 Optimal Protection and Switching Devices Placement 246 12.6.3 Prioritizing Investments in Distribution Networks 247 12.7 Conclusions 249 References 250 13 Application of Evolutionary Technique to Power System Vulnerability Assessment 261 Mingoo Kim, Mohamed A. El-Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis 13.1 Introduction 261 13.2 Vulnerability Assessment and Control 263 13.3 Vulnerability Assessment Challenges 264 13.3.1 Complexity of Power System 264 13.3.2 VA On-line Speed 265 13.3.3 Feature Selection 265 13.3.4 Vulnerability Border 270 13.3.5 Selection of Vulnerability Index 276 13.4 Conclusions 281 References 281 14 Applications to System Planning 285 Eduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong-Bae Park, Rubén Romero, and Yong-Hua Song 14.1 Introduction 285 14.2 Generation Expansion 286 14.2.1 A Coding Strategy for an Improved GA for the Least-Cost GEP 288 14.2.2 Fitness Function 288 14.2.3 Creation of an Artificial Initial Population 289 14.2.4 Stochastic Crossover Elitism and Mutation 291 14.2.5 Numerical Examples 292 14.2.6 Parameters for GEP and IGA 293 14.2.7 Numerical Results 295 14.3 Transmission Network Expansion 297 14.3.1 Overview of Static Transmission Network Planning 297 14.3.2 Solution Techniques for the Transmission Expansion Planning Problem 300 14.3.3 Coding, Problem Representation, and Test Systems 302 14.3.4 Complexity of the Test Systems 304 14.3.5 Simulated Annealing 306 14.3.6 Genetic Algorithms in Transmission Network Expansion Planning 307 14.3.7 Tabu Search in Transmission Network Expansion Planning 309 14.3.8 Hybrid TS/GA/SA Algorithm in Transmission Network Expansion Planning 310 14.3.9 Comments on the Performance of Meta-heuristic Methods in Transmission Network Expansion Planning 311 14.4 Distribution Network Expansion 311 14.4.1 Dynamic Planning of Distribution System Expansion: A Complete GA Model 312 14.4.2 Dynamic Planning of Distribution System Expansion: An Efficient GA Application 316 14.4.3 Application of TS to the Design of Distribution Networks in FRIENDS 317 14.5 Reactive Power Planning at Generation–Transmission Level 320 14.5.1 Benders Decomposition of the Reactive Power Planning Problem 321 14.5.2 Solution Algorithm 323 14.5.3 Results for the IEEE 30-Bus System 324 14.6 Reactive Power Planning at Distribution Level 326 14.6.1 Modeling Chromosome Repair Using an Analytical Model 326 14.6.2 Evolutionary Programming/Evolution Strategies Under Test 327 14.7 Conclusions 330 References 330 15 Applications to Power System Scheduling 337 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 15.1 Introduction 337 15.2 Economic Dispatch 337 15.2.1 Economic Dispatch Problem 337 15.2.2 GA Implementation to ED 339 15.2.3 PSO Implementation to ED 346 15.2.4 Numerical Example 348 15.2.5 Summary 354 15.3 Maintenance Scheduling 354 15.3.1 Maintenance Scheduling Problem 354 15.3.2 GA, PSO, and ES Implementation 355 15.3.3 Simulation Results 365 15.3.4 Summary 366 15.4 Cogeneration Scheduling 366 15.4.1 Cogeneration Scheduling Problem 367 15.4.2 IGA Implementation 370 15.4.3 Case Study 373 15.4.4 Summary 374 15.4.5 Nomenclature 379 15.5 Short-Term Generation Scheduling of Thermal Units 380 15.5.1 Short-Term Generation Scheduling Problem 380 15.5.2 ACSA Implementation 382 15.5.3 Experimental results 385 15.6 Constrained Load Flow Problem 385 15.6.1 Constrained Load Flow Problem 385 15.6.2 Heuristic Ant Colony Search Algorithm Implementation 386 15.6.3 Test Examples 390 15.6.4 Summary 399 References 399 16 Power System Controls 403 Yoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen-Ching Liu, Yong-Hua Song, and Ying Xiao 16.1 Introduction 403 16.2 Power System Controls: Particle Swarm Technique 404 16.2.1 Problem Formulation of VVC 405 16.2.2 Expansion of PSO for MINLP 406 16.2.3 Voltage Security Assessment 407 16.2.4 VVC Using PSO 408 16.2.5 Numerical Examples 409 16.2.6 Summary 416 16.3 Power Plant Controller Design with GA 417 16.3.1 Overview of the GA 417 16.3.2 The Boiler-Turbine Model 419 16.3.3 The GA Control System Design 420 16.3.4 GA Design Results 423 16.4 Evolutionary Programming Optimizer and Application in Intelligent Predictive Control 427 16.4.1 Structure of the Intelligent Predictive Controller 428 16.4.2 Power Plant Model 430 16.4.3 Control Input Optimization 431 16.4.4 Self-Organized Neuro-Fuzzy Identifier 435 16.4.5 Rule Generation and Tuning 438 16.4.6 Controller Implementation 442 16.4.7 Summary 444 16.5 An Interactive Compromise Programming-Based MO Approach to FACTS Control 444 16.5.1 Review of MO Optimization Techniques 446 16.5.2 Formulated MO Optimization Model 449 16.5.3 Power Flow Control Model of FACTS Devices 450 16.5.4 Proposed Interactive DWCP Method 453 16.5.5 Proposed Interactive Procedure with Worst Compromise Displacement 455 16.5.6 Implementation 457 16.5.7 Numerical Results 457 16.5.8 Summary 462 References 464 17 Genetic Algorithms for Solving Optimal Power Flow Problems 471 Loi Lei Lai and Nidul Sinha 17.1 Introduction 471 17.2 Genetic Algorithms 473 17.2.1 Terms Used in GA 473 17.3 Load Flow Problem 478 17.4 Optimal Power Flow Problem 483 17.4.1 Application Examples 485 17.5 OPF with FACTS Devices 488 17.5.1 FACTS Model 492 17.5.2 Problem Formulation 495 17.5.3 Numerical Results 496 17.6 Conclusions 499 References 499 18 An Interactive Compromise Programming-Based Multiobjective Approach to FACTS Control 501 Ying Xiao, Yong-Hua Song, and Chen-Ching Liu 18.1 Introduction 501 18.2 Review of Multiobjective Optimization Techniques 503 18.2.1 Weighting Method 503 18.2.2 Goal Programming 504 18.2.3 1-Constraint Method 504 18.2.4 Compromise Programming 504 18.2.5 Fuzzy Set Theory Applications 505 18.2.6 Genetic Algorithm 505 18.2.7 Interactive Procedure 506 18.3 Formulated MO Optimization Model 506 18.3.1 Formulated MO Optimization Model for FACTS Control 507 18.3.2 Power Flow Control Model of FACTS Devices 508 18.4 Proposed Interactive Displaced Worst Compromise Programming Method 511 18.4.1 Applied Fuzzy CP 511 18.4.2 Operation Cost Minimization 512 18.4.3 Local Power Flow Control 512 18.5 Proposed Interactive Procedure with WC Displacement 513 18.5.1 Phase 1: Model Formulation 513 18.5.2 Phase 2: Noninferior Solution Calculation 514 18.5.3 Phase 3: Scenario Evaluation 514 18.6 Implementation 516 18.7 Numerical Results 516 18.8 Conclusions 521 References 521 19 Hybrid Systems 525 Vladimiro Miranda 19.1 Introduction 525 19.2 Capacitor Sizing and Location and Analytical Sensitivities 527 19.2.1 From Darwin to Lamarck: Three Models 528 19.2.2 Building a Lamarckian Acquisition of Improvements 529 19.2.3 Analysis of a Didactic Example 531 19.3 Unit Commitment Fuzzy Sets and Cleverer Chromosomes 538 19.3.1 The Deceptive Characteristics of Unit Commitment Problems 538 19.3.2 Similarity Between the Capacitor Placement and the Unit Commitment Problems 539 19.3.3 The Need for Cleverer Chromosomes 540 19.3.4 A Biological Touch: The Chromosome as a Program 541 19.3.5 A Real-World Example: The CARE Model in Crete Greece 542 19.3.6 Fitness Evaluation: Reliability (Spinning Reserve as a Fuzzy Constraint) 547 19.3.7 Illustrative Results 547 19.4 Voltage/Var Control and Loss Reduction in Distribution Networks with an Evolutionary Self-Adaptive Particle Swarm Optimization Algorithm: EPSO 550 19.4.1 Justifying a Hybrid Approach 550 19.4.2 The Principles of EPSO: Reproduction and Movement Rule 551 19.4.3 Mutating Strategic Parameters 552 19.4.4 The Merits of EPSO 553 19.4.5 Experiencing with EPSO: Basic EPSO Model 554 19.4.6 EPSO in Test Functions 554 19.4.7 EPSO in Loss Reduction and Voltage/VAR Control: Definition of the Problem 557 19.4.8 Applying EPSO in the Management of Networks with Distributed Generation 558 19.5 Conclusions 559 References 560 Index 563