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Nature Inspired Optimization for Electrical Power System

E-BookPDF1 - PDF WatermarkE-Book
129 Seiten
Englisch
Springer Nature Singaporeerschienen am07.04.20201st ed. 2020
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.

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Produkt

KlappentextThis 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.

Details
Weitere ISBN/GTIN9789811540042
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2020
Erscheinungsdatum07.04.2020
Auflage1st ed. 2020
ReiheAlgorithms for Intelligent Systems
Seiten129 Seiten
SpracheEnglisch
IllustrationenXIV, 129 p. 49 illus., 35 illus. in color.
Artikel-Nr.5144053
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Preface;6
2;Synopsis;9
3;Contents;10
4;About the Editors;12
5;1 Teaching-Learning-Based Optimization for Static and Dynamic Load Dispatch;14
5.1;1 Introduction;14
5.2;2 Problem Statement;16
5.3;3 Teaching-Learning-Based Optimization;17
5.4;4 Description of Problems and Simulation Results;18
5.5;5 Conclusion;24
5.6;References;24
6;2 Application of Elitist Teacher-Learner-Based Optimization Algorithm for Congestion Management;26
6.1;1 Introduction;27
6.2;2 Problem Formulation;28
6.2.1;2.1 Equality Constraints;28
6.2.2;2.2 Inequality Constraints;29
6.2.3;2.3 Fitness Function;29
6.3;3 Frame of Elitist Teacher-Learner-Based Optimization (ETLBO);30
6.3.1;3.1 Teacher Phase;30
6.3.2;3.2 Learner Phase;31
6.3.3;3.3 Elitism;32
6.4;4 Elitist TLBO for Congestion Management;32
6.4.1;4.1 About Test Systems;32
6.4.2;4.2 Line Outage Contingency: Case I;32
6.4.3;4.3 Sudden Increment in Demand with Single Line Outage: Case II;33
6.4.4;4.4 Abrupt Line Power Limits Variation: Case III and IV;33
6.4.5;4.5 Generation Rescheduling for CM;33
6.4.6;4.6 ETLBO for Solution of CM Problem: Mathematical Procedure;34
6.5;5 Numerical Results and Analysis;34
6.5.1;5.1 Convergence Analysis of ETLBO;38
6.6;6 Conclusions;39
6.7;References;41
7;3 PSO-Based Optimization of Levelized Cost of Energy for Hybrid Renewable Energy System;43
7.1;1 Introduction;44
7.2;2 Problem Formulation;45
7.3;3 Optimization of LCOE;46
7.3.1;3.1 Power Generation Equality/Inequality Constraint;46
7.4;4 Results and Discussion;47
7.4.1;4.1 Test Case Description;47
7.4.2;4.2 Optimization of LCOE;47
7.4.3;4.3 Effect of Capacity Factor on Optimal Value of LCOE;48
7.4.4;4.4 Convergence Characteristics of the Solver;48
7.4.5;4.5 Validation of Results Using Particle Swarm Optimization;49
7.5;5 Conclusion;51
7.6;References;54
8;4 PSO-Based PID Controller Designing for LFC of Single Area Electrical Power Network;55
8.1;1 Introduction;55
8.2;2 Problem Formulation;57
8.2.1;2.1 System Description;57
8.2.2;2.2 A Brief Introduction of PID Controller;58
8.2.3;2.3 Objective Function Formulation;58
8.3;3 Employed Optimization Techniques;59
8.3.1;3.1 GA;59
8.3.2;3.2 PSO;59
8.4;4 Results and Discussions;59
8.4.1;4.1 Case 1: Objective Function-IAE;61
8.4.2;4.2 Case 2: Objective Function-ISE;62
8.4.3;4.3 Case 3: Objective Function-ITAE;63
8.4.4;4.4 Case 4: Objective Function-ITSE;64
8.5;5 Conclusion;65
8.6;References;66
9;5 Combined Economic Emission Dispatch of Hybrid Thermal PV System Using Artificial Bee Colony Optimization;67
9.1;1 Introduction;68
9.2;2 Problem Formulation;69
9.2.1;2.1 Objective Function;69
9.2.2;2.2 Equality Constraint;70
9.2.3;2.3 Inequality Constraint;70
9.3;3 Artificial Bee Colony Optimization;71
9.4;4 Results and Discussion;72
9.4.1;4.1 Description of Test Cases;72
9.4.2;4.2 Simulation Results;74
9.5;5 Conclusion;78
9.6;References;79
10;6 Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization;80
10.1;1 Introduction;81
10.2;2 Problem Formulation;82
10.2.1;2.1 Inequality Constraints;83
10.2.2;2.2 Equality Constraints;84
10.3;3 Grey Wolf Optimization;84
10.4;4 Results and Discussion;86
10.4.1;4.1 Description of Test Cases;86
10.4.2;4.2 Simulation Results;87
10.5;5 Conclusion;90
10.6;References;92
11;7 Mixed-Integer Differential Evolution Algorithm for Optimal Static/Dynamic Scheduling of a Microgrid with Mixed Generation;94
11.1;1 Introduction;94
11.2;2 Problem Formulation for Microgrid with Mixed Generation;95
11.2.1;2.1 Generating Unit Limits;96
11.2.2;2.2 Supply and Load Balance Constraint;96
11.2.3;2.3 Generator Ramp Rate Limits;96
11.2.4;2.4 Formulation of Total Cost Function for the Wind-PV-Diesel Microgrid;97
11.2.5;2.5 SO and Two-Objective Optimization Functions;98
11.3;3 Mixed-Integer Differential Evolution (MIDE);99
11.4;4 Results and Discussion;100
11.4.1;4.1 Description of the Modified Microgrid Test System;100
11.4.2;4.2 Setting of the Optimal Parameters of MIDE;101
11.4.3;4.3 SO Optimal Static Scheduling of Microgrid Using MIDE;101
11.4.4;4.4 SO Optimal Dynamic Scheduling of Wind-PV-Diesel Microgrid;103
11.4.5;4.5 Two-Objective Dynamic Optimal Scheduling of Wind-PV-Diesel Microgrid;105
11.5;5 Comparison and Validation of Results;108
11.6;6 Conclusion;109
11.7;References;109
12;8 NSGA-II Based Reactive Power Management in Radial Distribution System Integrated with DGs;111
12.1;1 Introduction;111
12.2;2 Multi-Objective Reactive Power Management;113
12.2.1;2.1 Objective Functions of RPM Problem;113
12.3;3 Non-dominated Sorting Genetic Algorithm-II for MORPM;115
12.4;4 Results and Discussion;116
12.4.1;4.1 Case1: Minimization of PL and TVV;118
12.4.2;4.2 Case 2: Minimization of PL and TCRPS;119
12.4.3;4.3 Case 3: Minimization of PL, TVV, and TCRPS;120
12.5;5 Conclusion;121
12.6;References;122
13;9 Short-Term Hydrothermal Scheduling Using Bio-inspired Computing: A Review;124
13.1;1 Introduction;125
13.2;2 Formulation of SHTS Problem;126
13.2.1;2.1 Objective Function;126
13.2.2;2.2 Operational Constraints;127
13.3;3 Bio-Inspired Algorithm and Their Application;128
13.3.1;3.1 Genetic Algorithm (GA);128
13.3.2;3.2 Particle Swarm Optimization (PSO);129
13.3.3;3.3 Differential Evolution (DE);129
13.3.4;3.4 Evolutionary Programming (EP);132
13.3.5;3.5 Artificial Bee Colony (ABC) Algorithm;132
13.3.6;3.6 Gravitational Search Algorithm (GSA);133
13.3.7;3.7 Cuckoo Search Algorithm (CSA);134
13.3.8;3.8 Teaching-Learning-Based Optimization (TLBO);134
13.3.9;3.9 Flower Pollination Algorithm (FPA);135
13.4;4 Conclusion;135
13.5;References;136mehr

Autor

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.