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Foreword |
7 |
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Preface |
9 |
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Acknowledgments |
17 |
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Contents |
18 |
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About the Authors |
25 |
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Part I: Fundamental Concepts of Optimization Problems and Theory of Meta-Heuristic Music-Inspired Optimization Algorithms |
27 |
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Chapter 1: Introduction to Meta-heuristic Optimization Algorithms |
28 |
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1.1 Introduction |
28 |
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1.2 An Optimization Problem and Its Parameters |
29 |
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1.2.1 Mathematical Description of an Optimization Problem |
29 |
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1.3 Classification of an Optimization Problem |
31 |
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1.3.1 Classification of Optimization Problems from the Perspective of a Number of Objective Functions |
31 |
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1.3.2 Classification of Optimization Problems from the Perspective of Constraints |
32 |
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1.3.3 Classification of Optimization Problems from the Perspective of the Nature of Employed Equations |
33 |
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1.3.4 Classification of Optimization Problems from the Perspective of an Objective Functions Landscape |
34 |
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1.3.5 Classification of Optimization Problems from the Perspective of the Kind of Decision-Making Variables |
34 |
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1.3.6 Classification of Optimization Problems from the Perspective of the Number of Decision-Making Variables |
35 |
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1.3.7 Classification of Optimization Problems from the Perspective of the Separability of the Employed Equations |
36 |
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1.3.8 Classification of Optimization Problems from the Perspective of Uncertainty |
36 |
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1.4 Optimization Algorithms and Their Characteristics |
37 |
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1.5 Meta-heuristic Optimization Algorithms |
38 |
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1.5.1 Classification of Meta-heuristic Optimization Algorithms with a Focus on Inspirational Sources |
39 |
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1.5.1.1 Swarm Intelligence-Based Meta-heuristic Optimization Algorithms |
39 |
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1.5.1.2 Biologically Inspired Meta-heuristic Optimization Algorithms Not Based on Swarm Intelligence |
40 |
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1.5.1.3 Physics- and Chemistry-Based Meta-heuristic Optimization Algorithms |
40 |
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1.5.1.4 Human Behavior- and Society-Inspired Meta-heuristic Optimization Algorithms |
41 |
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1.5.1.5 Some Hints Concerning the Architecture of Meta-heuristic Optimization Algorithms |
41 |
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1.6 Conclusions |
42 |
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Appendix 1: List of Abbreviations and Acronyms |
42 |
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Appendix 2: List of Mathematical Symbols |
43 |
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References |
44 |
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Chapter 2: Introduction to Multi-objective Optimization and Decision-Making Analysis |
46 |
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2.1 Introduction |
46 |
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2.2 Necessity of Using Multi-objective Optimization |
48 |
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2.3 Fundamental Concepts of Optimization in the MOOPs |
49 |
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2.3.1 Mathematical Description of a MOOP |
49 |
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2.3.2 Concepts Associated with Efficiency, Efficient frontier, and Dominance |
50 |
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2.3.3 Concepts Pertaining to Pareto Optimality |
51 |
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2.3.4 Concepts Related to the Vector of Ideal Objective Functions and the Vector of Nadir Objective Functions |
53 |
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2.3.5 Concepts Relevant to the Investigation of Pareto Optimality |
55 |
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2.4 Multi-objective Optimization Algorithms |
55 |
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2.4.1 Noninteractive Approaches |
56 |
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2.4.1.1 Basic Approaches |
56 |
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2.4.1.2 No-Preference Approaches |
60 |
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2.4.1.3 A Priori Approaches |
60 |
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2.4.1.4 A Posteriori Approaches |
61 |
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2.4.2 Interactive Approaches |
61 |
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2.5 Selection of the Final Solution by Using a Fuzzy Satisfying Method |
63 |
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2.5.1 Conservative Methodology |
65 |
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2.5.2 Distance Metric Methodology |
66 |
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2.5.3 Step-by-Step Process for Implementing the FSM |
66 |
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2.6 Conclusions |
67 |
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Appendix 1: List of Abbreviations and Acronyms |
68 |
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Appendix 2: List of Mathematical Symbols |
68 |
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References |
70 |
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Chapter 3: Music-Inspired Optimization Algorithms: From Past to Present |
71 |
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3.1 Introduction |
71 |
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3.2 A Brief Review of Music |
74 |
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3.2.1 The Definition of Music |
74 |
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3.2.2 A Brief Review of Music History |
75 |
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3.2.3 The Interdependencies of Phenomena and Concepts of Music and the Optimization Problem |
75 |
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3.3 Harmony Search Algorithm |
77 |
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3.3.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters |
78 |
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3.3.2 Stage 2: Initialization Stage |
79 |
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3.3.2.1 Sub-stage 2.1: Initialization of the Parameters of the SS-HSA |
79 |
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3.3.2.2 Sub-stage 2.2: Initialization of the HM |
80 |
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3.3.3 Stage 3: Computational Stage |
81 |
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3.3.3.1 Sub-stage 3.1: Improvisation of a New Harmony Vector |
83 |
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3.3.3.2 Sub-stage 3.2: Update of the HM |
85 |
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3.3.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SS-HSA |
87 |
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3.3.4 Stage 4: Selection Stage-Selection of the Final Optimal Solution-The Best Harmony |
87 |
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3.4 Enhanced Versions of the Single-Stage Computational, Single-Dimensional Harmony Search Algorithm |
89 |
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3.5 Improved Harmony Search Algorithm |
90 |
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3.6 Melody Search Algorithm |
93 |
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3.6.1 Stage 1: Definition Stage-Definition of the Optimization Problem and its Parameters |
97 |
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3.6.2 Stage 2: Initialization Stage |
98 |
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3.6.2.1 Sub-stage 2.1: Initialization of the Parameters of the TMS-MSA |
98 |
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3.6.2.2 Sub-stage 2.2: Initialization of the MM |
100 |
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3.6.3 Stage 3: Single Computational Stage or SIS |
103 |
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3.6.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player |
103 |
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3.6.3.2 Sub-stage 3.2: Update of Each PM |
104 |
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3.6.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS |
105 |
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3.6.4 Stage 4: Pseudo-Group Computational Stage or PGIS |
106 |
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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 |
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3.6.4.2 Sub-stage 4.2: Update of Each PM |
106 |
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3.6.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables-for the Next Improvisatio... |
106 |
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3.6.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the PGIS |
107 |
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3.6.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Best Melody |
108 |
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3.6.6 Alternative Improvisation Procedure |
109 |
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3.7 Conclusions |
115 |
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Appendix 1: List of Abbreviations and Acronyms |
115 |
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Appendix 2: List of Mathematical Symbols |
116 |
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References |
119 |
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Chapter 4: Advances in Music-Inspired Optimization Algorithms |
120 |
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4.1 Introduction |
120 |
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4.2 Continuous/Discrete TMS-MSA |
123 |
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4.2.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters |
124 |
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4.2.2 Stage 2: Initialization Stage |
125 |
|
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4.2.2.1 Sub-stage 2.1: Initialization of the Parameters of the Proposed Continuous/Discrete TMS-MSA |
125 |
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4.2.2.2 Sub-stage 2.2: Initialization of the MM |
125 |
|
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4.2.3 Stage 3: Single Computational Stage or SIS |
127 |
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4.2.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player |
127 |
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4.2.3.2 Sub-stage 3.2: Update of Each PM |
129 |
|
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4.2.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS |
130 |
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4.2.4 Stage 4: Pseudo-Group Computational Stage or PGIS |
130 |
|
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4.2.4.1 Sub-stage 4.1: Improvisation of a New Melody Vector by Each Player |
131 |
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4.2.4.2 Sub-stage 4.2: Update of Memory of Each Player |
132 |
|
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4.2.4.3 Sub-stage 4.3: Update of the Feasible Ranges of Pitches-Continuous Decision-Making Variables for the Next Improvisatio... |
132 |
|
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4.2.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the Pseudo-Group Improvisation Stage |
132 |
|
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4.2.5 Stage 5: Selection Stage-Selection of the Final Optimal Solution-The Most Favorable Melody |
132 |
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4.2.6 Continuous/Discrete Alternative Improvisation Procedure |
134 |
|
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4.3 Enhanced Version of the Proposed Continuous/Discrete TMS-MSA |
139 |
|
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4.4 Multi-stage Computational Multi-dimensional Multiple-Homogeneous Enhanced Melody Search Algorithm: Symphony Orchestra Sear... |
157 |
|
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4.4.1 Stage 1: Definition Stage-Definition of the Optimization Problem and Its Parameters |
165 |
|
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4.4.2 Stage 2: Initialization Stage |
166 |
|
|
4.4.2.1 Sub-stage 2.1: Initialization of the Parameters of the SOSA |
166 |
|
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4.4.2.2 Sub-stage 2.2: Initialization of the Symphony Orchestra Memory |
169 |
|
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4.4.3 Stage 3: Single Computational Stage or SIS |
171 |
|
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4.4.3.1 Sub-stage 3.1: Improvisation of a New Melody Vector by Each Player in the Symphony Orchestra |
171 |
|
|
4.4.3.2 Sub-stage 3.2: Update of Each Available PM in the Symphony Orchestra |
173 |
|
|
4.4.3.3 Sub-stage 3.3: Check of the Stopping Criterion of the SIS |
174 |
|
|
4.4.4 Stage 4: Group Computational Stage for Each Homogeneous Musical Group or GISHMG |
174 |
|
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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 |
|
|
4.4.4.2 Sub-stage 4.2: Update of Each Available PM in the Symphony Orchestra |
177 |
|
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4.4.4.3 Sub-stage 4.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for Each Homogeneous ... |
177 |
|
|
4.4.4.4 Sub-stage 4.4: Check of the Stopping Criterion of the GISHMG |
177 |
|
|
4.4.5 Stage 5: Group Computational Stage for the Inhomogeneous Musical Ensemble or GISIME |
178 |
|
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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 |
|
|
4.4.5.2 Sub-stage 5.2: Update of Each Available PM in the Symphony Orchestra |
180 |
|
|
4.4.5.3 Sub-stage 5.3: Update of the Feasible Ranges of the Pitches-Continuous Decision-Making Variables-for the Inhomogeneous... |
180 |
|
|
4.4.5.4 Sub-stage 5.4: Check of the Stopping Criterion of the GISIME |
180 |
|
|
4.4.6 Stage 6: Selection Stage-Selection of the Final Optimal Solution-the Best Melody |
182 |
|
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4.4.7 Novel Improvisation Procedure |
183 |
|
|
4.4.8 Some Hints Regarding the Architecture of the Proposed SOSA |
190 |
|
|
4.5 Multi-objective Strategies for the Music-Inspired Optimization Algorithms |
196 |
|
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4.5.1 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Single-Stage Computational... |
196 |
|
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4.5.1.1 Multi-objective Strategy for the SS-HSA |
197 |
|
|
4.5.1.2 Multi-objective Strategy for the SS-IHSA |
212 |
|
|
4.5.2 Multi-objective Strategies for the Meta-heuristic Music-Inspired Optimization Algorithms with Two-Stage Computational Mu... |
215 |
|
|
4.5.2.1 Multi-objective Strategy for the Proposed Continuous/Discrete TMS-MSA |
215 |
|
|
4.5.2.2 Multi-objective Strategy for the Proposed TMS-EMSA |
235 |
|
|
4.5.3 Multi-objective Strategy for the Meta-heuristic Music-Inspired Optimization Algorithms with Multi-stage Computational Mu... |
245 |
|
|
4.6 Conclusions |
271 |
|
|
Appendix 1: List of Abbreviations and Acronyms |
276 |
|
|
Appendix 2: List of Mathematical Symbols |
278 |
|
|
References |
285 |
|
|
Part II: Power Systems Operation and Planning Problems |
286 |
|
|
Chapter 5: Power Systems Operation |
287 |
|
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5.1 Introduction |
287 |
|
|
5.2 A Brief Review of Game Theory |
289 |
|
|
5.2.1 Classifications of the Game |
289 |
|
|
5.2.2 The Concept of Nash Equilibrium |
291 |
|
|
5.2.3 Modeling of Game Theory in the Electricity Markets with Imperfect Competition |
292 |
|
|
5.2.3.1 Cournot-Based Model and/or Playing with Quantities |
292 |
|
|
5.2.3.2 Stackelberg Leadership-Based Model |
294 |
|
|
5.2.3.3 Bertrand-Based Model and Playing with Prices |
294 |
|
|
5.2.3.4 The Supply Function Equilibrium-Based Model |
295 |
|
|
5.3 A Bilateral Bidding Mechanism in the Competitive Security-Constrained Electricity Market: A Bi-Level Computational-Logical... |
298 |
|
|
5.3.1 Bilateral Bidding Strategy Model: First Level (Problem A) |
299 |
|
|
5.3.1.1 Mathematical Model of Bidding Strategies for GENCOs |
302 |
|
|
5.3.1.2 Mathematical Model of a Bidding Strategy for DISCOs |
305 |
|
|
5.3.2 Security-Constrained Electricity Market Model: Second Level (Problem B) |
308 |
|
|
5.3.3 Overview of the Bi-Level Computational-Logical Framework |
312 |
|
|
5.3.4 Solution Method and Implementation Considerations |
314 |
|
|
5.3.5 Simulation Results and Case Studies |
315 |
|
|
5.3.5.1 First Case: Simulation Results and Discussion |
318 |
|
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5.3.5.2 Second Case: Simulation Results and Discussion |
321 |
|
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5.3.5.3 Performance Evaluation of the Proposed Music-Inspired Optimization Algorithms |
331 |
|
|
5.4 Conclusions |
335 |
|
|
Appendix 1: List of Abbreviations and Acronyms |
337 |
|
|
Appendix 2: List of Mathematical Symbols |
338 |
|
|
Appendix 3: Input data |
340 |
|
|
References |
346 |
|
|
Chapter 6: Power Systems Planning |
348 |
|
|
6.1 Introduction |
348 |
|
|
6.2 A Brief Review of Power System Planning Studies |
350 |
|
|
6.2.1 Why Do the Power Systems Need the Expansion Planning? |
350 |
|
|
6.2.2 A Brief Review of Power System Planning Structure |
350 |
|
|
6.2.3 Power System Planning Issues |
351 |
|
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6.2.3.1 From the Standpoint of Power System Structure |
352 |
|
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6.2.3.2 From the Standpoint of the Planning Horizon |
353 |
|
|
6.2.3.3 From the Standpoint of the Uncertainties |
354 |
|
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6.2.3.4 From the Standpoint of the Solving Algorithms |
357 |
|
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6.3 Pseudo-Dynamic Generation Expansion Planning: A Strategic Tri-level Computational-Logical Framework |
358 |
|
|
6.3.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework |
359 |
|
|
6.3.1.1 Bilateral Bidding Mechanism: First Level (Problem A) |
362 |
|
|
6.3.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B) |
362 |
|
|
6.3.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C) |
363 |
|
|
6.3.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework |
368 |
|
|
6.3.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework |
372 |
|
|
6.3.3.1 The IGDT Severe Twofold Uncertainty Model |
373 |
|
|
6.3.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function |
376 |
|
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6.3.3.3 The IGDT Risk-Taker Decision-Making Strategy: Opportunity Function |
379 |
|
|
6.3.4 Solution Method and Implementation Considerations |
382 |
|
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6.3.5 Simulation Results and Case Studies |
383 |
|
|
6.3.5.1 First Case: Simulation Results and Discussion |
387 |
|
|
6.3.5.2 Second Case: Simulation Results and Discussion |
399 |
|
|
6.3.5.3 Quantitative Verification of the Proposed IGDT Risk-Taker Decision-Making Policy in Comparison to a Robust Optimizatio... |
413 |
|
|
6.3.5.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion |
413 |
|
|
6.4 Pseudo-Dynamic Transmission Expansion Planning: A Strategic Tri-level Computational-Logical Framework |
423 |
|
|
6.4.1 Mathematical Model of the Deterministic Strategic Tri-level Computational-Logical Framework |
426 |
|
|
6.4.1.1 Bilateral Bidding Mechanism: First Level (Problem A) |
426 |
|
|
6.4.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B) |
426 |
|
|
6.4.1.3 Pseudo-Dynamic Transmission Expansion Planning: Third Level (Problem C) |
426 |
|
|
6.4.2 Overview of the Deterministic Strategic Tri-level Computational-Logical Framework |
431 |
|
|
6.4.3 Mathematical Model of the Risk-Driven Strategic Tri-level Computational-Logical Framework |
434 |
|
|
6.4.3.1 The IGDT Severe Twofold Uncertainty Model |
435 |
|
|
6.4.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function |
435 |
|
|
6.4.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function |
438 |
|
|
6.4.4 Solution Method and Implementation Considerations |
440 |
|
|
6.4.5 Simulation Results and Case Studies |
441 |
|
|
6.4.5.1 The Modified IEEE 30-Bus Test System |
443 |
|
|
6.4.5.1.1 First Case: Simulation Results and Discussion |
446 |
|
|
6.4.5.1.2 Second Case: Simulation Results and Discussion |
456 |
|
|
6.4.5.2 Large-Scale Iranian 400 kV Transmission Network |
463 |
|
|
6.4.5.2.1 First Case: Simulation Results and Discussion |
471 |
|
|
6.4.5.2.2 Second Case: Simulation Results and Discussion |
471 |
|
|
6.4.5.2.3 Investigation of the Effects of Volatility in Market Price and Demand Uncertainties |
473 |
|
|
6.4.5.2.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimi... |
478 |
|
|
6.4.5.2.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion |
481 |
|
|
6.5 Coordination of Pseudo-Dynamic Generation and Transmission Expansion Planning: A Strategic Quad-Level Computational-Logica... |
487 |
|
|
6.5.1 Mathematical Model of the Deterministic Strategic Quad-Level Computational-Logical Framework |
488 |
|
|
6.5.1.1 Bilateral Bidding Mechanism: First Level (Problem A) |
488 |
|
|
6.5.1.2 Competitive Security-Constrained Electricity Market: Second Level (Problem B) |
491 |
|
|
6.5.1.3 Pseudo-Dynamic Generation Expansion Planning: Third Level (Problem C) |
492 |
|
|
6.5.1.4 Pseudo-Dynamic Transmission Expansion Planning: Fourth Level (Problem D) |
492 |
|
|
6.5.2 Overview of the Deterministic Strategic Quad-Level Computational-Logical Framework |
492 |
|
|
6.5.3 Mathematical Model of the Risk-Driven Strategic Quad-Level Computational-Logical Framework |
500 |
|
|
6.5.3.1 The IGDT Severe Twofold Uncertainty Model |
500 |
|
|
6.5.3.2 The IGDT Risk-Averse Decision-Making Policy: Robustness Function |
500 |
|
|
6.5.3.3 The IGDT Risk-Taker Decision-Making Policy: Opportunity Function |
503 |
|
|
6.5.4 Solution Method and Implementation Considerations |
506 |
|
|
6.5.5 Simulation Results and Case Studies |
509 |
|
|
6.5.5.1 First Case: Simulation Results and Discussion |
514 |
|
|
6.5.5.2 Second Case: Simulation Results and Discussion |
517 |
|
|
6.5.5.3 Investigation into the Performance of the Proposed Framework Under the Coordinated and Uncoordinated Decisions for the... |
520 |
|
|
6.5.5.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza... |
524 |
|
|
6.5.5.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion |
527 |
|
|
6.6 Pseudo-Dynamic Open-Loop Distribution Expansion Planning: A Techno-Economic Framework |
540 |
|
|
6.6.1 Mathematical Model of the Deterministic Techno-Economic Framework |
541 |
|
|
6.6.2 Mathematical Model of the Risk-Driven Techno-Economic Framework |
553 |
|
|
6.6.2.1 The IGDT Severe Twofold Uncertainty Model |
553 |
|
|
6.6.2.2 The IGDT Risk-Averse Decision-Making Model: Robustness Function |
555 |
|
|
6.6.2.3 The IGDT Risk-Taker Decision-Making Model: Opportunity Function |
556 |
|
|
6.6.3 Solution Method and Implementation Considerations |
558 |
|
|
6.6.4 Simulation Results and Case Studies |
559 |
|
|
6.6.4.1 First Case: Simulation Results and Discussion |
563 |
|
|
6.6.4.2 Second Case: Simulation Results and Discussion |
569 |
|
|
6.6.4.3 The Impact of the Presence of Distributed Generation Resources on the Voltage Profile |
574 |
|
|
6.6.4.4 Quantitative Verification of the Proposed IGDT Risk-Averse Decision-Making Policy in Comparison to the Robust Optimiza... |
576 |
|
|
6.6.4.5 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion |
578 |
|
|
6.7 Conclusions |
589 |
|
|
Appendix 1: List of Abbreviations and Acronyms |
592 |
|
|
Appendix 2: List of Mathematical Symbols |
594 |
|
|
Appendix 3: Input Data |
607 |
|
|
References |
642 |
|
|
Chapter 7: Power Filters Planning |
647 |
|
|
7.1 Introduction |
647 |
|
|
7.2 A Brief Review of Harmonic Power Filter Planning Studies |
649 |
|
|
7.2.1 Nonlinear Loads and Their Malicious Effects |
650 |
|
|
7.2.2 Harmonic Power Filters |
651 |
|
|
7.2.3 Harmonic Power Flow |
653 |
|
|
7.2.4 Harmonic Power Filter Planning Problem |
654 |
|
|
7.3 Hybrid Harmonic Power Filter Planning: A Techno-economic Framework |
655 |
|
|
7.3.1 Mathematical Model of the Techno-economic Multi-objective Framework |
656 |
|
|
7.3.1.1 Deterministic Decoupled Harmonic Power Flow Methodology |
659 |
|
|
7.3.1.2 Passive and Active Harmonic Power Filters |
666 |
|
|
7.3.1.3 Hybrid Harmonic Power Filter Planning Problem |
670 |
|
|
7.3.1.4 Probabilistic Decoupled Harmonic Power Flow Methodology |
677 |
|
|
7.3.2 Solution Method and Implementation Considerations |
681 |
|
|
7.3.3 Simulation Results and Case Studies |
681 |
|
|
7.3.3.1 IEEE 18-Bus Distorted Test Network |
682 |
|
|
7.3.3.1.1 First Case: Simulation Results and Discussion |
687 |
|
|
7.3.3.1.2 Second Case: Simulation Results and Discussion |
692 |
|
|
7.3.3.1.3 Third Case: Simulation Results and Discussion |
695 |
|
|
7.3.3.1.4 Investigation of Passive Harmonic Power Filter Performance |
700 |
|
|
7.3.3.2 The 34-Bus Distribution Test Network |
701 |
|
|
7.3.3.2.1 First Case: Simulation Results and Discussion |
704 |
|
|
7.3.3.2.2 Second Case: Simulation Results and Discussion |
705 |
|
|
7.3.3.2.3 Third Case: Simulation Results and Discussion |
706 |
|
|
7.3.3.2.4 Performance Evaluation of the Proposed Optimization Algorithms: Simulation Results and Discussion |
709 |
|
|
7.4 Conclusions |
717 |
|
|
Appendix 1: List of Abbreviations and Acronyms |
720 |
|
|
Appendix 2: List of Mathematical Symbols |
721 |
|
|
Appendix 3: Input Data |
727 |
|
|
References |
735 |
|
|
Index |
737 |
|