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E-raamat: Applications of Modern Heuristic Optimization Methods in Power and Energy Systems [Wiley Online]

Edited by , Edited by (Pennsylvania State University)
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Applications of Modern Heuristic Optimization Methods in Power and Energy SystemsSuurem pilt
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Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119602286

Reviews state-of-the-art technologies in modern heuristic optimization techniques and presents case studies showing how they have been applied in complex power and energy systems problems

Written by a team of international experts, this book describes the use of metaheuristic applications in the analysis and design of electric power systems. This includes a discussion of optimum energy and commitment of generation (nonrenewable & renewable) and load resources during day-to-day operations and control activities in regulated and competitive market structures, along with transmission and distribution systems.

Applications of Modern Heuristic Optimization Methods in Power and Energy Systems begins with an introduction and overview of applications in power and energy systems before moving on to planning and operation, control, and distribution. Further chapters cover the integration of renewable energy and the smart grid and electricity markets. The book finishes with final conclusions drawn by the editors.

Applications of Modern Heuristic Optimization Methods in Power and Energy Systems:

  • Explains the application of differential evolution in electric power systems' active power multi-objective optimal dispatch
  • Includes studies of optimization and stability in load frequency control in modern power systems
  • Describes optimal compliance of reactive power requirements in near-shore wind power plants
  • Features contributions from noted experts in the field

Ideal for power and energy systems designers, planners, operators, and consultants, Applications of Modern Heuristic Optimization Methods in Power and Energy Systems will also benefit engineers, software developers, researchers, academics, and students.

Preface xv
Contributors xvii
List Of Figures xxi
List Of Tables xxxiii
Chapter 1 Introduction 1(20)
1.1 Background
1(2)
1.2 Evolutionary Computation: A Successful Branch of CI
3(12)
1.2.1 Genetic Algorithm
6(2)
1.2.2 Non-dominated Sorting Genetic Algorithm II
8(1)
1.2.3 Evolution Strategies and Evolutionary Programming
8(1)
1.2.4 Simulated Annealing
9(1)
1.2.5 Particle Swarm Optimization
10(1)
1.2.6 Quantum Particle Swarm Optimization
10(1)
1.2.7 Multi-objective Particle Swarm Optimization
11(1)
1.2.8 Particle Swarm Optimization Variants
12(1)
1.2.9 Artificial Bee Colony
13(1)
1.2.10 Tabu Search
14(1)
References
15(6)
Chapter 2 Overview Of Applications In Power And Energy Systems 21(18)
2.1 Applications to Power Systems
21(7)
2.1.1 Unit Commitment
23(1)
2.1.2 Economic Dispatch
24(1)
2.1.3 Forecasting in Power Systems
25(2)
2.1.4 Other Applications in Power Systems
27(1)
2.2 Smart Grid Application Competition Series
28(4)
2.2.1 Problem Description
29(1)
2.2.2 Best Algorithms and Ranks
30(2)
2.2.3 Further Information and How to Download
32(1)
References
32(7)
Chapter 3 Power System Planning And Operation 39(188)
3.1 Introduction
39(1)
3.2 Unit Commitment
40(16)
3.2.1 Introduction
40(1)
3.2.2 Problem Formulation
40(2)
3.2.3 Advancement in UCP Formulations and Models
42(4)
3.2.4 Solution Methodologies, State-of-the-Art, History, and Evolution
46(10)
3.2.5 Conclusions
56(1)
3.3 Economic Dispatch Based on Genetic Algorithms and Particle Swarm Optimization
56(31)
3.3.1 Introduction s
s6
3.3.2 Fundamentals of Genetic Algorithms and Particle Swarm Optimization
58(2)
3.3.3 Economic Dispatch Problem
60(3)
3.3.4 GA Implementation to ED
63(8)
3.3.5 PSO Implementation to ED
71(8)
3.3.6 Numerical Example
79(8)
3.3.7 Conclusions
87(1)
3.4 Differential Evolution in Active Power Multi-Objective Optimal Dispatch
87(19)
3.4.1 Introduction
87(1)
3.4.2 Differential Evolution for Multi-Objective Optimization
88(9)
3.4.3 Multi-Objective Model of Active Power Optimization for Wind Power Integrated Systems
97(3)
3.4.4 Case Studies
100(5)
3.4.5 Analyses of Dispatch Plan
105(1)
3.4.6 Conclusions
106(1)
3.5 Hydrothermal Coordination
106(9)
3.5.1 Introduction
106(1)
3.5.2 Hydrothermal Coordination Formulation
107(3)
3.5.3 Problem Decomposition
110(4)
3.5.4 Case Studies
3.5.5 Conclusions
114(1)
3.6 Meta-Heuristic Method for Gms Based on Genetic Algorithm
115(28)
3.6.1 History
115(1)
3.6.2 Meta-heuristic Search Method
116(3)
3.6.3 Flexible GMS
119(12)
3.6.4 User-Friendly GMS System
131(10)
3.6.5 Conclusion
141(2)
3.7 Load Flow
143(18)
3.7.1 Introduction
143(1)
3.7.2 Load Flow Analysis in Electrical Power Systems
144(4)
3.7.3 Particle Swarm Optimization and Mutation Operation
148(2)
3.7.4 Load Flow Computation via Particle Swarm Optimization with Mutation Operation
150(3)
3.7.5 Numerical Results
153(7)
3.7.6 Conclusions
160(1)
3.8 Artificial Bee Colony Algorithm for Solving Optimal Power Flow
161(15)
3.8.1 Optimization in Power System Operation
162(1)
3.8.2 The Optimal Power Flow Problem
162(4)
3.8.3 Artificial Bee Colony
166(2)
3.8.4 ABC for the OPF Problem
168(2)
3.8.5 Case Studies
170(6)
3.8.6 Conclusions
176(1)
3.9 OPF Test Bed and Performance Evaluation of Modem Heuristic Optimization
176(21)
3.9.1 Introduction
176(1)
3.9.2 Problem Definition
177(1)
3.9.3 OPF Test Systems
178(5)
3.9.4 Differential Evolutionary Particle Swarm Optimization: DEEPSO
183(4)
3.9.5 Enhanced Version of Mean-Variance Mapping Optimization Algorithm: MVMO-PHM
187(6)
3.9.6 Evaluation Results
193(3)
3.9.7 Conclusions
196(1)
3.10 Transmission System Expansion Planning
197(13)
3.10.1 Introduction
197(1)
3.10.2 Transmission System Expansion Planning Models
198(1)
3.10.3 Mathematical Modeling
199(2)
3.10.4 Challenges
201(1)
3.10.5 Application of Meta-heuristics to TEP
202(8)
3.10.6 Conclusions
210(1)
3.11 Conclusion
210(1)
References
210(17)
Chapter 4 Power System And Power Plant Control 227(154)
4.1 Introduction
227(1)
4.2 Load Frequency Control - Optimization and Stability
228(16)
4.2.1 Introduction
228(1)
4.2.2 Load Frequency Control
229(1)
4.2.3 Components of Active Power Control System
230(2)
4.2.4 Designing LFC Structure for an Interconnected Power System
232(5)
4.2.5 Parameter Optimization and System Performance
237(5)
4.2.6 System Stability in the Presence of Communication Delay
242(2)
4.2.7 Conclusions
244(1)
4.3 Control of Facts Devices
244(40)
4.3.1 Introduction
244(2)
4.3.2 Role of FACTS
246(1)
4.3.3 Static Modeling of FACTS devices
247(8)
4.3.4 Power Flow Control using FACTS
255(4)
4.3.5 Optimal Power Flow Using Suitability FACTS devices
259(22)
4.3.6 Use of Particle Swarm Optimization
281(2)
4.3.7 Conclusions
283(1)
4.4 Hybrid of Analytical and Heuristic Techniques for facts Devices
284(21)
4.4.1 Introduction
284(1)
4.4.2 Heuristic Algorithms
285(3)
4.4.3 SVC and Voltage Instability Improvement
288(5)
4.4.4 FACTS Devices and Angle Stability Improvement
293(2)
4.4.5 Selection of Supplementary Input Signals for Damping Inter-area Oscillations
295(7)
4.4.6 TCSC and Improvement of Total Transfer Capability
302(3)
4.4.7 Conclusions
305(1)
4.5 Power System Automation
305(29)
4.5.1 Introduction
305(2)
4.5.2 Application of PSO on Power System's Corrective Control
307(15)
4.5.3 Genetic Algorithm-aided DTs for Load Shedding
322(2)
4.5.4 Power System-Controlled Islanding
324(2)
4.5.5 Application of the method on the IEEE - 30 buses test system
326(1)
4.5.6 Application of the method on the IEEE - 118 buses test system
327(1)
4.5.7 Conclusions
327(1)
4.5.8 Appendix
328(6)
4.6 Power Plant Control
334(21)
4.6.1 Introduction
334(1)
4.6.2 Coal Mill Modeling
335(5)
4.6.3 Nonlinear Model Predictive Control of Reheater Steam Temperature
340(5)
4.6.4 Multi-objective Optimization of Boiler Combustion System
345(10)
4.6.5 Conclusions
355(1)
4.7 Predictive Control in Large-Scale Power Plant
355(13)
4.7.1 Introduction
355(1)
4.7.2 Particle Swarm Optimization Algorithm
356(1)
4.7.3 Performance Prediction Model Development Based on NARMA Model
357(4)
4.7.4 Design of Intelligent MPOC Scheme
361(3)
4.7.5 Control Simulation Tests
364(3)
4.7.6 Conclusions
367(1)
4.8 Conclusion
368(1)
References
369(12)
Chapter 5 Distribution System 381(232)
5.1 Introduction
381(1)
5.2 Active Distribution Network Planning
382(10)
5.2.1 Introduction
382(1)
5.2.2 Problem Formulation
382(3)
5.2.3 Overview of the Solution Techniques for Distribution Network Planning
385(1)
5.2.4 Genetic Algorithm Solution to Active Distribution Network Planning Problem
385(3)
5.2.5 Numerical Results
388(4)
5.2.6 Conclusions
392(1)
5.3 Optimal Selection of Distribution System Architecture
392(26)
5.3.1 Introduction
392(1)
5.3.2 Deterministic Optimization Techniques
393(1)
5.3.3 Stochastic Optimization Techniques
394(6)
5.3.4 Multi-Objective Optimization
400(1)
5.3.5 Mathematical Modeling for Power System Components
401(8)
5.3.6 AC/DC Power Flow in Hybrid Networks
409(1)
5.3.7 Pareto-Based Multi-Objective Optimization Problem
409(9)
5.4 Conservation Voltage Reduction Planning
418(9)
5.4.1 Introduction
418(1)
5.4.2 Conservation Voltage Reduction
418(2)
5.4.3 CVR Based on PSO
420(3)
5.4.4 CVR Based on AHP
423(1)
5.4.5 Case Studies for CVR in Korean Power System
424(3)
5.4.6 Conclusion
427(1)
5.5 Dynamic Distribution Network Expansion Planning with Demand Side Management
427(40)
5.5.1 Introduction
427(4)
5.5.2 Expansion Options
431(5)
5.5.3 Problem Formulation
436(6)
5.5.4 Optimization Algorithm
442(8)
5.5.5 Case Studies
450(10)
5.5.6 Conclusions
460(7)
5.6 GA-Guided Trust-Tech Methodology for Capacitor Placement in Distribution Systems
467(22)
5.6.1 Introduction
467(2)
5.6.2 Overview of the Trust-Tech Method
469(3)
5.6.3 Computing Tier-One Local Optimal Solutions
472(2)
5.6.4 The GA-Guided Trust-Tech Method
474(4)
5.6.5 Applications to Capacitor Placement Problems
478(3)
5.6.6 Numerical Study
481(7)
5.6.7 Conclusions
488(1)
5.7 Network Reconfiguration
489(21)
5.7.1 Introduction
489(1)
5.7.2 Modem Distribution Systems: A Concept
490(3)
5.7.3 Distribution System Reconfiguration
493(3)
5.7.4 Distribution System Service Restoration
496(5)
5.7.5 Multi-Agent System for Distribution System Reconfiguration
501(9)
5.7.6 Conclusions
510(1)
5.8 Distribution System Restoration
510(21)
5.8.1 Introduction
510(1)
5.8.2 Power System Restoration Process
511(20)
5.9 Group-based PSO for System Restoration
531(22)
5.9.1 Introduction
531(2)
5.9.2 Group-Based PSO Method
533(6)
5.9.3 Overview of the Service Restoration Problem
539(3)
5.9.4 Application to the Service Restoration Problem
542(3)
5.9.5 Numerical Results
545(7)
5.9.6 Conclusions
552(1)
5.10 MVMO for Parameter Identification of Dynamic Equivalents for Active Distribution Networks
553(20)
5.10.1 Introduction
553(1)
5.10.2 Active Distribution System
553(1)
5.10.3 Need for Aggregation and the Concept of Dynamic Equivalents
554(2)
5.10.4 Proposed Approach with MVMO
556(2)
5.10.5 Adaptation of MVMO for Identification Problem
558(4)
5.10.6 Case Study
562(6)
5.10.7 Application to Test Case
568(1)
5.10.8 Analysis
569(3)
5.10.9 Reflections
572(1)
5.10.10 Conclusions
572(1)
5.11 Parameter Estimation of Circuit Model for Distribution Transformers
573(17)
5.11.1 Introduction
573(1)
5.11.2 Transformer Winding Equivalent Circuit
574(2)
5.11.3 Signal Comparison Indicators
576(2)
5.11.4 Coefficients Estimation Using Heuristic Optimization
578(4)
5.11.5 Coefficients Estimation Results and Conclusion
582(4)
5.11.6 Conclusions
586(4)
References
590(23)
Chapter 6 Integration Of Renewable Energy In Smart Grid 613(162)
6.1 Introduction
613(1)
6.2 Renewable Energy Sources
613(22)
6.2.1 Renewable Energy Sources Management Overview
613(2)
6.2.2 Energy Resource Scheduling - Problem Formulation
615(2)
6.2.3 Energy Resources Scheduling - Particle Swarm Optimization
617(1)
6.2.4 Energy Resources Scheduling - Simulated Annealing
618(3)
6.2.5 Practical Case Study
621(11)
6.2.6 Appendix
632(2)
6.2.7 Conclusions
634(1)
6.3 Operation and Control of Smart Grid
635(10)
6.3.1 Introduction
635(1)
6.3.2 Problems for Systems Configuration or Systems Design
636(2)
6.3.3 Systems Operation and Systems Control
638(2)
6.3.4 System's Management
640(5)
6.3.5 Conclusion
645(1)
6.4 Compliance of Reactive Power Requirements in Wind Power Plants
645(22)
6.4.1 Introduction
645(1)
6.4.2 Problem Definition
646(2)
6.4.3 NN-Based Wind Speed Forecasting Method
648(2)
6.4.4 Mean Variance Mapping Optimization Algorithm
650(4)
6.4.5 Case Studies
654(11)
6.4.6 Conclusions
665(2)
6.5 Photovoltaic Controller Design
667(13)
6.5.1 Introduction
667(1)
6.5.2 Maximum Power Point Tracking in PV System
668(6)
6.5.3 Particle Swarm Optimization
674(1)
6.5.4 Application of Particle Swarm Optimization in MPPT
674(2)
6.5.5 Illustration of PSO Technique for MPPT During Different Irradiance Conditions
676(2)
6.5.6 Conclusion
678(2)
6.6 Demand Side Management and Demand Response
680(11)
6.6.1 Introduction
680(3)
6.6.2 Methodology for Consumption Shifting and Generation Scheduling
683(2)
6.6.3 Quantum PSO
685(2)
6.6.4 Numeric Example
687(4)
6.6.5 Conclusions
691(1)
6.7 EPSO-Based Solar Power Forecasting
691(13)
6.7.1 Introduction
691(2)
6.7.2 General Radial Basis Function Network
693(2)
6.7.3 k-Means
695(1)
6.7.4 Deterministic Annealing Clustering
695(2)
6.7.5 Evolutionary Particle Swarm Optimization
697(1)
6.7.6 Hybrid Intelligent Method
698(1)
6.7.7 Case Studies
699(5)
6.7.8 Conclusion
704(1)
6.8 Load Demand and Solar Generation Forecast for PV Integrated Smart Buildings
704(25)
6.8.1 Introduction
704(10)
6.8.2 Literature Review of Forecasting Techniques
714(3)
6.8.3 Ensemble Forecast Methodology for Load Demand and PV Output Power
717(5)
6.8.4 Numerical Results and Discussion
722(6)
6.8.5 Conclusions
728(1)
6.9 Multi-Objective Planning of Public Electric Vehicle Charging Stations
729(12)
6.9.1 Introduction
729(1)
6.9.2 Multi-Objective Electric Vehicle Charging Station Layout Planning Model
730(3)
6.9.3 An Improved SPEA2 for Solving EVCSLP Problem
733(4)
6.9.4 Case Study
737(3)
6.9.5 Conclusion
740(1)
6.10 Dispatch Modeling Incorporating Maneuver Components, Wind Power, and Electric Vehicles
741(16)
6.10.1 Introduction
741(2)
6.10.2 Proposed Economic Dispatch Formulation
743(8)
6.10.3 Population-Based Optimization Algorithms
751(2)
6.10.4 Test System and Results Analysis
753(3)
6.10.5 Conclusion
756(1)
6.11 Conclusions
757(1)
References
757(18)
Chapter 7 Electricity Markets 775(44)
7.1 Introduction
775(2)
7.2 Bidding Strategies
777(4)
7.2.1 Introduction
777(2)
7.2.2 Context Analysis
779(1)
7.2.3 Strategic Bidding
780(1)
7.3 Market Analysis and Clearing
781(12)
7.3.1 Introduction
781(1)
7.3.2 Electricity Market Simulators
782(3)
7.3.3 Didactic Example
785(8)
7.4 Electricity Market Forecasting
793(5)
7.4.1 Introduction
793(1)
7.4.2 Artificial Neural Networks for Electricity Market Price Forecasting
794(1)
7.4.3 Support Vector Machines for Electricity Market Price Forecasting
795(1)
7.4.4 Illustrative Results
796(2)
7.5 Simultaneous Bidding of V2G In Ancillary Service Markets Using Fuzzy Optimization
798(14)
7.5.1 Introduction
798(1)
7.5.2 Fuzzy Optimization
799(2)
7.5.3 FO-based Simultaneous Bidding of Ancillary Services Using V2G
801(5)
7.5.4 Case Study
806(1)
7.5.5 Results and Discussions
807(4)
7.5.6 Conclusion
811(1)
7.6 Conclusions
812(1)
References
812(7)
Index 819
KWANG Y. LEE, PhD, is a Professor and Chair of Electrical and Computer Engineering at Baylor University. He is active in the Intelligent Systems Subcommittee and Station Control Subcommittee of the IEEE Power and Energy Society. He served as Editor of IEEE Transactions on Energy Conversion and Associate Editor of IEEE Transactions on Neural Networks and IFAC Journal on Control Engineering Practice.

ZITA A. VALE, PhD, is a Full Professor in the Electrical Engineering Department at the School of Engineering of the Polytechnic of Porto and Director of GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development. She has published over 800 works, including more than 100 papers in international scientific journals.
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