Applications of Modern Heuristic Optimization Methods in Power and Energy Systems

Inbunden, Engelska, 2020

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Beskrivning

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 dispatchIncludes studies of optimization and stability in load frequency control in modern power systemsDescribes optimal compliance of reactive power requirements in near-shore wind power plantsFeatures contributions from noted experts in the fieldIdeal 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.

Produktinformation

Utgivningsdatum:2020-04-17
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Serie:IEEE Press Series on Power and Energy Systems
Antal sidor:896
Förlag:John Wiley & Sons Inc
ISBN:9781119602293

Utforska kategorier

Energiteknik inom Naturvetenskap och teknik

Mer om författaren

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 GECADResearch 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.

Innehållsförteckning

Preface xvContributors xviiList of Figures xxiList of Tables xxxiiiChapter 1 Introduction 11.1 Background 11.2 Evolutionary Computation: A Successful Branch of CI 31.2.1 Genetic Algorithm 61.2.2 Non-dominated Sorting Genetic Algorithm II 81.2.3 Evolution Strategies and Evolutionary Programming 81.2.4 Simulated Annealing 91.2.5 Particle Swarm Optimization 101.2.6 Quantum Particle Swarm Optimization 101.2.7 Multi-objective Particle Swarm Optimization 111.2.8 Particle Swarm Optimization Variants 121.2.9 Artificial Bee Colony 131.2.10 Tabu Search 14References 15Chapter 2 Overview Of Applications In Power And Energy Systems 212.1 Applications to Power Systems 212.1.1 Unit Commitment 232.1.2 Economic Dispatch 242.1.3 Forecasting in Power Systems 252.1.4 Other Applications in Power Systems 272.2 Smart Grid Application Competition Series 282.2.1 Problem Description 292.2.2 Best Algorithms and Ranks 302.2.3 Further Information and How to Download 32References 32Chapter 3 Power System Planning And Operation 393.1 Introduction 393.2 Unit Commitment 403.2.1 Introduction 403.2.2 Problem Formulation 403.2.3 Advancement in UCP Formulations and Models 423.2.4 Solution Methodologies, State-of-the-Art, History, and Evolution 463.2.5 Conclusions 563.3 Economic Dispatch Based on Genetic Algorithms and Particle Swarm Optimization 563.3.1 Introduction 563.3.2 Fundamentals of Genetic Algorithms and Particle Swarm Optimization 583.3.3 Economic Dispatch Problem 603.3.4 GA Implementation to ED 633.3.5 PSO Implementation to ED 713.3.6 Numerical Example 793.3.7 Conclusions 873.4 Differential Evolution in Active Power Multi-Objective Optimal Dispatch 873.4.1 Introduction 873.4.2 Differential Evolution for Multi-Objective Optimization 883.4.3 Multi-Objective Model of Active Power Optimization for Wind Power Integrated Systems 973.4.4 Case Studies 1003.4.5 Analyses of Dispatch Plan 1053.4.6 Conclusions 1063.5 Hydrothermal Coordination 1063.5.1 Introduction 1063.5.2 Hydrothermal Coordination Formulation 1073.5.3 Problem Decomposition 1103.5.4 Case Studies 1113.5.5 Conclusions 1143.6 Meta-Heuristic Method for Gms Based on Genetic Algorithm 1153.6.1 History 1153.6.2 Meta-heuristic Search Method 1163.6.3 Flexible GMS 1193.6.4 User-Friendly GMS System 1313.6.5 Conclusion 1413.7 Load Flow 1433.7.1 Introduction 1433.7.2 Load Flow Analysis in Electrical Power Systems 1443.7.3 Particle Swarm Optimization and Mutation Operation 1483.7.4 Load Flow Computation via Particle Swarm Optimization with Mutation Operation 1503.7.5 Numerical Results 1533.7.6 Conclusions 1603.8 Artificial Bee Colony Algorithm for Solving Optimal Power Flow 1613.8.1 Optimization in Power System Operation 1623.8.2 The Optimal Power Flow Problem 1623.8.3 Artificial Bee Colony 1663.8.4 ABC for the OPF Problem 1683.8.5 Case Studies 1703.8.6 Conclusions 1763.9 OPF Test Bed and Performance Evaluation of Modern Heuristic Optimization 1763.9.1 Introduction 1763.9.2 Problem Definition 1773.9.3 OPF Test Systems 1783.9.4 Differential Evolutionary Particle Swarm Optimization: DEEPSO 1833.9.5 Enhanced Version of Mean–Variance Mapping Optimization Algorithm: MVMO-PHM 1873.9.6 Evaluation Results 1933.9.7 Conclusions 1963.10 Transmission System Expansion Planning 1973.10.1 Introduction 1973.10.2 Transmission System Expansion Planning Models 1983.10.3 Mathematical Modeling 1993.10.4 Challenges 2013.10.5 Application of Meta-heuristics to TEP 2023.10.6 Conclusions 2103.11 Conclusion 210References 210Chapter 4 Power System And Power Plant Control 2274.1 Introduction 2274.2 Load Frequency Control – Optimization and Stability 2284.2.1 Introduction 2284.2.2 Load Frequency Control 2294.2.3 Components of Active Power Control System 2304.2.4 Designing LFC Structure for an Interconnected Power System 2324.2.5 Parameter Optimization and System Performance 2374.2.6 System Stability in the Presence of Communication Delay 2424.2.7 Conclusions 2444.3 Control of Facts Devices 2444.3.1 Introduction 2444.3.2 Role of FACTS 2464.3.3 Static Modeling of FACTS devices 2474.3.4 Power Flow Control using FACTS 2554.3.5 Optimal Power Flow Using Suitability FACTS devices 2594.3.6 Use of Particle Swarm Optimization 2814.3.7 Conclusions 2834.4 Hybrid of Analytical and Heuristic Techniques for facts Devices 2844.4.1 Introduction 2844.4.2 Heuristic Algorithms 2854.4.3 SVC and Voltage Instability Improvement 2884.4.4 FACTS Devices and Angle Stability Improvement 2934.4.5 Selection of Supplementary Input Signals for Damping Inter-area Oscillations 2954.4.6 TCSC and Improvement of Total Transfer Capability 3024.4.7 Conclusions 3054.5 Power System Automation 3054.5.1 Introduction 3054.5.2 Application of PSO on Power System’s Corrective Control 3074.5.3 Genetic Algorithm-aided DTs for Load Shedding 3224.5.4 Power System-Controlled Islanding 3244.5.5 Application of the method on the IEEE – 30 buses test system 3264.5.6 Application of the method on the IEEE – 118 buses test system 3274.5.7 Conclusions 3274.5.8 Appendix 3284.6 Power Plant Control 3344.6.1 Introduction 3344.6.2 Coal Mill Modeling 3354.6.3 Nonlinear Model Predictive Control of Reheater Steam Temperature 3404.6.4 Multi-objective Optimization of Boiler Combustion System 3454.6.5 Conclusions 3554.7 Predictive Control in Large-Scale Power Plant 3554.7.1 Introduction 3554.7.2 Particle Swarm Optimization Algorithm 3564.7.3 Performance Prediction Model Development Based on NARMA Model 3574.7.4 Design of Intelligent MPOC Scheme 3614.7.5 Control Simulation Tests 3644.7.6 Conclusions 3674.8 Conclusion 368References 369Chapter 5 Distribution System 3815.1 Introduction 3815.2 Active Distribution Network Planning 3825.2.1 Introduction 3825.2.2 Problem Formulation 3825.2.3 Overview of the Solution Techniques for Distribution Network Planning 3855.2.4 Genetic Algorithm Solution to Active Distribution Network Planning Problem 3855.2.5 Numerical Results 3885.2.6 Conclusions 3925.3 Optimal Selection of Distribution System Architecture 3925.3.1 Introduction 3925.3.2 Deterministic Optimization Techniques 3935.3.3 Stochastic Optimization Techniques 3945.3.4 Multi-Objective Optimization 4005.3.5 Mathematical Modeling for Power System Components 4015.3.6 AC/DC Power Flow in Hybrid Networks 4055.3.7 Pareto-Based Multi-Objective Optimization Problem 4095.4 Conservation Voltage Reduction Planning 4185.4.1 Introduction 4185.4.2 Conservation Voltage Reduction 4185.4.3 CVR Based on PSO 4205.4.4 CVR Based on AHP 4235.4.5 Case Studies for CVR in Korean Power System 4245.4.6 Conclusion 4275.5 Dynamic Distribution Network Expansion Planning with Demand Side Management 4275.5.1 Introduction 4275.5.2 Expansion Options 4315.5.3 Problem Formulation 4365.5.4 Optimization Algorithm 4425.5.5 Case Studies 4505.5.6 Conclusions 4605.6 GA-Guided Trust-Tech Methodology for Capacitor Placement in Distribution Systems 4675.6.1 Introduction 4675.6.2 Overview of the Trust-Tech Method 4695.6.3 Computing Tier-One Local Optimal Solutions 4725.6.4 The GA-Guided Trust-Tech Method 4745.6.5 Applications to Capacitor Placement Problems 4785.6.6 Numerical Study 4815.6.7 Conclusions 4885.7 Network Reconfiguration 4895.7.1 Introduction 4895.7.2 Modern Distribution Systems: A Concept 4905.7.3 Distribution System Reconfiguration 4935.7.4 Distribution System Service Restoration 4965.7.5 Multi-Agent System for Distribution System Reconfiguration 5015.7.6 Conclusions 5105.8 Distribution System Restoration 5105.8.1 Introduction 5105.8.2 Power System Restoration Process 5115.9 Group-based PSO for System Restoration 5315.9.1 Introduction 5315.9.2 Group-Based PSO Method 5335.9.3 Overview of the Service Restoration Problem 5395.9.4 Application to the Service Restoration Problem 5425.9.5 Numerical Results 5455.9.6 Conclusions 5525.10 MVMO for Parameter Identification of Dynamic Equivalents for Active Distribution Networks 5535.10.1 Introduction 5535.10.2 Active Distribution System 5535.10.3 Need for Aggregation and the Concept of Dynamic Equivalents 5545.10.4 Proposed Approach with MVMO 5565.10.5 Adaptation of MVMO for Identification Problem 5585.10.6 Case Study 5625.10.7 Application to Test Case 5685.10.8 Analysis 5695.10.9 Reflections 5725.10.10 Conclusions 5725.11 Parameter Estimation of Circuit Model for Distribution Transformers 5735.11.1 Introduction 5735.11.2 Transformer Winding Equivalent Circuit 5745.11.3 Signal Comparison Indicators 5765.11.4 Coefficients Estimation Using Heuristic Optimization 5785.11.5 Coefficients Estimation Results and Conclusion 5825.11.6 Conclusions 586References 590Chapter 6 Integration Of Renewable Energy In Smart Grid 6136.1 Introduction 6136.2 Renewable Energy Sources 6136.2.1 Renewable Energy Sources Management Overview 6136.2.2 Energy Resource Scheduling – Problem Formulation 6156.2.3 Energy Resources Scheduling – Particle Swarm Optimization 6176.2.4 Energy Resources Scheduling – Simulated Annealing 6186.2.5 Practical Case Study 6216.2.6 Appendix 6326.2.7 Conclusions 6346.3 Operation and Control of Smart Grid 6356.3.1 Introduction 6356.3.2 Problems for Systems Configuration or Systems Design 6366.3.3 Systems Operation and Systems Control 6386.3.4 System’s Management 6406.3.5 Conclusion 6456.4 Compliance of Reactive Power Requirements in Wind Power Plants 6456.4.1 Introduction 6456.4.2 Problem Definition 6466.4.3 NN-Based Wind Speed Forecasting Method 6486.4.4 Mean Variance Mapping Optimization Algorithm 6506.4.5 Case Studies 6546.4.6 Conclusions 6656.5 Photovoltaic Controller Design 6676.5.1 Introduction 6676.5.2 Maximum Power Point Tracking in PV System 6686.5.3 Particle Swarm Optimization 6746.5.4 Application of Particle Swarm Optimization in MPPT 6746.5.5 Illustration of PSO Technique for MPPT During Different Irradiance Conditions 6766.5.6 Conclusion 6786.6 Demand Side Management and Demand Response 6806.6.1 Introduction 6806.6.2 Methodology for Consumption Shifting and Generation Scheduling 6836.6.3 Quantum PSO 6856.6.4 Numeric Example 6876.6.5 Conclusions 6916.7 EPSO-Based Solar Power Forecasting 6916.7.1 Introduction 6916.7.2 General Radial Basis Function Network 6936.7.3 k-Means 6956.7.4 Deterministic Annealing Clustering 6956.7.5 Evolutionary Particle Swarm Optimization 6976.7.6 Hybrid Intelligent Method 6986.7.7 Case Studies 6996.7.8 Conclusion 7046.8 Load Demand and Solar Generation Forecast for PV Integrated Smart Buildings 7046.8.1 Introduction 7046.8.2 Literature Review of Forecasting Techniques 7146.8.3 Ensemble Forecast Methodology for Load Demand and PV Output Power 7176.8.4 Numerical Results and Discussion 7226.8.5 Conclusions 7286.9 Multi-Objective Planning of Public Electric Vehicle Charging Stations 7296.9.1 Introduction 7296.9.2 Multi-Objective Electric Vehicle Charging Station Layout Planning Model 7306.9.3 An Improved SPEA2 for Solving EVCSLP Problem 7336.9.4 Case Study 7376.9.5 Conclusion 7406.10 Dispatch Modeling Incorporating Maneuver Components, Wind Power, and Electric Vehicles 7416.10.1 Introduction 7416.10.2 Proposed Economic Dispatch Formulation 7436.10.3 Population-Based Optimization Algorithms 7516.10.4 Test System and Results Analysis 7536.10.5 Conclusion 7566.11 Conclusions 757References 757Chapter 7 Electricity Markets 7757.1 Introduction 7757.2 Bidding Strategies 7777.2.1 Introduction 7777.2.2 Context Analysis 7797.2.3 Strategic Bidding 7807.3 Market Analysis and Clearing 7817.3.1 Introduction 7817.3.2 Electricity Market Simulators 7827.3.3 Didactic Example 7857.4 Electricity Market Forecasting 7937.4.1 Introduction 7937.4.2 Artificial Neural Networks for Electricity Market Price Forecasting 7947.4.3 Support Vector Machines for Electricity Market Price Forecasting 7957.4.4 Illustrative Results 7967.5 Simultaneous Bidding of V2G In Ancillary Service Markets Using Fuzzy Optimization 7987.5.1 Introduction 7987.5.2 Fuzzy Optimization 7997.5.3 FO-based Simultaneous Bidding of Ancillary Services Using V2G 8017.5.4 Case Study 8067.5.5 Results and Discussions 8077.5.6 Conclusion 8117.6 Conclusions 812References 812Index 819