{"product_id":"modern-heuristic-optimization-techniques-theory-and-applications-to-power-systems-hardback-9780471457114","title":"Modern Heuristic Optimization Techniques; Theory and Applications to Power Systems (Hardback) 9780471457114","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModern Heuristic Optimization Techniques\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheory and Applications to Power Systems\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eKwang Y. Lee (Edited by), KY Lee (Author), Mohamed A. El-Sharkawi (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471457114, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 11 March 2008\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e624 pages, Charts: 55 B\u0026amp;W, 0 Color; Drawings: 40 B\u0026amp;W, 0 Color; Screen captures: 15 B\u0026amp;W, 0 Color; Tables: 15 B\u0026amp;W, 0 Color; Graphs: 85 B\u0026amp;W, 0 Color\u003cbr\u003e24.4 x 16.5 x 3.6 cm, 1.002 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003eThis text provides excellent, expert level, treatment of a very important systems engineering topic that will benefit students and practicing engineers. (\u003ci\u003eIEEE Power Electronics Society Newsletter\u003c\/i\u003e, 3rd Quarter, 2008)\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eContributors xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Theory of Modern Heuristic Optimization 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Evolutionary Computation 3\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDavid B. Fogel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Advantages of Evolutionary Computation 4\u003c\/p\u003e \u003cp\u003e1.2.1 Conceptual Simplicity 4\u003c\/p\u003e \u003cp\u003e1.2.2 Broad Applicability 6\u003c\/p\u003e \u003cp\u003e1.2.3 Outperform Classic Methods on Real Problems 7\u003c\/p\u003e \u003cp\u003e1.2.4 Potential to Use Knowledge and Hybridize with Other Methods 8\u003c\/p\u003e \u003cp\u003e1.2.5 Parallelism 8\u003c\/p\u003e \u003cp\u003e1.2.6 Robust to Dynamic Changes 9\u003c\/p\u003e \u003cp\u003e1.2.7 Capability for Self-Optimization 10\u003c\/p\u003e \u003cp\u003e1.2.8 Able to Solve Problems That Have No Known Solutions 11\u003c\/p\u003e \u003cp\u003e1.3 Current Developments 12\u003c\/p\u003e \u003cp\u003e1.3.1 Review of Some Historical Theory in Evolutionary Computation 12\u003c\/p\u003e \u003cp\u003e1.3.2 No Free Lunch Theorem 12\u003c\/p\u003e \u003cp\u003e1.3.3 Computational Equivalence of Representations 14\u003c\/p\u003e \u003cp\u003e1.3.4 Schema Theorem in the Presence of Random Variation 16\u003c\/p\u003e \u003cp\u003e1.3.5 Two-Armed Bandits and the Optimal Allocation of Trials 17\u003c\/p\u003e \u003cp\u003e1.4 Conclusions 19\u003c\/p\u003e \u003cp\u003eAcknowledgments 20\u003c\/p\u003e \u003cp\u003eReferences 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Fundamentals of Genetic Algorithms 25\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlexandre P. Alves da Silva and Djalma M. Falcao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Modern Heuristic Search Techniques 25\u003c\/p\u003e \u003cp\u003e2.3 Introduction to GAs 27\u003c\/p\u003e \u003cp\u003e2.4 Encoding 28\u003c\/p\u003e \u003cp\u003e2.5 Fitness Function 30\u003c\/p\u003e \u003cp\u003e2.5.1 Premature Convergence 32\u003c\/p\u003e \u003cp\u003e2.5.2 Slow Finishing 32\u003c\/p\u003e \u003cp\u003e2.6 Basic Operators 33\u003c\/p\u003e \u003cp\u003e2.6.1 Selection 33\u003c\/p\u003e \u003cp\u003e2.6.2 Crossover 36\u003c\/p\u003e \u003cp\u003e2.6.3 Mutation 38\u003c\/p\u003e \u003cp\u003e2.6.4 Control Parameters Estimation 38\u003c\/p\u003e \u003cp\u003e2.7 Niching Methods 38\u003c\/p\u003e \u003cp\u003e2.8 Parallel Genetic Algorithms 39\u003c\/p\u003e \u003cp\u003e2.9 Final Comments 40\u003c\/p\u003e \u003cp\u003eAcknowledgments 41\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fundamentals of Evolution Strategies and Evolutionary Programming 43\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eVladimiro Miranda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 43\u003c\/p\u003e \u003cp\u003e3.2 Evolution Strategies 46\u003c\/p\u003e \u003cp\u003e3.2.1 The General (\u003ci\u003eµ\u003c\/i\u003e, \u003ci\u003eκ\u003c\/i\u003e, \u003ci\u003eλ\u003c\/i\u003e, \u003ci\u003eρ\u003c\/i\u003e) Evolution Strategies Scheme 47\u003c\/p\u003e \u003cp\u003e3.2.2 Some More Basic Concepts 50\u003c\/p\u003e \u003cp\u003e3.2.3 The Early (1 + 1)ES and the 1\/5 Rule 51\u003c\/p\u003e \u003cp\u003e3.2.4 Focusing on the Optimum 53\u003c\/p\u003e \u003cp\u003e3.2.5 The (1, \u003ci\u003eλ\u003c\/i\u003e)ES and σSA Self-Adaptation 54\u003c\/p\u003e \u003cp\u003e3.2.6 How to Choose a Value for the Learning Parameter? 56\u003c\/p\u003e \u003cp\u003e3.2.7 The (\u003ci\u003eµ\u003c\/i\u003e, l)ES as an Extension of (1, \u003ci\u003eλ\u003c\/i\u003e)ES 57\u003c\/p\u003e \u003cp\u003e3.2.8 Self-Adaptation in (\u003ci\u003eµ\u003c\/i\u003e, \u003ci\u003eλ\u003c\/i\u003e)ES 58\u003c\/p\u003e \u003cp\u003e3.3 Evolutionary Programming 60\u003c\/p\u003e \u003cp\u003e3.3.1 The (\u003ci\u003eµ\u003c\/i\u003e + \u003ci\u003eλ\u003c\/i\u003e) Bridge to ES 60\u003c\/p\u003e \u003cp\u003e3.3.2 A Scheme for Evolutionary Programming 61\u003c\/p\u003e \u003cp\u003e3.3.3 Other Evolutionary Programming Variants 63\u003c\/p\u003e \u003cp\u003e3.4 Common Features 63\u003c\/p\u003e \u003cp\u003e3.4.1 Enhancing the Mutation Process 63\u003c\/p\u003e \u003cp\u003e3.4.2 Recombination as a Major Factor 65\u003c\/p\u003e \u003cp\u003e3.4.3 Handling Constraints 67\u003c\/p\u003e \u003cp\u003e3.4.4 Starting Point 67\u003c\/p\u003e \u003cp\u003e3.4.5 Fitness Function 67\u003c\/p\u003e \u003cp\u003e3.4.6 Computing 68\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 68\u003c\/p\u003e \u003cp\u003eReferences 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Fundamentals of Particle Swarm Optimization Techniques 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYoshikazu Fukuyama\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e4.2 Basic Particle Swarm Optimization 72\u003c\/p\u003e \u003cp\u003e4.2.1 Background of Particle Swarm Optimization 72\u003c\/p\u003e \u003cp\u003e4.2.2 Original PSO 72\u003c\/p\u003e \u003cp\u003e4.3 Variations of Particle Swarm Optimization 76\u003c\/p\u003e \u003cp\u003e4.3.1 Discrete PSO 76\u003c\/p\u003e \u003cp\u003e4.3.2 PSO for MINLPs 77\u003c\/p\u003e \u003cp\u003e4.3.3 Constriction Factor Approach (CFA) 77\u003c\/p\u003e \u003cp\u003e4.3.4 Hybrid PSO (HPSO) 78\u003c\/p\u003e \u003cp\u003e4.3.5 Lbest Model 79\u003c\/p\u003e \u003cp\u003e4.3.6 Adaptive PSO (APSO) 79\u003c\/p\u003e \u003cp\u003e4.3.7 Evolutionary PSO (EPSO) 81\u003c\/p\u003e \u003cp\u003e4.4 Research Areas and Applications 82\u003c\/p\u003e \u003cp\u003e4.5 Conclusions 83\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fundamentals of Ant Colony Search Algorithms 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I. K. Yu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.2 Ant Colony Search Algorithm 90\u003c\/p\u003e \u003cp\u003e5.2.1 Behavior of Real Ants 90\u003c\/p\u003e \u003cp\u003e5.2.2 Ant Colony Algorithms 91\u003c\/p\u003e \u003cp\u003e5.2.3 Major Characteristics of Ant Colony Search Algorithms 98\u003c\/p\u003e \u003cp\u003e5.3 Conclusions 99\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fundamentals of Tabu Search 101\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlcir J. Monticelli, Rub\u003c\/i\u003e\u003ci\u003eén Romero, and Eduardo Nobuhiro Asada\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 101\u003c\/p\u003e \u003cp\u003e6.1.1 Overview of the Tabu Search Approach 101\u003c\/p\u003e \u003cp\u003e6.1.2 Problem Formulation 103\u003c\/p\u003e \u003cp\u003e6.1.3 Coding and Representation 104\u003c\/p\u003e \u003cp\u003e6.1.4 Neighborhood Structure 105\u003c\/p\u003e \u003cp\u003e6.1.5 Characterization of the Neighborhood 108\u003c\/p\u003e \u003cp\u003e6.2 Functions and Strategies in Tabu Search 110\u003c\/p\u003e \u003cp\u003e6.2.1 Recency-Based Tabu Search 110\u003c\/p\u003e \u003cp\u003e6.2.2 Basic Tabu Search Algorithm 112\u003c\/p\u003e \u003cp\u003e6.2.3 The Use of Long-Term Memory in Tabu Search 115\u003c\/p\u003e \u003cp\u003e6.3 Applications of Tabu Search 119\u003c\/p\u003e \u003cp\u003e6.4 Conclusions 120\u003c\/p\u003e \u003cp\u003eReferences 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Fundamentals of Simulated Annealing 123\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlcir J. Monticelli, Rub\u003c\/i\u003e\u003ci\u003eén Romero, and Eduardo Nobuhiro Asada\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 123\u003c\/p\u003e \u003cp\u003e7.2 Basic Principles 125\u003c\/p\u003e \u003cp\u003e7.2.1 Metropolis Algorithm 125\u003c\/p\u003e \u003cp\u003e7.2.2 Simulated Annealing Algorithm 126\u003c\/p\u003e \u003cp\u003e7.3 Cooling Schedule 127\u003c\/p\u003e \u003cp\u003e7.3.1 Determination of the Initial Temperature\u003ci\u003e T\u003csub\u003e0 \u003c\/sub\u003e\u003c\/i\u003e128\u003c\/p\u003e \u003cp\u003e7.3.2 Determination of\u003ci\u003e N\u003csub\u003ek \u003c\/sub\u003e\u003c\/i\u003e129\u003c\/p\u003e \u003cp\u003e7.3.3 Determination of Cooling Rate 130\u003c\/p\u003e \u003cp\u003e7.3.4 Stopping Criterion 130\u003c\/p\u003e \u003cp\u003e7.4 SA Algorithm for the Traveling Salesman Problem 131\u003c\/p\u003e \u003cp\u003e7.4.1 Problem Coding 131\u003c\/p\u003e \u003cp\u003e7.4.2 Evaluation of the Cost Function 132\u003c\/p\u003e \u003cp\u003e7.4.3 Cooling Schedule 133\u003c\/p\u003e \u003cp\u003e7.4.4 Comments on the Results for the TSP 134\u003c\/p\u003e \u003cp\u003e7.5 SA for Transmission Network Expansion Problem 134\u003c\/p\u003e \u003cp\u003e7.5.1 Problem Coding 136\u003c\/p\u003e \u003cp\u003e7.5.2 Determination of the Initial Solution 136\u003c\/p\u003e \u003cp\u003e7.5.3 Neighborhood Structure 138\u003c\/p\u003e \u003cp\u003e7.5.4 Variation of the Objective Function 139\u003c\/p\u003e \u003cp\u003e7.5.5 Cooling Schedule 140\u003c\/p\u003e \u003cp\u003e7.6 Parallel Simulated Annealing 140\u003c\/p\u003e \u003cp\u003e7.6.1 Division Algorithm 141\u003c\/p\u003e \u003cp\u003e7.6.2 Clustering Algorithm 142\u003c\/p\u003e \u003cp\u003e7.7 Applications of Simulated Annealing 143\u003c\/p\u003e \u003cp\u003e7.8 Conclusions 144\u003c\/p\u003e \u003cp\u003eReferences 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fuzzy Systems 147\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eGermano Lambert-Torres\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Motivation and Definitions 147\u003c\/p\u003e \u003cp\u003e8.1.1 Introduction 147\u003c\/p\u003e \u003cp\u003e8.1.2 Typical Actions in Fuzzy Systems 148\u003c\/p\u003e \u003cp\u003e8.2 Integration of Fuzzy Systems with Evolutionary Techniques 150\u003c\/p\u003e \u003cp\u003e8.2.1 Integration Types of Hybrid Systems 150\u003c\/p\u003e \u003cp\u003e8.2.2 Hybrid Systems in Evolutionary Techniques 151\u003c\/p\u003e \u003cp\u003e8.2.3 Evolutionary Algorithms and Fuzzy Logic 152\u003c\/p\u003e \u003cp\u003e8.3 An Illustrative Example of a Hybrid System 152\u003c\/p\u003e \u003cp\u003e8.3.1 Parking Conditions 153\u003c\/p\u003e \u003cp\u003e8.3.2 Creation of the Fuzzy Control 154\u003c\/p\u003e \u003cp\u003e8.3.3 First Simulations 156\u003c\/p\u003e \u003cp\u003e8.3.4 Problem Presentation 156\u003c\/p\u003e \u003cp\u003e8.3.5 Genetic Training Modulus Description 158\u003c\/p\u003e \u003cp\u003e8.3.6 The Option to Define the Starting Positions 158\u003c\/p\u003e \u003cp\u003e8.3.7 The Option Genetic Training 158\u003c\/p\u003e \u003cp\u003e8.3.8 Tests 163\u003c\/p\u003e \u003cp\u003e8.4 Conclusions 167\u003c\/p\u003e \u003cp\u003eReferences 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Differential Evolution, an Alternative Approach to Evolutionary Algorithm 171\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eKit Po Wong and ZhaoYang Dong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 171\u003c\/p\u003e \u003cp\u003e9.2 Evolutionary Algorithms 172\u003c\/p\u003e \u003cp\u003e9.2.1 Basic EAs 172\u003c\/p\u003e \u003cp\u003e9.2.2 Virtual Population-Based Acceleration Techniques 174\u003c\/p\u003e \u003cp\u003e9.3 Differential Evolution 176\u003c\/p\u003e \u003cp\u003e9.3.1 Function Optimization Formulation 176\u003c\/p\u003e \u003cp\u003e9.3.2 DE Fundamentals 177\u003c\/p\u003e \u003cp\u003e9.4 Key Operators for Differential Evolution 181\u003c\/p\u003e \u003cp\u003e9.4.1 Encoding 181\u003c\/p\u003e \u003cp\u003e9.4.2 Mutation 181\u003c\/p\u003e \u003cp\u003e9.4.3 Crossover 183\u003c\/p\u003e \u003cp\u003e9.4.4 Other Operators 183\u003c\/p\u003e \u003cp\u003e9.5 An Optimization Example 184\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 186\u003c\/p\u003e \u003cp\u003eAcknowledgments 186\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Pareto Multiobjective Optimization 189\u003c\/b\u003e\u003cbr\u003e \u003ci\u003ePatrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 189\u003c\/p\u003e \u003cp\u003e10.2 Basic Principles 190\u003c\/p\u003e \u003cp\u003e10.2.1 Generic Formulation of MO Problems 191\u003c\/p\u003e \u003cp\u003e10.2.2 Pareto Optimality Concepts 191\u003c\/p\u003e \u003cp\u003e10.2.3 Objectives of Multiobjective Optimization 193\u003c\/p\u003e \u003cp\u003e10.3 Solution Approaches 194\u003c\/p\u003e \u003cp\u003e10.3.1 Classic Methods 194\u003c\/p\u003e \u003cp\u003e10.3.2 Intelligent Methods 196\u003c\/p\u003e \u003cp\u003e10.4 Performance Analysis 202\u003c\/p\u003e \u003cp\u003e10.4.1 Objective of Performance Assessment 202\u003c\/p\u003e \u003cp\u003e10.4.2 Comparison Methodologies 203\u003c\/p\u003e \u003cp\u003e10.5 Conclusions 205\u003c\/p\u003e \u003cp\u003eAcknowledgments 205\u003c\/p\u003e \u003cp\u003eReferences 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Trust-Tech Paradigm for Computing High-Quality Optimal Solutions: Method and Theory 209\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eHsiao-Dong Chiang and Jaewook Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 209\u003c\/p\u003e \u003cp\u003e11.2 Problem Preliminaries 210\u003c\/p\u003e \u003cp\u003e11.3 A Trust-Tech Paradigm 213\u003c\/p\u003e \u003cp\u003e11.3.1 Phase I 213\u003c\/p\u003e \u003cp\u003e11.3.2 Phase II 214\u003c\/p\u003e \u003cp\u003e11.4 Theoretical Analysis of Trust-Tech Method 218\u003c\/p\u003e \u003cp\u003e11.5 A Numerical Trust-Tech Method 221\u003c\/p\u003e \u003cp\u003e11.5.1 Computing Another Local Optimal Solution 222\u003c\/p\u003e \u003cp\u003e11.5.2 Computing Tier-One Local Optimal Solutions 223\u003c\/p\u003e \u003cp\u003e11.5.3 Computing Tier-N Solutions 224\u003c\/p\u003e \u003cp\u003e11.6 Hybrid Trust-Tech Methods 225\u003c\/p\u003e \u003cp\u003e11.7 Numerical Schemes 227\u003c\/p\u003e \u003cp\u003e11.8 Numerical Studies 228\u003c\/p\u003e \u003cp\u003e11.9 Conclusions Remarks 231\u003c\/p\u003e \u003cp\u003eReferences 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Selected Applications of Modern Heuristic Optimization In Power Systems 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Overview of Applications in Power Systems 237\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAlexandre P. Alves da Silva, Djalma M. Falc\u003c\/i\u003e\u003ci\u003eão, and Kwang Y. Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 237\u003c\/p\u003e \u003cp\u003e12.2 Optimization 237\u003c\/p\u003e \u003cp\u003e12.3 Power System Applications 238\u003c\/p\u003e \u003cp\u003e12.4 Model Identification 239\u003c\/p\u003e \u003cp\u003e12.4.1 Dynamic Load Modeling 239\u003c\/p\u003e \u003cp\u003e12.4.2 Short-Term Load Forecasting 240\u003c\/p\u003e \u003cp\u003e12.4.3 Neural Network Training 241\u003c\/p\u003e \u003cp\u003e12.5 Control 242\u003c\/p\u003e \u003cp\u003e12.5.1 Examples 243\u003c\/p\u003e \u003cp\u003e12.6 Distribution System Applications 244\u003c\/p\u003e \u003cp\u003e12.6.1 Network Reconfiguration for Loss Reduction 245\u003c\/p\u003e \u003cp\u003e12.6.2 Optimal Protection and Switching Devices Placement 246\u003c\/p\u003e \u003cp\u003e12.6.3 Prioritizing Investments in Distribution Networks 247\u003c\/p\u003e \u003cp\u003e12.7 Conclusions 249\u003c\/p\u003e \u003cp\u003eReferences 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Application of Evolutionary Technique to Power System Vulnerability Assessment 261\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMingoo Kim, Mohamed A. El-Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 261\u003c\/p\u003e \u003cp\u003e13.2 Vulnerability Assessment and Control 263\u003c\/p\u003e \u003cp\u003e13.3 Vulnerability Assessment Challenges 264\u003c\/p\u003e \u003cp\u003e13.3.1 Complexity of Power System 264\u003c\/p\u003e \u003cp\u003e13.3.2 VA On-line Speed 265\u003c\/p\u003e \u003cp\u003e13.3.3 Feature Selection 265\u003c\/p\u003e \u003cp\u003e13.3.4 Vulnerability Border 270\u003c\/p\u003e \u003cp\u003e13.3.5 Selection of Vulnerability Index 276\u003c\/p\u003e \u003cp\u003e13.4 Conclusions 281\u003c\/p\u003e \u003cp\u003eReferences 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Applications to System Planning 285\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong-Bae Park, Rub\u003c\/i\u003eé\u003ci\u003en Romero, and Yong-Hua Song\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 285\u003c\/p\u003e \u003cp\u003e14.2 Generation Expansion 286\u003c\/p\u003e \u003cp\u003e14.2.1 A Coding Strategy for an Improved GA for the Least-Cost GEP 288\u003c\/p\u003e \u003cp\u003e14.2.2 Fitness Function 288\u003c\/p\u003e \u003cp\u003e14.2.3 Creation of an Artificial Initial Population 289\u003c\/p\u003e \u003cp\u003e14.2.4 Stochastic Crossover Elitism and Mutation 291\u003c\/p\u003e \u003cp\u003e14.2.5 Numerical Examples 292\u003c\/p\u003e \u003cp\u003e14.2.6 Parameters for GEP and IGA 293\u003c\/p\u003e \u003cp\u003e14.2.7 Numerical Results 295\u003c\/p\u003e \u003cp\u003e14.3 Transmission Network Expansion 297\u003c\/p\u003e \u003cp\u003e14.3.1 Overview of Static Transmission Network Planning 297\u003c\/p\u003e \u003cp\u003e14.3.2 Solution Techniques for the Transmission Expansion Planning Problem 300\u003c\/p\u003e \u003cp\u003e14.3.3 Coding, Problem Representation, and Test Systems 302\u003c\/p\u003e \u003cp\u003e14.3.4 Complexity of the Test Systems 304\u003c\/p\u003e \u003cp\u003e14.3.5 Simulated Annealing 306\u003c\/p\u003e \u003cp\u003e14.3.6 Genetic Algorithms in Transmission Network Expansion Planning 307\u003c\/p\u003e \u003cp\u003e14.3.7 Tabu Search in Transmission Network Expansion Planning 309\u003c\/p\u003e \u003cp\u003e14.3.8 Hybrid TS\/GA\/SA Algorithm in Transmission Network Expansion Planning 310\u003c\/p\u003e \u003cp\u003e14.3.9 Comments on the Performance of Meta-heuristic Methods in Transmission Network Expansion Planning 311\u003c\/p\u003e \u003cp\u003e14.4 Distribution Network Expansion 311\u003c\/p\u003e \u003cp\u003e14.4.1 Dynamic Planning of Distribution System Expansion: A Complete GA Model 312\u003c\/p\u003e \u003cp\u003e14.4.2 Dynamic Planning of Distribution System Expansion: An Efficient GA Application 316\u003c\/p\u003e \u003cp\u003e14.4.3 Application of TS to the Design of Distribution Networks in FRIENDS 317\u003c\/p\u003e \u003cp\u003e14.5 Reactive Power Planning at Generation–Transmission Level 320\u003c\/p\u003e \u003cp\u003e14.5.1 Benders Decomposition of the Reactive Power Planning Problem 321\u003c\/p\u003e \u003cp\u003e14.5.2 Solution Algorithm 323\u003c\/p\u003e \u003cp\u003e14.5.3 Results for the IEEE 30-Bus System 324\u003c\/p\u003e \u003cp\u003e14.6 Reactive Power Planning at Distribution Level 326\u003c\/p\u003e \u003cp\u003e14.6.1 Modeling Chromosome Repair Using an Analytical Model 326\u003c\/p\u003e \u003cp\u003e14.6.2 Evolutionary Programming\/Evolution Strategies Under Test 327\u003c\/p\u003e \u003cp\u003e14.7 Conclusions 330\u003c\/p\u003e \u003cp\u003eReferences 330\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Applications to Power System Scheduling 337\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eKoay 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 337\u003c\/p\u003e \u003cp\u003e15.2 Economic Dispatch 337\u003c\/p\u003e \u003cp\u003e15.2.1 Economic Dispatch Problem 337\u003c\/p\u003e \u003cp\u003e15.2.2 GA Implementation to ED 339\u003c\/p\u003e \u003cp\u003e15.2.3 PSO Implementation to ED 346\u003c\/p\u003e \u003cp\u003e15.2.4 Numerical Example 348\u003c\/p\u003e \u003cp\u003e15.2.5 Summary 354\u003c\/p\u003e \u003cp\u003e15.3 Maintenance Scheduling 354\u003c\/p\u003e \u003cp\u003e15.3.1 Maintenance Scheduling Problem 354\u003c\/p\u003e \u003cp\u003e15.3.2 GA, PSO, and ES Implementation 355\u003c\/p\u003e \u003cp\u003e15.3.3 Simulation Results 365\u003c\/p\u003e \u003cp\u003e15.3.4 Summary 366\u003c\/p\u003e \u003cp\u003e15.4 Cogeneration Scheduling 366\u003c\/p\u003e \u003cp\u003e15.4.1 Cogeneration Scheduling Problem 367\u003c\/p\u003e \u003cp\u003e15.4.2 IGA Implementation 370\u003c\/p\u003e \u003cp\u003e15.4.3 Case Study 373\u003c\/p\u003e \u003cp\u003e15.4.4 Summary 374\u003c\/p\u003e \u003cp\u003e15.4.5 Nomenclature 379\u003c\/p\u003e \u003cp\u003e15.5 Short-Term Generation Scheduling of Thermal Units 380\u003c\/p\u003e \u003cp\u003e15.5.1 Short-Term Generation Scheduling Problem 380\u003c\/p\u003e \u003cp\u003e15.5.2 ACSA Implementation 382\u003c\/p\u003e \u003cp\u003e15.5.3 Experimental results 385\u003c\/p\u003e \u003cp\u003e15.6 Constrained Load Flow Problem 385\u003c\/p\u003e \u003cp\u003e15.6.1 Constrained Load Flow Problem 385\u003c\/p\u003e \u003cp\u003e15.6.2 Heuristic Ant Colony Search Algorithm Implementation 386\u003c\/p\u003e \u003cp\u003e15.6.3 Test Examples 390\u003c\/p\u003e \u003cp\u003e15.6.4 Summary 399\u003c\/p\u003e \u003cp\u003eReferences 399\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Power System Controls 403\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen-Ching Liu, Yong-Hua Song, and Ying Xiao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 403\u003c\/p\u003e \u003cp\u003e16.2 Power System Controls: Particle Swarm Technique 404\u003c\/p\u003e \u003cp\u003e16.2.1 Problem Formulation of VVC 405\u003c\/p\u003e \u003cp\u003e16.2.2 Expansion of PSO for MINLP 406\u003c\/p\u003e \u003cp\u003e16.2.3 Voltage Security Assessment 407\u003c\/p\u003e \u003cp\u003e16.2.4 VVC Using PSO 408\u003c\/p\u003e \u003cp\u003e16.2.5 Numerical Examples 409\u003c\/p\u003e \u003cp\u003e16.2.6 Summary 416\u003c\/p\u003e \u003cp\u003e16.3 Power Plant Controller Design with GA 417\u003c\/p\u003e \u003cp\u003e16.3.1 Overview of the GA 417\u003c\/p\u003e \u003cp\u003e16.3.2 The Boiler-Turbine Model 419\u003c\/p\u003e \u003cp\u003e16.3.3 The GA Control System Design 420\u003c\/p\u003e \u003cp\u003e16.3.4 GA Design Results 423\u003c\/p\u003e \u003cp\u003e16.4 Evolutionary Programming Optimizer and Application in Intelligent Predictive Control 427\u003c\/p\u003e \u003cp\u003e16.4.1 Structure of the Intelligent Predictive Controller 428\u003c\/p\u003e \u003cp\u003e16.4.2 Power Plant Model 430\u003c\/p\u003e \u003cp\u003e16.4.3 Control Input Optimization 431\u003c\/p\u003e \u003cp\u003e16.4.4 Self-Organized Neuro-Fuzzy Identifier 435\u003c\/p\u003e \u003cp\u003e16.4.5 Rule Generation and Tuning 438\u003c\/p\u003e \u003cp\u003e16.4.6 Controller Implementation 442\u003c\/p\u003e \u003cp\u003e16.4.7 Summary 444\u003c\/p\u003e \u003cp\u003e16.5 An Interactive Compromise Programming-Based MO Approach to FACTS Control 444\u003c\/p\u003e \u003cp\u003e16.5.1 Review of MO Optimization Techniques 446\u003c\/p\u003e \u003cp\u003e16.5.2 Formulated MO Optimization Model 449\u003c\/p\u003e \u003cp\u003e16.5.3 Power Flow Control Model of FACTS Devices 450\u003c\/p\u003e \u003cp\u003e16.5.4 Proposed Interactive DWCP Method 453\u003c\/p\u003e \u003cp\u003e16.5.5 Proposed Interactive Procedure with Worst Compromise Displacement 455\u003c\/p\u003e \u003cp\u003e16.5.6 Implementation 457\u003c\/p\u003e \u003cp\u003e16.5.7 Numerical Results 457\u003c\/p\u003e \u003cp\u003e16.5.8 Summary 462\u003c\/p\u003e \u003cp\u003eReferences 464\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Genetic Algorithms for Solving Optimal Power Flow Problems 471\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLoi Lei Lai and Nidul Sinha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 471\u003c\/p\u003e \u003cp\u003e17.2 Genetic Algorithms 473\u003c\/p\u003e \u003cp\u003e17.2.1 Terms Used in GA 473\u003c\/p\u003e \u003cp\u003e17.3 Load Flow Problem 478\u003c\/p\u003e \u003cp\u003e17.4 Optimal Power Flow Problem 483\u003c\/p\u003e \u003cp\u003e17.4.1 Application Examples 485\u003c\/p\u003e \u003cp\u003e17.5 OPF with FACTS Devices 488\u003c\/p\u003e \u003cp\u003e17.5.1 FACTS Model 492\u003c\/p\u003e \u003cp\u003e17.5.2 Problem Formulation 495\u003c\/p\u003e \u003cp\u003e17.5.3 Numerical Results 496\u003c\/p\u003e \u003cp\u003e17.6 Conclusions 499\u003c\/p\u003e \u003cp\u003eReferences 499\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 An Interactive Compromise Programming-Based Multiobjective Approach to FACTS Control 501\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYing Xiao, Yong-Hua Song, and Chen-Ching Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 501\u003c\/p\u003e \u003cp\u003e18.2 Review of Multiobjective Optimization Techniques 503\u003c\/p\u003e \u003cp\u003e18.2.1 Weighting Method 503\u003c\/p\u003e \u003cp\u003e18.2.2 Goal Programming 504\u003c\/p\u003e \u003cp\u003e18.2.3 1-Constraint Method 504\u003c\/p\u003e \u003cp\u003e18.2.4 Compromise Programming 504\u003c\/p\u003e \u003cp\u003e18.2.5 Fuzzy Set Theory Applications 505\u003c\/p\u003e \u003cp\u003e18.2.6 Genetic Algorithm 505\u003c\/p\u003e \u003cp\u003e18.2.7 Interactive Procedure 506\u003c\/p\u003e \u003cp\u003e18.3 Formulated MO Optimization Model 506\u003c\/p\u003e \u003cp\u003e18.3.1 Formulated MO Optimization Model for FACTS Control 507\u003c\/p\u003e \u003cp\u003e18.3.2 Power Flow Control Model of FACTS Devices 508\u003c\/p\u003e \u003cp\u003e18.4 Proposed Interactive Displaced Worst Compromise Programming Method 511\u003c\/p\u003e \u003cp\u003e18.4.1 Applied Fuzzy CP 511\u003c\/p\u003e \u003cp\u003e18.4.2 Operation Cost Minimization 512\u003c\/p\u003e \u003cp\u003e18.4.3 Local Power Flow Control 512\u003c\/p\u003e \u003cp\u003e18.5 Proposed Interactive Procedure with WC Displacement 513\u003c\/p\u003e \u003cp\u003e18.5.1 Phase 1: Model Formulation 513\u003c\/p\u003e \u003cp\u003e18.5.2 Phase 2: Noninferior Solution Calculation 514\u003c\/p\u003e \u003cp\u003e18.5.3 Phase 3: Scenario Evaluation 514\u003c\/p\u003e \u003cp\u003e18.6 Implementation 516\u003c\/p\u003e \u003cp\u003e18.7 Numerical Results 516\u003c\/p\u003e \u003cp\u003e18.8 Conclusions 521\u003c\/p\u003e \u003cp\u003eReferences 521\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Hybrid Systems 525\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eVladimiro Miranda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 525\u003c\/p\u003e \u003cp\u003e19.2 Capacitor Sizing and Location and Analytical Sensitivities 527\u003c\/p\u003e \u003cp\u003e19.2.1 From Darwin to Lamarck: Three Models 528\u003c\/p\u003e \u003cp\u003e19.2.2 Building a Lamarckian Acquisition of Improvements 529\u003c\/p\u003e \u003cp\u003e19.2.3 Analysis of a Didactic Example 531\u003c\/p\u003e \u003cp\u003e19.3 Unit Commitment Fuzzy Sets and Cleverer Chromosomes 538\u003c\/p\u003e \u003cp\u003e19.3.1 The Deceptive Characteristics of Unit Commitment Problems 538\u003c\/p\u003e \u003cp\u003e19.3.2 Similarity Between the Capacitor Placement and the Unit Commitment Problems 539\u003c\/p\u003e \u003cp\u003e19.3.3 The Need for Cleverer Chromosomes 540\u003c\/p\u003e \u003cp\u003e19.3.4 A Biological Touch: The Chromosome as a Program 541\u003c\/p\u003e \u003cp\u003e19.3.5 A Real-World Example: The CARE Model in Crete Greece 542\u003c\/p\u003e \u003cp\u003e19.3.6 Fitness Evaluation: Reliability (Spinning Reserve as a Fuzzy Constraint) 547\u003c\/p\u003e \u003cp\u003e19.3.7 Illustrative Results 547\u003c\/p\u003e \u003cp\u003e19.4 Voltage\/Var Control and Loss Reduction in Distribution Networks with an Evolutionary Self-Adaptive Particle Swarm Optimization Algorithm: EPSO 550\u003c\/p\u003e \u003cp\u003e19.4.1 Justifying a Hybrid Approach 550\u003c\/p\u003e \u003cp\u003e19.4.2 The Principles of EPSO: Reproduction and Movement Rule 551\u003c\/p\u003e \u003cp\u003e19.4.3 Mutating Strategic Parameters 552\u003c\/p\u003e \u003cp\u003e19.4.4 The Merits of EPSO 553\u003c\/p\u003e \u003cp\u003e19.4.5 Experiencing with EPSO: Basic EPSO Model 554\u003c\/p\u003e \u003cp\u003e19.4.6 EPSO in Test Functions 554\u003c\/p\u003e \u003cp\u003e19.4.7 EPSO in Loss Reduction and Voltage\/VAR Control: Definition of the Problem 557\u003c\/p\u003e \u003cp\u003e19.4.8 Applying EPSO in the Management of Networks with Distributed Generation 558\u003c\/p\u003e \u003cp\u003e19.5 Conclusions 559\u003c\/p\u003e \u003cp\u003eReferences 560\u003c\/p\u003e \u003cp\u003eIndex 563\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand 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