Nature Inspired Optimization for Electrical Power System (eBook)

Manjaree Pandit, Hari Mohan Dubey, Jagdish Chand Bansal (Herausgeber)

eBook Download: PDF

2020 | 1st ed. 2020
XIV, 129 Seiten
Springer Singapore (Verlag)
978-981-15-4004-2 (ISBN)

Infos

This book presents a wide range of optimization methods and their applications to various electrical power system problems such as economical load dispatch, demand supply management in microgrids, levelized energy pricing, load frequency control and congestion management, and reactive power management in radial distribution systems. Problems related to electrical power systems are often highly complex due to the massive dimensions, nonlinearity, non-convexity and discontinuity associated with objective functions. These systems also have a large number of equality and inequality constraints, which give rise to optimization problems that are difficult to solve using classical numerical methods. In this regard, nature inspired optimization algorithms offer an effective alternative, due to their ease of use, population-based parallel search mechanism, non-dependence on the nature of the problem, and ability to accommodate non-differentiable, non-convex problems. The analytical model of nature inspired techniques mimics the natural behaviors and intelligence of life forms. These techniques are mainly based on evolution, swarm intelligence, ecology, human intelligence and physical science.

 

Prof. Manjaree Pandit received her M.Tech. degree in Electrical Engineering from Maulana Azad College of Technology, Bhopal, India, in 1989 and her Ph.D. degree from Jiwaji University Gwalior, India, in 2001. She is currently working as a Professor and Dean of Academics at the Department of Electrical Engineering, M.I.T.S., Gwalior, India. She is a senior member of the IEEE, a reviewer for several journals, and has published more than 60 papers in respected international journals. Her research interests include the integration of hybrid renewable energy sources with power grids, nature inspired algorithms, ANN and fuzzy neural network applications to electrical power systems.

Dr. Hari Mohan Dubey is an Associate Professor at Madhav Institute of Technology & Science, Gwalior, India. Dr. Dubey received his Ph.D. degree in Electrical Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He is associated with various SCI journals as reviewer, and has published more than 70 research papers in various international journals/conference proceedings. His main research interests are in bio-inspired algorithms and their applications to electrical engineering, particularly, power system planning and operation with the integration of renewable energy sources.

Dr. Jagdish Chand Bansal is an Associate Professor at South Asian University New Delhi and Visiting Faculty at the Department of Maths and Computer Science, Liverpool Hope University, UK. Dr. Bansal received his Ph.D. in Mathematics from the IIT Roorkee. Before joining SAU New Delhi, he worked as an Assistant Professor at ABV-Indian Institute of Information Technology and Management, Gwalior, and at BITS Pilani. He is the series editor of Algorithms for Intelligent Systems (AIS), published by Springer; the Editor-in-Chief of International Journal of Swarm Intelligence (IJSI), published by Inderscience; and an Associate Editor of IEEE ACCESS, published by the IEEE. He is the general secretary of the Soft Computing Research Society (SCRS). His main research interests are in swarm intelligence and nature inspired optimization techniques. Recently, he proposed a fission-fusion social structure-based optimization algorithm, Spider Monkey Optimization (SMO), which is currently being applied to various problems in the engineering domain. He has published more than 60 research papers in various international journals/conference proceedings.

 

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 7.4.2020
• Reihe/Serie: Algorithms for Intelligent Systems
• Reihe/Serie: Algorithms for Intelligent Systems
• Zusatzinfo: XIV, 129 p. 49 illus., 35 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Competitive Electricity Market • Economic Load Dispatch • Hydrothermal scheduling • Levelized Cost Of Energy • Load Frequency Control • Microgrid • Nature inspired Optimization • Reactive Power Management • Renewable Energy Resources
• ISBN-10: 981-15-4004-7 / 9811540047
• ISBN-13: 978-981-15-4004-2 / 9789811540042