Kiani-Moghaddam / Shivaie / Weinsier Modern Music-Inspired Optimization Algorithms for Electric Power Systems

Modeling, Analysis and Practice
1. Auflage 2019
ISBN: 978-3-030-12044-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 747 Seiten

Reihe: Power Systems

In today's world, with an increase in the breadth and scope of real-world engineering optimization problems as well as with the advent of big data, improving the performance and efficiency of algorithms for solving such problems has become an indispensable need for specialists and researchers. In contrast to conventional books in the field that employ traditional single-stage computational, single-dimensional, and single-homogeneous optimization algorithms, this book addresses multiple newfound architectures for meta-heuristic music-inspired optimization algorithms. These proposed algorithms, with multi-stage computational, multi-dimensional, and multi-inhomogeneous structures, bring about a new direction in the architecture of meta-heuristic algorithms for solving complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data. The architectures of these new algorithms may also be appropriate for finding an optimal solution or a Pareto-optimal solution set with higher accuracy and speed in comparison to other optimization algorithms, when feasible regions of the solution space and/or dimensions of the optimization problem increase. 
This book, unlike conventional books on power systems problems that only consider simple and impractical models, deals with complicated, techno-economic, real-world, large-scale models of power systems operation and planning. Innovative applicable ideas in these models make this book a precious resource for specialists and researchers with a background in power systems operation and planning.Provides an understanding of the optimization problems and algorithms, particularly meta-heuristic optimization algorithms, found in fields such as engineering, economics, management, and operations research;
Enhances existing architectures and develops innovative architectures for meta-heuristic music-inspired optimization algorithms in order to deal with complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data;
Addresses innovative multi-level, techno-economic, real-world, large-scale, computational-logical frameworks for power systems operation and planning, and illustrates practical training on implementation of the frameworks using the meta-heuristic music-inspired optimization algorithms.



Mohammad Kiani-Moghaddam received the B.Sc. degree with first class honors in Electrical Engineering from the Islamic Azad University of Najafabad, Isfahan, Iran, and the M.Sc. degree with first class honors in Electrical Engineering from the Shahid Beheshti University, Tehran, Iran. His emphasis is on the research, design, and application of complex mathematical models for use in the analysis of power systems with a particular focus on risk assessment, worth-based reliability evaluation, economic strategies, as well as artificial intelligence and optimization theory. He has served as a peer reviewer for over four international journals.
Mojtaba Shivaie is currently an Assistant Professor in the Faculty of Electrical Engineering and Robotic at the Shahrood University of Technology, Shahrood, Iran. He obtained the B.Sc. degree with first class honors in Electrical Engineering from the Semnan University, Semnan, Iran, in 2008. He also received the M.Sc. and Ph.D. degrees with first class honors, both in Electrical Engineering, from the Shahid Beheshti University, Tehran, Iran, in 2010 and 2015, respectively. He has worked extensively in the areas of power systems, smart distribution grids, stochastic simulation and optimization techniques, and he (with Mr. Kiani-Moghaddam and Prof. Weinsier) is the inventor of a modern optimization technique known as 'symphony orchestra search algorithm' and an innovative architecture for competitive electricity markets known as 'Hypaethral market'. He was awarded the Dr. Shahriari's scholarship by the office of honor students of the Shahid Beheshti University and the Dr. Kazemi-Ashtiani's award by the Iran's National Elites Foundation for outstanding educational and research achievements. He has served as an editorial board of the International Transaction of Electrical and Computer Engineers System journal and the Control and Systems Engineering journal and also a peer reviewer for over twelve high impact journals. He was a recipient of the outstanding reviewer award of the Applied Soft Computing in 2014, the Energy Conversion and Management in 2016, and the Electric Power Systems Research in 2017.
Philip D. Weinsier is currently Professor and Electrical/Electronic Engineering Technology Program Director at Bowling Green State University-Firelands. He received his BS degrees in Physics/Mathematics and Industrial Education/Teaching from Berry College in 1978; MS degree in Industrial Education and EdD degree in Vocational/Technical Education from Clemson University in 1979 and 1990, respectively. He is currently senior editor of the International Journal of Modern Engineering and the International Journal of Engineering Research and Innovation, and Editor-in-Chief of the Technology Interface International Journal. He is a Fulbright Scholar, a lifetime member of the International Fulbright Association, and a member of the European Association for Research on Learning and Instruction since 1989.

Autoren/Hrsg.

Kiani-Moghaddam, Mohammad

Shivaie, Mojtaba

Weinsier, Philip D.

Inhaltsverzeichnis

1;Foreword;7
2;Preface;9
3;Acknowledgments;17
4;Contents;18
5;About the Authors;25
6;Part I: Fundamental Concepts of Optimization Problems and Theory of Meta-Heuristic Music-Inspired Optimization Algorithms;27
6.1;Chapter 1: Introduction to Meta-heuristic Optimization Algorithms;28
6.1.1;1.1 Introduction;28
6.1.2;1.2 An Optimization Problem and Its Parameters;29
6.1.2.1;1.2.1 Mathematical Description of an Optimization Problem;29
6.1.3;1.3 Classification of an Optimization Problem;31
6.1.3.1;1.3.1 Classification of Optimization Problems from the Perspective of a Number of Objective Functions;31
6.1.3.2;1.3.2 Classification of Optimization Problems from the Perspective of Constraints;32
6.1.3.3;1.3.3 Classification of Optimization Problems from the Perspective of the Nature of Employed Equations;33
6.1.3.4;1.3.4 Classification of Optimization Problems from the Perspective of an Objective Functions Landscape;34
6.1.3.5;1.3.5 Classification of Optimization Problems from the Perspective of the Kind of Decision-Making Variables;34
6.1.3.6;1.3.6 Classification of Optimization Problems from the Perspective of the Number of Decision-Making Variables;35
6.1.3.7;1.3.7 Classification of Optimization Problems from the Perspective of the Separability of the Employed Equations;36
6.1.3.8;1.3.8 Classification of Optimization Problems from the Perspective of Uncertainty;36
6.1.4;1.4 Optimization Algorithms and Their Characteristics;37
6.1.5;1.5 Meta-heuristic Optimization Algorithms;38
6.1.5.1;1.5.1 Classification of Meta-heuristic Optimization Algorithms with a Focus on Inspirational Sources;39
6.1.5.1.1;1.5.1.1 Swarm Intelligence-Based Meta-heuristic Optimization Algorithms;39
6.1.5.1.2;1.5.1.2 Biologically Inspired Meta-heuristic Optimization Algorithms Not Based on Swarm Intelligence;40
6.1.5.1.3;1.5.1.3 Physics- and Chemistry-Based Meta-heuristic Optimization Algorithms;40
6.1.5.1.4;1.5.1.4 Human Behavior- and Society-Inspired Meta-heuristic Optimization Algorithms;41
6.1.5.1.5;1.5.1.5 Some Hints Concerning the Architecture of Meta-heuristic Optimization Algorithms;41
6.1.6;1.6 Conclusions;42
6.1.7;Appendix 1: List of Abbreviations and Acronyms;42
6.1.8;Appendix 2: List of Mathematical Symbols;43
6.1.9;References;44
6.2;Chapter 2: Introduction to Multi-objective Optimization and Decision-Making Analysis;46
6.2.1;2.1 Introduction;46
6.2.2;2.2 Necessity of Using Multi-objective Optimization;48
6.2.3;2.3 Fundamental Concepts of Optimization in the MOOPs;49
6.2.3.1;2.3.1 Mathematical Description of a MOOP;49
6.2.3.2;2.3.2 Concepts Associated with Efficiency, Efficient frontier, and Dominance;50
6.2.3.3;2.3.3 Concepts Pertaining to Pareto Optimality;51
6.2.3.4;2.3.4 Concepts Related to the Vector of Ideal Objective Functions and the Vector of Nadir Objective Functions;53
6.2.3.5;2.3.5 Concepts Relevant to the Investigation of Pareto Optimality;55
6.2.4;2.4 Multi-objective Optimization Algorithms;55
6.2.4.1;2.4.1 Noninteractive Approaches;56
6.2.4.1.1;2.4.1.1 Basic Approaches;56
6.2.4.1.2;2.4.1.2 No-Preference Approaches;60
6.2.4.1.3;2.4.1.3 A Priori Approaches;60
6.2.4.1.4;2.4.1.4 A Posteriori Approaches;61
6.2.4.2;2.4.2 Interactive Approaches;61
6.2.5;2.5 Selection of the Final Solution by Using a Fuzzy Satisfying Method;63
6.2.5.1;2.5.1 Conservative Methodology;65
6.2.5.2;2.5.2 Distance Metric Methodology;66
6.2.5.3;2.5.3 Step-by-Step Process for Implementing the FSM;66
6.2.6;2.6 Conclusions;67
6.2.7;Appendix 1: List of Abbreviations and Acronyms;68
6.2.8;Appendix 2: List of Mathematical Symbols;68
6.2.9;References;70
6.3;Chapter 3: Music-Inspired Optimization Algorithms: From Past to Present;71
6.3.1;3.1 Introduction;71
6.3.2;3.2 A Brief Review of Music;74
6.3.2.1;3.2.1 The Definition of Music;74
6.3.2.2;3.2.2 A Brief Review of Music History;75
6.3.2.3;3.2.3 The Interdependencies of Phenomena and Concepts of Music and the Optimization Problem;75
6.3.3;3.3 Harmony Search Algorithm;77
6.3.3.1;3.3.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters;78
6.3.3.2;3.3.2 Stage 2: Initialization Stage;79
6.3.3.2.1;3.3.2.1 Sub-stage 2.1: Initialization of the Parameters of the SS-HSA;79
6.3.3.2.2;3.3.2.2 Sub-stage 2.2: Initialization of the HM;80
6.3.3.3;3.3.3 Stage 3: Computational Stage;81
6.3.3.3.1;3.3.3.1 Sub-stage 3.1: Improvisation of a New Harmony Vector;83
6.3.3.3.2;3.3.3.2 Sub-stage 3.2: Update of the HM;85
6.3.3.3.3;3.3.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SS-HSA;87
6.3.3.4;3.3.4 Stage 4: Selection Stage-Selection of the Final Optimal Solution-The Best Harmony;87
6.3.4;3.4 Enhanced Versions of the Single-Stage Computational, Single-Dimensional Harmony Search Algorithm;89
6.3.5;3.5 Improved Harmony Search Algorithm;90
6.3.6;3.6 Melody Search Algorithm;93
6.3.6.1;3.6.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters;97
6.3.6.2;3.6.2 Stage 2: Initialization Stage;98
6.3.6.2.1;3.6.2.1 Sub-stage 2.1: Initialization of the Parameters of the TMS-MSA;98
6.3.6.2.2;3.6.2.2 Sub-stage 2.2: Initialization of the MM;100
6.3.6.3;3.6.3 Stage 3: Single Computational Stage or SIS;103
6.3.6.3.1;3.6.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player;103
6.3.6.3.2;3.6.3.2 Sub-stage 3.2: Update of Each PM;104
6.3.6.3.3;3.6.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS;105
6.3.6.4;3.6.4 Stage 4: Pseudo-Group Computational Stage or PGIS;106
6.3.6.4.1;3.6.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player Taking into Account the Feasible Ranges of the Upda...;106
6.3.6.4.2;3.6.4.2 Sub-stage 4.2: Update of Each PM;106
6.3.6.4.3;3.6.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables-for the Next Improvisatio...;106
6.3.6.4.4;3.6.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the PGIS;107
6.3.6.5;3.6.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Best Melody;108
6.3.6.6;3.6.6 Alternative Improvisation Procedure;109
6.3.7;3.7 Conclusions;115
6.3.8;Appendix 1: List of Abbreviations and Acronyms;115
6.3.9;Appendix 2: List of Mathematical Symbols;116
6.3.10;References;119
6.4;Chapter 4: Advances in Music-Inspired Optimization Algorithms;120
6.4.1;4.1 Introduction;120
6.4.2;4.2 Continuous/Discrete TMS-MSA;123
6.4.2.1;4.2.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters;124
6.4.2.2;4.2.2 Stage 2: Initialization Stage;125
6.4.2.2.1;4.2.2.1 Sub-stage 2.1: Initialization of the Parameters of the Proposed Continuous/Discrete TMS-MSA;125
6.4.2.2.2;4.2.2.2 Sub-stage 2.2: Initialization of the MM;125
6.4.2.3;4.2.3 Stage 3: Single Computational Stage or SIS;127
6.4.2.3.1;4.2.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player;127
6.4.2.3.2;4.2.3.2 Sub-stage 3.2: Update of Each PM;129
6.4.2.3.3;4.2.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS;130
6.4.2.4;4.2.4 Stage 4: Pseudo-Group Computational Stage or PGIS;130
6.4.2.4.1;4.2.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player;131
6.4.2.4.2;4.2.4.2 Sub-stage 4.2: Update of Memory of Each Player;132
6.4.2.4.3;4.2.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables for the Next Improvisatio...;132
6.4.2.4.4;4.2.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the Pseudo-Group Improvisation Stage;132
6.4.2.5;4.2.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Most Favorable Melody;132
6.4.2.6;4.2.6 Continuous/Discrete Alternative Improvisation Procedure;134
6.4.3;4.3 Enhanced Version of the Proposed Continuous/Discrete TMS-MSA;139
6.4.4;4.4 Multi-stage Computational Multi-dimensional Multiple-Homogeneous Enhanced Melody Search Algorithm: Symphony Orchestra Sear...;157
6.4.4.1;4.4.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters;165
6.4.4.2;4.4.2 Stage 2: Initialization Stage;166
6.4.4.2.1;4.4.2.1 Sub-stage 2.1: Initialization of the Parameters of the SOSA;166
6.4.4.2.2;4.4.2.2 Sub-stage 2.2: Initialization of the Symphony Orchestra Memory;169
6.4.4.3;4.4.3 Stage 3: Single Computational Stage or SIS;171
6.4.4.3.1;4.4.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra;171
6.4.4.3.2;4.4.3.2 Sub-stage 3.2: Update of Each Available PM in the Symphony Orchestra;173
6.4.4.3.3;4.4.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS;174
6.4.4.4;4.4.4 Stage 4: Group Computational Stage for Each Homogeneous Musical Group or GISHMG;174
6.4.4.4.1;4.4.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra Taking into Account the F...;175
6.4.4.4.2;4.4.4.2 Sub-stage 4.2: Update of Each Available PM in the Symphony Orchestra;177
6.4.4.4.3;4.4.4.3 Sub-stage 4.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for Each Homogeneous ...;177
6.4.4.4.4;4.4.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the GISHMG;177
6.4.4.5;4.4.5 Stage 5: Group Computational Stage for the Inhomogeneous Musical Ensemble or GISIME;178
6.4.4.5.1;4.4.5.1 Sub-stage 5.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra Taking into Account the F...;179
6.4.4.5.2;4.4.5.2 Sub-stage 5.2: Update of Each Available PM in the Symphony Orchestra;180
6.4.4.5.3;4.4.5.3 Sub-stage 5.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for the Inhomogeneous...;180
6.4.4.5.4;4.4.5.4 Sub-stage 5.4: Check of the Stopping Criterion of the GISIME;180
6.4.4.6;4.4.6 Stage 6: Selection Stage-Selection of the Final Optimal Solution-the Best Melody;182
6.4.4.7;4.4.7 Novel Improvisation Procedure;183
6.4.4.8;4.4.8 Some Hints Regarding the Architecture of the Proposed SOSA;190
6.4.5;4.5 Multi-objective Strategies for the Music-Inspired Optimization Algorithms;196
6.4.5.1;4.5.1 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Single-Stage Computational...;196
6.4.5.1.1;4.5.1.1 Multi-objective Strategy for the SS-HSA;197
6.4.5.1.2;4.5.1.2 Multi-objective Strategy for the SS-IHSA;212
6.4.5.2;4.5.2 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Two-Stage Computational Mu...;215
6.4.5.2.1;4.5.2.1 Multi-objective Strategy for the Proposed Continuous/Discrete TMS-MSA;215
6.4.5.2.2;4.5.2.2 Multi-objective Strategy for the Proposed TMS-EMSA;235
6.4.5.3;4.5.3 Multi-objective Strategy for the Meta-heuristic Music-Inspired Optimization Algorithms with Multi-stage Computational Mu...;245
6.4.6;4.6 Conclusions;271
6.4.7;Appendix 1: List of Abbreviations and Acronyms;276
6.4.8;Appendix 2: List of Mathematical Symbols;278
6.4.9;References;285
7;Part II: Power Systems Operation and Planning Problems;286
7.1;Chapter 5: Power Systems Operation;287
7.1.1;5.1 Introduction;287
7.1.2;5.2 A Brief Review of Game Theory;289
7.1.2.1;5.2.1 Classifications of the Game;289
7.1.2.2;5.2.2 The Concept of Nash Equilibrium;291
7.1.2.3;5.2.3 Modeling of Game Theory in the Electricity Markets with Imperfect Competition;292
7.1.2.3.1;5.2.3.1 Cournot-Based Model and/or Playing with Quantities;292
7.1.2.3.2;5.2.3.2 Stackelberg Leadership-Based Model;294
7.1.2.3.3;5.2.3.3 Bertrand-Based Model and Playing with Prices;294
7.1.2.3.4;5.2.3.4 The Supply Function Equilibrium-Based Model;295
7.1.3;5.3 A Bilateral Bidding Mechanism in the Competitive Security-Constrained Electricity Market: A Bi-Level Computational-Logical...;298
7.1.3.1;5.3.1 Bilateral Bidding Strategy Model: First Level (Problem A);299
7.1.3.1.1;5.3.1.1 Mathematical Model of Bidding Strategies for GENCOs;302
7.1.3.1.2;5.3.1.2 Mathematical Model of a Bidding Strategy for DISCOs;305
7.1.3.2;5.3.2 Security-Constrained Electricity Market Model: Second Level (Problem B);308
7.1.3.3;5.3.3 Overview of the Bi-Level Computational-Logical Framework;312
7.1.3.4;5.3.4 Solution Method and Implementation Considerations;314
7.1.3.5;5.3.5 Simulation Results and Case Studies;315
7.1.3.5.1;5.3.5.1 First Case: Simulation Results and Discussion;318
7.1.3.5.2;5.3.5.2 Second Case: Simulation Results and Discussion;321
7.1.3.5.3;5.3.5.3 Performance Evaluation of the Proposed Music-Inspired Optimization Algorithms;331
7.1.4;5.4 Conclusions;335
7.1.5;Appendix 1: List of Abbreviations and Acronyms;337
7.1.6;Appendix 2: List of Mathematical Symbols;338
7.1.7;Appendix 3: Input data;340
7.1.8;References;346
7.2;Chapter 6: Power Systems Planning;348
7.2.1;6.1 Introduction;348
7.2.2;6.2 A Brief Review of Power System Planning Studies;350
7.2.2.1;6.2.1 Why Do the Power Systems Need the Expansion Planning?;350
7.2.2.2;6.2.2 A Brief Review of Power System Planning Structure;350
7.2.2.3;6.2.3 Power System Planning Issues;351
7.2.2.3.1;6.2.3.1 From the Standpoint of Power System Structure;352
7.2.2.3.2;6.2.3.2 From the Standpoint of the Planning Horizon;353
7.2.2.3.3;6.2.3.3 From the Standpoint of the Uncertainties;354
7.2.2.3.4;6.2.3.4 From the Standpoint of the Solving Algorithms;357
7.2.3;6.3 Pseudo-Dynamic Generation Expansion Planning: A Strategic Tri-level Computational-Logical Framework;358
7.2.3.1;6.3.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework;359
7.2.3.1.1;6.3.1.1 Bilateral Bidding Mechanism: First Level (Problem A);362
7.2.3.1.2;6.3.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B);362
7.2.3.1.3;6.3.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C);363
7.2.3.2;6.3.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework;368
7.2.3.3;6.3.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework;372
7.2.3.3.1;6.3.3.1 The IGDT Severe Twofold Uncertainty Model;373
7.2.3.3.2;6.3.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function;376
7.2.3.3.3;6.3.3.3 The IGDT Risk-Taker Decision-Making Strategy: Opportunity Function;379
7.2.3.4;6.3.4 Solution Method and Implementation Considerations;382
7.2.3.5;6.3.5 Simulation Results and Case Studies;383
7.2.3.5.1;6.3.5.1 First Case: Simulation Results and Discussion;387
7.2.3.5.2;6.3.5.2 Second Case: Simulation Results and Discussion;399
7.2.3.5.3;6.3.5.3 Quantitative Verification of the Proposed IGDT Risk-Taker Decision-Making Policy in Comparison to a Robust Optimizatio...;413
7.2.3.5.4;6.3.5.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion;413
7.2.4;6.4 Pseudo-Dynamic Transmission Expansion Planning: A Strategic Tri-level Computational-Logical Framework;423
7.2.4.1;6.4.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework;426
7.2.4.1.1;6.4.1.1 Bilateral Bidding Mechanism: First Level (Problem A);426
7.2.4.1.2;6.4.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B);426
7.2.4.1.3;6.4.1.3 Pseudo-Dynamic Transmission Expansion Planning: Third Level (Problem C);426
7.2.4.2;6.4.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework;431
7.2.4.3;6.4.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework;434
7.2.4.3.1;6.4.3.1 The IGDT Severe Twofold Uncertainty Model;435
7.2.4.3.2;6.4.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function;435
7.2.4.3.3;6.4.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function;438
7.2.4.4;6.4.4 Solution Method and Implementation Considerations;440
7.2.4.5;6.4.5 Simulation Results and Case Studies;441
7.2.4.5.1;6.4.5.1 The Modified IEEE 30-Bus Test System;443
7.2.4.5.1.1;6.4.5.1.1 First Case: Simulation Results and Discussion;446
7.2.4.5.1.2;6.4.5.1.2 Second Case: Simulation Results and Discussion;456
7.2.4.5.2;6.4.5.2 Large-Scale Iranian 400 kV Transmission Network;463
7.2.4.5.2.1;6.4.5.2.1 First Case: Simulation Results and Discussion;471
7.2.4.5.2.2;6.4.5.2.2 Second Case: Simulation Results and Discussion;471
7.2.4.5.2.3;6.4.5.2.3 Investigation of the Effects of Volatility in Market Price and Demand Uncertainties;473
7.2.4.5.2.4;6.4.5.2.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimi...;478
7.2.4.5.2.5;6.4.5.2.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion;481
7.2.5;6.5 Coordination of Pseudo-Dynamic Generation and Transmission Expansion Planning: A Strategic Quad-Level Computational-Logica...;487
7.2.5.1;6.5.1 Mathematical Model of the Deterministic Strategic Quad-Level Computational-Logical Framework;488
7.2.5.1.1;6.5.1.1 Bilateral Bidding Mechanism: First Level (Problem A);488
7.2.5.1.2;6.5.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B);491
7.2.5.1.3;6.5.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C);492
7.2.5.1.4;6.5.1.4 Pseudo-Dynamic Transmission Expansion Planning: Fourth Level (Problem D);492
7.2.5.2;6.5.2 Overview of the Deterministic Strategic Quad-Level Computational-Logical Framework;492
7.2.5.3;6.5.3 Mathematical Model of the Risk-Driven Strategic Quad-Level Computational-Logical Framework;500
7.2.5.3.1;6.5.3.1 The IGDT Severe Twofold Uncertainty Model;500
7.2.5.3.2;6.5.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function;500
7.2.5.3.3;6.5.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function;503
7.2.5.4;6.5.4 Solution Method and Implementation Considerations;506
7.2.5.5;6.5.5 Simulation Results and Case Studies;509
7.2.5.5.1;6.5.5.1 First Case: Simulation Results and Discussion;514
7.2.5.5.2;6.5.5.2 Second Case: Simulation Results and Discussion;517
7.2.5.5.3;6.5.5.3 Investigation into the Performance of the Proposed Framework Under the Coordinated and Uncoordinated Decisions for the...;520
7.2.5.5.4;6.5.5.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza...;524
7.2.5.5.5;6.5.5.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion;527
7.2.6;6.6 Pseudo-Dynamic Open-Loop Distribution Expansion Planning: A Techno-Economic Framework;540
7.2.6.1;6.6.1 Mathematical Model of the Deterministic Techno-Economic Framework;541
7.2.6.2;6.6.2 Mathematical Model of the Risk-Driven Techno-Economic Framework;553
7.2.6.2.1;6.6.2.1 The IGDT Severe Twofold Uncertainty Model;553
7.2.6.2.2;6.6.2.2 The IGDT Risk-Averse Decision-Making Model: Robustness Function;555
7.2.6.2.3;6.6.2.3 The IGDT Risk-Taker Decision-Making Model: Opportunity Function;556
7.2.6.3;6.6.3 Solution Method and Implementation Considerations;558
7.2.6.4;6.6.4 Simulation Results and Case Studies;559
7.2.6.4.1;6.6.4.1 First Case: Simulation Results and Discussion;563
7.2.6.4.2;6.6.4.2 Second Case: Simulation Results and Discussion;569
7.2.6.4.3;6.6.4.3 The Impact of the Presence of Distributed Generation Resources on the Voltage Profile;574
7.2.6.4.4;6.6.4.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza...;576
7.2.6.4.5;6.6.4.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion;578
7.2.7;6.7 Conclusions;589
7.2.8;Appendix 1: List of Abbreviations and Acronyms;592
7.2.9;Appendix 2: List of Mathematical Symbols;594
7.2.10;Appendix 3: Input Data;607
7.2.11;References;642
7.3;Chapter 7: Power Filters Planning;647
7.3.1;7.1 Introduction;647
7.3.2;7.2 A Brief Review of Harmonic Power Filter Planning Studies;649
7.3.2.1;7.2.1 Nonlinear Loads and Their Malicious Effects;650
7.3.2.2;7.2.2 Harmonic Power Filters;651
7.3.2.3;7.2.3 Harmonic Power Flow;653
7.3.2.4;7.2.4 Harmonic Power Filter Planning Problem;654
7.3.3;7.3 Hybrid Harmonic Power Filter Planning: A Techno-economic Framework;655
7.3.3.1;7.3.1 Mathematical Model of the Techno-economic Multi-objective Framework;656
7.3.3.1.1;7.3.1.1 Deterministic Decoupled Harmonic Power Flow Methodology;659
7.3.3.1.2;7.3.1.2 Passive and Active Harmonic Power Filters;666
7.3.3.1.3;7.3.1.3 Hybrid Harmonic Power Filter Planning Problem;670
7.3.3.1.4;7.3.1.4 Probabilistic Decoupled Harmonic Power Flow Methodology;677
7.3.3.2;7.3.2 Solution Method and Implementation Considerations;681
7.3.3.3;7.3.3 Simulation Results and Case Studies;681
7.3.3.3.1;7.3.3.1 IEEE 18-Bus Distorted Test Network;682
7.3.3.3.1.1;7.3.3.1.1 First Case: Simulation Results and Discussion;687
7.3.3.3.1.2;7.3.3.1.2 Second Case: Simulation Results and Discussion;692
7.3.3.3.1.3;7.3.3.1.3 Third Case: Simulation Results and Discussion;695
7.3.3.3.1.4;7.3.3.1.4 Investigation of Passive Harmonic Power Filter Performance;700
7.3.3.3.2;7.3.3.2 The 34-Bus Distribution Test Network;701
7.3.3.3.2.1;7.3.3.2.1 First Case: Simulation Results and Discussion;704
7.3.3.3.2.2;7.3.3.2.2 Second Case: Simulation Results and Discussion;705
7.3.3.3.2.3;7.3.3.2.3 Third Case: Simulation Results and Discussion;706
7.3.3.3.2.4;7.3.3.2.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion;709
7.3.4;7.4 Conclusions;717
7.3.5;Appendix 1: List of Abbreviations and Acronyms;720
7.3.6;Appendix 2: List of Mathematical Symbols;721
7.3.7;Appendix 3: Input Data;727
7.3.8;References;735
8;Index;737