Nature Inspired Optimization for Electrical Power System (eBook)

Preface 6
Synopsis 9
Contents 10
About the Editors 12
1 Teaching-Learning-Based Optimization for Static and Dynamic Load Dispatch 14
1 Introduction 14
2 Problem Statement 16
3 Teaching–Learning-Based Optimization 17
4 Description of Problems and Simulation Results 18
5 Conclusion 24
References 24
2 Application of Elitist Teacher–Learner-Based Optimization Algorithm for Congestion Management 26
1 Introduction 27
2 Problem Formulation 28
2.1 Equality Constraints 28
2.2 Inequality Constraints 29
2.3 Fitness Function 29
3 Frame of Elitist Teacher–Learner-Based Optimization (ETLBO) 30
3.1 Teacher Phase 30
3.2 Learner Phase 31
3.3 Elitism 32
4 Elitist TLBO for Congestion Management 32
4.1 About Test Systems 32
4.2 Line Outage Contingency: Case I 32
4.3 Sudden Increment in Demand with Single Line Outage: Case II 33
4.4 Abrupt Line Power Limits Variation: Case III and IV 33
4.5 Generation Rescheduling for CM 33
4.6 ETLBO for Solution of CM Problem: Mathematical Procedure 34
5 Numerical Results and Analysis 34
5.1 Convergence Analysis of ETLBO 38
6 Conclusions 39
References 41
3 PSO-Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System 43
1 Introduction 44
2 Problem Formulation 45
3 Optimization of LCOE 46
3.1 Power Generation Equality/Inequality Constraint 46
4 Results and Discussion 47
4.1 Test Case Description 47
4.2 Optimization of LCOE 47
4.3 Effect of Capacity Factor on Optimal Value of LCOE 48
4.4 Convergence Characteristics of the Solver 48
4.5 Validation of Results Using Particle Swarm Optimization 49
5 Conclusion 51
References 54
4 PSO-Based PID Controller Designing for LFC of Single Area Electrical Power Network 55
1 Introduction 55
2 Problem Formulation 57
2.1 System Description 57
2.2 A Brief Introduction of PID Controller 58
2.3 Objective Function Formulation 58
3 Employed Optimization Techniques 59
3.1 GA 59
3.2 PSO 59
4 Results and Discussions 59
4.1 Case 1: Objective Function—IAE 61
4.2 Case 2: Objective Function—ISE 62
4.3 Case 3: Objective Function-ITAE 63
4.4 Case 4: Objective Function-ITSE 64
5 Conclusion 65
References 66
5 Combined Economic Emission Dispatch of Hybrid Thermal PV System Using Artificial Bee Colony Optimization 67
1 Introduction 68
2 Problem Formulation 69
2.1 Objective Function 69
2.2 Equality Constraint 70
2.3 Inequality Constraint 70
3 Artificial Bee Colony Optimization 71
4 Results and Discussion 72
4.1 Description of Test Cases 72
4.2 Simulation Results 74
5 Conclusion 78
References 79
6 Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization 80
1 Introduction 81
2 Problem Formulation 82
2.1 Inequality Constraints 83
2.2 Equality Constraints 84
3 Grey Wolf Optimization 84
4 Results and Discussion 86
4.1 Description of Test Cases 86
4.2 Simulation Results 87
5 Conclusion 90
References 92
7 Mixed-Integer Differential Evolution Algorithm for Optimal Static/Dynamic Scheduling of a Microgrid with Mixed Generation 94
1 Introduction 94
2 Problem Formulation for Microgrid with Mixed Generation 95
2.1 Generating Unit Limits 96
2.2 Supply and Load Balance Constraint 96
2.3 Generator Ramp Rate Limits 96
2.4 Formulation of Total Cost Function for the Wind–PV–Diesel Microgrid 97
2.5 SO and Two-Objective Optimization Functions 98
3 Mixed-Integer Differential Evolution (MIDE) 99
4 Results and Discussion 100
4.1 Description of the Modified Microgrid Test System 100
4.2 Setting of the Optimal Parameters of MIDE 101
4.3 SO Optimal Static Scheduling of Microgrid Using MIDE 101
4.4 SO Optimal Dynamic Scheduling of Wind–PV–Diesel Microgrid 103
4.5 Two-Objective Dynamic Optimal Scheduling of Wind–PV–Diesel Microgrid 105
5 Comparison and Validation of Results 108
6 Conclusion 109
References 109
8 NSGA-II Based Reactive Power Management in Radial Distribution System Integrated with DGs 111
1 Introduction 111
2 Multi-Objective Reactive Power Management 113
2.1 Objective Functions of RPM Problem 113
3 Non-dominated Sorting Genetic Algorithm-II for MORPM 115
4 Results and Discussion 116
4.1 Case1: Minimization of PL and TVV 118
4.2 Case 2: Minimization of PL and TCRPS 119
4.3 Case 3: Minimization of PL, TVV, and TCRPS 120
5 Conclusion 121
References 122
9 Short-Term Hydrothermal Scheduling Using Bio-inspired Computing: A Review 124
1 Introduction 125
2 Formulation of SHTS Problem 126
2.1 Objective Function 126
2.2 Operational Constraints 127
3 Bio-Inspired Algorithm and Their Application 128
3.1 Genetic Algorithm (GA) 128
3.2 Particle Swarm Optimization (PSO) 129
3.3 Differential Evolution (DE) 129
3.4 Evolutionary Programming (EP) 132
3.5 Artificial Bee Colony (ABC) Algorithm 132
3.6 Gravitational Search Algorithm (GSA) 133
3.7 Cuckoo Search Algorithm (CSA) 134
3.8 Teaching-Learning-Based Optimization (TLBO) 134
3.9 Flower Pollination Algorithm (FPA) 135
4 Conclusion 135
References 136