Applications of modern heuristic optimization methods in power and energy systems

COVER......Page 1
TITLE PAGE......Page 5
COPYRIGHT PAGE......Page 6
CONTENTS......Page 9
PREFACE......Page 17
CONTRIBUTORS......Page 19
LIST OF FIGURES......Page 23
LIST OF TABLES......Page 35
ABOUT THE EDITORS......Page 41
 1.1 BACKGROUND......Page 43
 1.2 EVOLUTIONARY COMPUTATION: A SUCCESSFUL BRANCH OF CI......Page 45
  1.2.1 Genetic Algorithm......Page 48
  1.2.3 Evolution Strategies and Evolutionary Programming......Page 50
  1.2.4 Simulated Annealing......Page 51
  1.2.6 Quantum Particle Swarm Optimization......Page 52
  1.2.7 Multi-objective Particle Swarm Optimization......Page 53
  1.2.8 Particle Swarm Optimization Variants......Page 54
  1.2.9 Artificial Bee Colony......Page 55
  1.2.10 Tabu Search......Page 56
 REFERENCES......Page 57
 2.1 APPLICATIONS TO POWER SYSTEMS......Page 63
  2.1.1 Unit Commitment......Page 65
  2.1.2 Economic Dispatch......Page 66
  2.1.3 Forecasting in Power Systems......Page 67
  2.1.4 Other Applications in Power Systems......Page 69
 2.2 SMART GRID APPLICATION COMPETITION SERIES......Page 70
  2.2.1 Problem Description......Page 71
  2.2.2 Best Algorithms and Ranks......Page 72
REFERENCES......Page 74
 3.1 INTRODUCTION......Page 81
  3.2.2 Problem Formulation......Page 82
  3.2.3 Advancement in UCP Formulations and Models......Page 84
  3.2.4 Solution Methodologies, State-of-the-Art, History, and Evolution......Page 88
  3.3.1 Introduction......Page 98
  3.3.2 Fundamentals of Genetic Algorithms and Particle Swarm Optimization......Page 100
  3.3.3 Economic Dispatch Problem......Page 102
  3.3.4 GA Implementation to ED......Page 105
  3.3.5 PSO Implementation to ED......Page 113
  3.3.6 Numerical Example......Page 121
  3.4.1 Introduction......Page 129
  3.4.2 Differential Evolution for Multi-Objective Optimization......Page 130
  3.4.3 Multi-Objective Model of Active Power Optimization for Wind Power Integrated Systems......Page 139
  3.4.4 Case Studies......Page 142
  3.4.5 Analyses of Dispatch Plan......Page 147
  3.5.1 Introduction......Page 148
  3.5.2 Hydrothermal Coordination Formulation......Page 149
  3.5.3 Problem Decomposition......Page 152
  3.5.4 Case Studies......Page 153
  3.5.5 Conclusions......Page 156
  3.6.1 History......Page 157
  3.6.2 Meta-heuristic Search Method......Page 158
  3.6.3 Flexible GMS......Page 161
  3.6.4 User-Friendly GMS System......Page 173
  3.6.5 Conclusion......Page 183
  3.7.1 Introduction......Page 185
  3.7.2 Load Flow Analysis in Electrical Power Systems......Page 186
  3.7.3 Particle Swarm Optimization and Mutation Operation......Page 190
  3.7.4 Load Flow Computation via Particle Swarm Optimization with Mutation Operation......Page 192
  3.7.5 Numerical Results......Page 195
  3.7.6 Conclusions......Page 202
 3.8 ARTIFICIAL BEE COLONY ALGORITHM FOR SOLVING OPTIMAL POWER FLOW......Page 203
  3.8.2 The Optimal Power Flow Problem......Page 204
  3.8.3 Artificial Bee Colony......Page 208
  3.8.4 ABC for the OPF Problem......Page 210
  3.8.5 Case Studies......Page 212
  3.9.1 Introduction......Page 218
  3.9.2 Problem Definition......Page 219
  3.9.3 OPF Test Systems......Page 220
  3.9.4 Differential Evolutionary Particle Swarm Optimization: DEEPSO......Page 225
  3.9.5 Enhanced Version of Mean–Variance Mapping Optimization Algorithm: MVMO-PHM......Page 229
  3.9.6 Evaluation Results......Page 235
  3.9.7 Conclusions......Page 238
  3.10.1 Introduction......Page 239
  3.10.2 Transmission System Expansion Planning Models......Page 240
  3.10.3 Mathematical Modeling......Page 241
  3.10.4 Challenges......Page 243
  3.10.5 Application of Meta-heuristics to TEP......Page 244
 REFERENCES......Page 252
 4.1 INTRODUCTION......Page 269
  4.2.1 Introduction......Page 270
  4.2.2 Load Frequency Control......Page 271
  4.2.3 Components of Active Power Control System......Page 272
  4.2.4 Designing LFC Structure for an Interconnected Power System......Page 274
  4.2.5 Parameter Optimization and System Performance......Page 279
  4.2.6 System Stability in the Presence of Communication Delay......Page 284
  4.5.1 Introduction......Page 286
  4.3.2 Role of FACTS......Page 288
  4.3.3 Static Modeling of FACTS devices......Page 289
  4.3.4 Power Flow Control using FACTS......Page 297
  4.3.5 Optimal Power Flow Using Suitability FACTS devices......Page 301
  4.3.6 Use of Particle Swarm Optimization......Page 323
  4.3.7 Conclusions......Page 325
  4.4.1 Introduction......Page 326
  4.4.2 Heuristic Algorithms......Page 327
  4.4.3 SVC and Voltage Instability Improvement......Page 330
  4.4.4 FACTS Devices and Angle Stability Improvement......Page 335
  4.4.5 Selection of Supplementary Input Signals for Damping Inter-area Oscillations......Page 337
  4.4.6 TCSC and Improvement of Total Transfer Capability......Page 344
 4.5 POWER SYSTEM AUTOMATION......Page 347
  4.5.2 Application of PSO on Power System's Corrective Control......Page 349
  4.5.3 Genetic Algorithm-aided DTs for Load Shedding......Page 364
  4.5.4 Power System-Controlled Islanding......Page 366
  4.5.5 Application of the method on the IEEE – 30 buses test system......Page 368
  4.5.7 Conclusions......Page 369
  4.5.8 Appendix......Page 370
  4.6.1 Introduction......Page 376
  4.6.2 Coal Mill Modeling......Page 377
  4.6.3 Nonlinear Model Predictive Control of Reheater Steam Temperature......Page 382
  4.6.4 Multi-objective Optimization of Boiler Combustion System......Page 387
  4.7.1 Introduction......Page 397
  4.7.2 Particle Swarm Optimization Algorithm......Page 398
  4.7.3 Performance Prediction Model Development Based on NARMA Model......Page 399
  4.7.4 Design of Intelligent MPOC Scheme......Page 403
  4.7.5 Control Simulation Tests......Page 406
  4.7.6 Conclusions......Page 409
 4.8 CONCLUSION......Page 410
 REFERENCES......Page 411
 5.1 INTRODUCTION......Page 423
  5.2.2 Problem Formulation......Page 424
  5.2.4 Genetic Algorithm Solution to Active Distribution Network Planning Problem......Page 427
  5.2.5 Numerical Results......Page 430
  5.3.1 Introduction......Page 434
  5.3.2 Deterministic Optimization Techniques......Page 435
  5.3.3 Stochastic Optimization Techniques......Page 436
  5.3.4 Multi-Objective Optimization......Page 442
  5.3.5 Mathematical Modeling for Power System Components......Page 443
  5.3.6 AC/DC Power Flow in Hybrid Networks......Page 447
  5.3.7 Pareto-Based Multi-Objective Optimization Problem......Page 451
  5.4.2 Conservation Voltage Reduction......Page 460
  5.4.3 CVR Based on PSO......Page 462
  5.4.4 CVR Based on AHP......Page 465
  5.4.5 Case Studies for CVR in Korean Power System......Page 466
  5.5.1 Introduction......Page 469
  5.5.2 Expansion Options......Page 473
  5.5.3 Problem Formulation......Page 478
  5.5.4 Optimization Algorithm......Page 484
  5.5.5 Case Studies......Page 492
  5.5.6 Conclusions......Page 502
  5.6.1 Introduction......Page 509
  5.6.2 Overview of the Trust-Tech Method......Page 511
  5.6.3 Computing Tier-One Local Optimal Solutions......Page 514
  5.6.4 The GA-Guided Trust-Tech Method......Page 516
  5.6.5 Applications to Capacitor Placement Problems......Page 520
  5.6.6 Numerical Study......Page 523
  5.6.7 Conclusions......Page 530
  5.7.1 Introduction......Page 531
  5.7.2 Modern Distribution Systems: A Concept......Page 532
  5.7.3 Distribution System Reconfiguration......Page 535
  5.7.4 Distribution System Service Restoration......Page 538
  5.7.5 Multi-Agent System for Distribution System Reconfiguration......Page 543
  5.8.1 Introduction......Page 552
  5.8.2 Power System Restoration Process......Page 553
  5.9.1 Introduction......Page 573
  5.9.2 Group-Based PSO Method......Page 575
  5.9.3 Overview of the Service Restoration Problem......Page 581
  5.9.4 Application to the Service Restoration Problem......Page 584
  5.9.5 Numerical Results......Page 587
  5.9.6 Conclusions......Page 594
  5.10.2 Active Distribution System......Page 595
  5.10.3 Need for Aggregation and the Concept of Dynamic Equivalents......Page 596
  5.10.4 Proposed Approach with MVMO......Page 598
  5.10.5 Adaptation of MVMO for Identification Problem......Page 600
  5.10.6 Case Study......Page 604
  5.10.7 Application to Test Case......Page 610
  5.10.8 Analysis......Page 611
  5.10.10 Conclusions......Page 614
  5.11.1 Introduction......Page 615
  5.11.2 Transformer Winding Equivalent Circuit......Page 616
  5.11.3 Signal Comparison Indicators......Page 618
  5.11.4 Coefficients Estimation Using Heuristic Optimization......Page 620
  5.11.5 Coefficients Estimation Results and Conclusion......Page 624
  5.11.6 Conclusions......Page 628
 REFERENCES......Page 632
  6.2.1 Renewable Energy Sources Management Overview......Page 655
  6.2.2 Energy Resource Scheduling – Problem Formulation......Page 657
  6.2.3 Energy Resources Scheduling – Particle Swarm Optimization......Page 659
  6.2.4 Energy Resources Scheduling – Simulated Annealing......Page 660
  6.2.5 Practical Case Study......Page 663
  6.2.6 Appendix......Page 674
  6.2.7 Conclusions......Page 676
  6.3.1 Introduction......Page 677
  6.3.2 Problems for Systems Configuration or Systems Design......Page 678
  6.3.3 Systems Operation and Systems Control......Page 680
  6.3.4 System's Management......Page 682
  6.4.1 Introduction......Page 687
  6.4.2 Problem Definition......Page 688
  6.4.3 NN-Based Wind Speed Forecasting Method......Page 690
  6.4.4 Mean Variance Mapping Optimization Algorithm......Page 692
  6.4.5 Case Studies......Page 696
  6.4.6 Conclusions......Page 707
  6.5.1 Introduction......Page 709
  6.5.2 Maximum Power Point Tracking in PV System......Page 710
  6.5.4 Application of Particle Swarm Optimization in MPPT......Page 716
  6.5.5 Illustration of PSO Technique for MPPT During Different Irradiance Conditions......Page 718
  6.5.6 Conclusion......Page 720
  6.6.1 Introduction......Page 722
  6.6.2 Methodology for Consumption Shifting and Generation Scheduling......Page 725
  6.6.3 Quantum PSO......Page 727
  6.6.4 Numeric Example......Page 729
  6.7.1 Introduction......Page 733
  6.7.2 General Radial Basis Function Network......Page 735
  6.7.4 Deterministic Annealing Clustering......Page 737
  6.7.5 Evolutionary Particle Swarm Optimization......Page 739
  6.7.6 Hybrid Intelligent Method......Page 740
  6.7.7 Case Studies......Page 741
  6.8.1 Introduction......Page 746
  6.8.2 Literature Review of Forecasting Techniques......Page 756
  6.8.3 Ensemble Forecast Methodology for Load Demand and PV Output Power......Page 759
  6.8.4 Numerical Results and Discussion......Page 764
  6.8.5 Conclusions......Page 770
  6.9.1 Introduction......Page 771
  6.9.2 Multi-Objective Electric Vehicle Charging Station Layout Planning Model......Page 772
  6.9.3 An Improved SPEA2 for Solving EVCSLP Problem......Page 775
  6.9.4 Case Study......Page 779
  6.9.5 Conclusion......Page 782
  6.10.1 Introduction......Page 783
  6.10.2 Proposed Economic Dispatch Formulation......Page 785
  6.10.3 Population-Based Optimization Algorithms......Page 793
  6.10.4 Test System and Results Analysis......Page 795
  6.10.5 Conclusion......Page 798
 REFERENCES......Page 799
 7.1 INTRODUCTION......Page 817
  7.2.1 Introduction......Page 819
  7.2.2 Context Analysis......Page 821
  7.2.3 Strategic Bidding......Page 822
  7.3.1 Introduction......Page 823
  7.3.2 Electricity Market Simulators......Page 824
  7.3.3 Didactic Example......Page 827
  7.4.1 Introduction......Page 835
  7.4.2 Artificial Neural Networks for Electricity Market Price Forecasting......Page 836
  7.4.3 Support Vector Machines for Electricity Market Price Forecasting......Page 837
  7.4.4 Illustrative Results......Page 838
  7.5.1 Introduction......Page 840
  7.5.2 Fuzzy Optimization......Page 841
  7.5.3 FO-based Simultaneous Bidding of Ancillary Services Using V2G......Page 843
  7.5.4 Case Study......Page 848
  7.5.5 Results and Discussions......Page 849
  7.5.6 Conclusion......Page 853
 REFERENCES......Page 854
INDEX......Page 861
IEEE PRESS SERIES ON POWER ENGINEERING......Page 881
EULA......Page 888