Modern Optimization Techniques for Smart Grids

Modern Optimization Techniques for Smart Grids presents current research and methods for monitoring transmission systems and enhancing distribution system performance using optimization techniques considering the role of different single and multi-objective functions. The authors present in-depth information on integrated systems for smart transmission and distribution, including using smart meters such as phasor measurement units (PMUs), enhancing distribution system performance using the optimal placement of distributed generations (DGs) and/or capacitor banks, and optimal capacitor placement for power loss reduction and voltage profile improvement. The book will be a valuable reference for researchers, students, and engineers working in electrical power engineering and renewable energy systems. Predicts future development of hybrid power systems; Introduces enhanced optimization strategies; Includes MATLAB M-file codes. Preface 6 Contents 8 List of Symbols 12 Abbreviations 14 Chapter 1: Introduction 17 1.1 General 17 1.2 Book Contributions 24 1.3 Scope of the Book 24 Chapter 2: Optimization Techniques 26 2.1 Introduction 26 2.2 Conventional Optimization Techniques 29 2.2.1 Linear Programming (LP) 29 2.2.2 Quadratic Programming (QP) 30 2.2.3 Integer Programming (IP) 31 2.2.4 Dynamic Programming (DP) 32 2.3 Artificial Intelligence (AI) Techniques 32 2.3.1 Artificial Neural Network (ANN) 32 2.3.2 Fuzzy Linear Programming (FLP) 33 2.3.3 Expert Systems (ES) 37 2.4 Modern Optimization Methods 37 2.5 Evolutionary Optimization Techniques 38 2.5.1 Genetic Algorithm (GA) 38 2.6 Differential Evolution (DE) Algorithm 39 2.6.1 Standard DE Algorithm 39 2.7 Particle Swarm Optimization (PSO) Technique 40 2.7.1 PSO Mathematical Model 40 2.8 Seeker Optimization Algorithm (SOA) 43 2.8.1 SOA 43 2.9 Ant Colony Optimization (ACO) Algorithm 45 2.9.1 Description of Real Ants 47 2.9.2 Comparison Between Artificial and Real Ant Systems 48 2.9.3 ACO Mathematical Model 49 2.9.4 ACO Algorithm 49 2.10 Conclusion 52 Chapter 3: Smart Grid Technologies 54 3.1 Introduction 54 3.2 Smart Grid (SG) 55 3.2.1 Definitions of SG 55 3.2.2 Traditional Grid Versus SG 56 3.2.3 Benefits of SG 58 3.3 Smart Metering 59 3.3.1 Evolution of Electricity Metering 59 3.3.2 Comparison Between Conventional and Smart Metering 60 3.3.3 Examples of Smart Metering 61 3.3.3.1 Phasor Measurement Units (PMUs) 61 3.3.3.2 Supervisory Control and Data Acquisition (SCADA) System 61 3.3.3.3 The Comparison Between SCADA System and PMU System 62 3.4 Phasor Measurement Units (PMUs) 62 3.4.1 PMU Hardware Components 62 3.4.2 Applications of PMUs in Power Systems 64 3.5 Observability Analysis 64 3.6 Capacitor Banks 65 3.6.1 Fixed Versus Switched Capacitor Banks 66 3.6.2 Benefits of Capacitor Banks 66 3.7 Distributed Generations (DGs) 67 3.7.1 Definition of DG 67 3.7.2 Types of DGs 68 3.7.3 Applications of DGs 69 3.8 Conclusion 70 Chapter 4: Optimal Placement of PMUs in Smart Power Systems 71 4.1 Introduction 71 4.2 Rules of Observability Based on PMUs 76 4.3 Problem Formulation 76 4.3.1 Formulation of Optimal PMU Placement Problem 76 4.3.2 Installation Cost of PMUs 77 4.4 Optimal Solution Using Proposed Multistage Method 78 4.4.1 Optimal PMU Placement Using ACO Algorithm 78 4.4.2 Proposed Reduction Strategy (RS) Rules for PMU Channels 81 4.4.3 Numerical Example 83 4.4.3.1 Without Considering ZIBs 83 4.4.3.2 Considering ZIBs 90 4.5 Applications 96 4.5.1 Test Systems 96 4.5.2 Results and Comments 98 4.5.2.1 Without Considering ZIBs 98 4.5.2.2 Considering ZIBs 110 4.6 Conclusion 120 Chapter 5: Optimal Capacitor Placement for Power Loss Reduction and Voltage Profile Improvement 121 5.1 Introduction 121 5.2 Problem Formulation 124 5.3 Sensitivity Analysis and Loss Sensitivity Indices 126 5.4 Optimal Capacitor Placement Using ACO Algorithm 131 5.5 Applications 134 5.5.1 Test Systems 134 5.5.2 Results and Comments 135 5.5.2.1 10-Bus System 135 5.5.2.2 34-Bus System 138 5.5.2.3 85-Bus System 144 5.5.2.4 EDN System 149 5.6 Conclusion 149 Chapter 6: Optimal Combination of DGs and Capacitor Banks for Performance Enhancement of Distribution Systems 154 6.1 Introduction 154 6.2 Problem Formulation 157 6.2.1 Objective Functions 157 6.2.1.1 Objective 1: Minimization of Total Power Loss 157 6.2.1.2 Objective 2: Minimization of Voltage Deviation 158 6.2.1.3 Objective 3: Maximization of the Voltage Stability Index 158 6.2.2 Multi-objective Function 160 6.2.3 System Constraints 161 6.2.3.1 Equality Constraint 161 6.2.3.2 Inequality Constraints 161 6.3 Two Loss Sensitivity Indices 163 6.4 Placement of Optimal DGs and Capacitor Banks Using ACO Algorithm 164 6.5 Applications 167 6.5.1 Test Systems 167 6.5.2 Results and Comments 168 6.5.2.1 Results of Loss Sensitivity Indices 169 6.5.2.2 Results of Minimizing Power Loss 169 6.5.2.3 Results of Minimizing Voltage Deviation 176 6.5.2.4 Results of Minimizing the Inverse of Total VSI 180 6.5.2.5 Results of Multi-objective Function 184 6.5.2.6 Evaluation of the Results of Objective Functions 185 6.6 Conclusion 188 Chapter 7: Conclusions 190 7.1 Future Work 192 Appendix A. Test Systems 193 A.1. Power Systems 193 A.1.1. IEEE 14-Bus Test System 193 A.1.2. IEEE 24-Bus Test System 193 A.1.3. IEEE 30-Bus Test System 194 A.1.4. New England (NE) 39-Bus Test System 194 A.1.5. IEEE 57-Bus Test System 194 A.1.6. IEEE 118-Bus Test System 195 A.1.7. WDN 52-Bus System 195 A.2. Distribution Systems 198 A.2.1. 10-Bus System 198 A.2.2. 34-Bus System 200 A.2.3. 85-Bus System 202 A.2.4. East Delta Network (EDN) System 202 Appendix B. Backward/Forward Sweep (BFS) Algorithm 220 References 226 Index 234