Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems

ISBN-13: 9781119684138 / Angielski / Twarda / 2021 / 512 str.

Jaeseok Choi; Kwang Y. Lee

Spis treści

Author Biographies xviiPreface xixAcknowledgments xxvPart I Generation Expansion Planning 11 Introduction 31.1 Electricity Outlook 31.2 Renewables 81.3 Power System Planning 122 Background on Generation Expansion Planning 152.1 Methodology and Issues 152.2 Formulation of the Least-Cost Generation Expansion Planning Problem 183 Cost Assessment and Methodologies in Generation Expansion Planning 213.1 Basic Cost Concepts 213.1.1 Annual Effective Discount Rate 223.1.2 Present Value 233.1.3 Relationship Between Salvage Value and Depreciation Cost 243.2 Methodologies 263.2.1 Dynamic Programming 263.2.2 Linear Programming 273.2.2.1 Investment Cost (Capital Cost) 273.2.2.2 Operating Cost 273.2.2.3 LP Formula 283.2.3 Integer Programming 283.2.4 Multi-objective Linear Programming 283.2.5 Genetic Algorithm 293.2.6 Game Theory 303.2.7 Reliability Worth 323.2.8 Maximum Principle 323.3 Conventional Approach for Load Modeling 343.3.1 Load Duration Curve 344 Load Model and Generation Expansion Planning 394.1 Introduction 394.2 Analytical Approach for Long-Term Generation Expansion Planning 404.2.1 Representation of Random Load Fluctuations 414.2.2 Available Generation Capacities 434.2.3 Expected Plant Outputs 444.2.4 Expected Annual Energy 474.2.5 Reliability Measures 474.2.5.1 Expected Annual Unserved Energy 474.2.5.2 Annual Loss-of-Load Probability 474.2.6 Expected Annual Cost 484.2.7 Expected Marginal Values 494.3 Optimal Utilization of Hydro Resources 504.3.1 Introduction 504.3.2 Conventional Peak-Shaving Operation and its Problems 514.3.3 Peak-Shaving Operation Based on Analytical Production Costing Model 524.3.3.1 Basic Concept 524.3.3.2 Peak-Shaving Operation Problem 534.3.4 Optimization Procedure for Peak-Shaving Operation 534.4 Long-Range Generation Expansion Planning 564.4.1 Statement of Long-Range Generation Expansion Planning Problem 564.4.1.1 Master Problem and Basic Subproblems 574.4.1.2 Hydro Subproblem 584.4.2 Optimization Procedures 594.5 Case Studies 604.5.1 Test for Accuracy of Formulas 604.5.2 Test for Solution Convergence and Computing Efficiency 624.6 Conclusion 655 Probabilistic Production Simulation Model 675.1 Introduction 675.2 Effective Load Distribution Curve 675.3 Case Studies 715.3.1 Case Study I: Sample System I With One 30MW Generator Only 715.3.2 Case Study II: Sample System II With One 10MW Generator Only 755.3.3 Case Study III: Sample System III With Two Generators - 30 and 10MW 785.4 Probabilistic Production Simulation Algorithm 825.4.1 Hartley Transform 825.5 Supply Reserve Rate 906 Decision Maker's Satisfaction Using Fuzzy Set Theory 956.1 Introduction 956.2 Fuzzy Dynamic Programming 966.3 Best Generation Mix 976.3.1 Problem Statement 976.3.2 Objective Functions 976.3.3 Constraints 996.3.4 Membership Functions 1006.3.5 The Proposed Fuzzy Dynamic Programming-Based Solution Procedure 1016.4 Case Study 1026.4.1 Results and Discussion 1046.5 Conclusion 1087 Best Generation Mix Considering Air Pollution Constraints 1117.1 Introduction 1117.2 Concept of Flexible Planning 1117.3 LP Formulation of the Best Generation Mix 1127.3.1 Problem Statement 1127.3.2 Objective Functions 1137.4 Fuzzy LP Formulation of Flexible Generation Mix 1167.4.1 The Optimal Decision Theory by Fuzzy Set Theory 1167.4.2 The Function of Fuzzy Linear Programming 1177.5 Case Studies 1187.5.1 Results by Non-Fuzzy Model 1207.5.2 Results by Fuzzy Model 1227.6 Conclusion 1248 Generation System Expansion Planning with Renewable Energy 1278.1 Introduction 1278.2 LP Formulation of the Best Generation Mix 1288.2.1 Problem Statement 1288.2.2 Objective Function and Constraints 1298.3 Fuzzy LP Formulation of Flexible Generation Mix 1328.3.1 The Optimal Decision Theory by Fuzzy Set Theory 1328.3.2 The Function of Fuzzy Linear Programming 1338.4 Case Studies 1348.4.1 Test Results 1348.4.2 Sensitivity Analysis 1348.4.2.1 Capacity Factor of WTG and SCG 1348.5 Conclusion 1409 Reliability Evaluation for Power System Planning with Wind Generators and Multi-Energy Storage Systems 1419.1 Introduction 1419.2 Probabilistic Reliability Evaluation by Monte Carlo Simulation 1439.2.1 Probabilistic Operation Model of Generator 1 1439.2.2 Probabilistic Operation Model of Generator 2 1449.3 Probabilistic Output Prediction Model of WTG 1459.4 Multi-Energy Storage System Operational Model 1479.4.1 Constraints of ESS control (EUi,k) 1499.5 Multi-ESS Operation Rule 1509.5.1 Discharging Mode 1509.5.2 Charging Mode 1519.6 Reliability Evaluation with Energy Storage System 1519.7 Case Studies 1529.7.1 Power System of Jeju Island 1529.7.2 Reliability Evaluation of Single-ESS 1569.7.3 Reliability Evaluation of Multi-ESS 1599.7.4 Comparison of System A and System B 1629.8 Conclusion 1639.A Appendices 1649.A.1 Single-ESS Model 1649.A.2 Multi-ESS Model 1679.A.3 Operation of Multi-ESS Models 168Method 1: Energy Rate Dispatch Method (ERDM) 173Method 2: Maximum First Priority Method (MFPM) 1739.A.4 A Comparative Analysis of Single-ESS and Multi-ESS Models 17510 Genetic Algorithm for Generation Expansion Planning and Reactive Power Planning 17710.1 Introduction 17710.2 Generation Expansion Planning 17810.3 The Least-Cost GEP Problem 17910.4 Simple Genetic Algorithm 18010.4.1 String Representation 18110.4.2 Genetic Operations 18110.5 Improved GA for the Least-Cost GEP 18210.5.1 String Structure 18210.5.2 Fitness Function 18210.5.3 Creation of an Artificial Initial Population 18310.5.4 Stochastic Crossover, Elitism, and Mutation 18510.6 Case Studies 18610.6.1 Test Systems' Description 18610.6.2 Parameters for GEP and IGA 18710.6.3 Numerical Results 18910.6.4 Summary 19210.7 Reactive Power Planning 19210.8 Decomposition of Reactive Power Planning Problem 19410.8.1 Investment-Operation Problem 19410.8.2 Benders Decomposition Formulation 19510.9 Solution Algorithm for VAR Planning 19610.10 Simulation Results 19810.10.1 The 6-bus System 19810.10.2 IEEE 30-bus System 19910.10.3 Summary 20010.11 Conclusion 201References 203Part II Transmission System Expansion Planning 21311 Transmission Expansion Planning Problem 21511.1 Introduction 21511.2 Long-Term Transmission Expansion Planning 21611.3 Yearly Transmission Expansion Planning 21811.3.1 Power Flow Model 21811.3.2 Optimal Operation Cost Model 22011.3.3 Probability of Line Failures 22211.3.4 Expected Operation Cost 22311.3.5 Annual Expected Operation Cost 22411.4 Long-Term Transmission Planning Problem 22411.4.1 Long-Term Transmission Planning Model 22511.4.2 Solution Technique for the Planning Problem 22611.5 Case Study 22711.6 Conclusion 23212 Models and Methodologies 23512.1 Introduction 23512.2 Transmission System Expansion Planning Problem 23512.3 Cost Evaluation for TEP Considering Electricity Market 23612.4 Model Development History for TEP Problem 23712.5 General DC Power Flow-Based Formulation of TEP Problem 23812.5.1 Linear Programming 23912.5.2 Dynamic Programming 24012.5.3 Integer Programming (IP) 24212.5.4 Genetic Algorithm by Mixed Integer Programming (MIP) 24512.6 Branch and Bound Algorithm 24612.6.1 Branch and Bound Algorithm and Flow Chart 24612.6.2 Sample System Study by Branch and Bound 24813 Probabilistic Production Cost Simulation for TEP 25713.1 Introduction 25713.2 Modeling of Extended Effective Load for Composite Power System 25913.3 Probability Distribution Function of the Synthesized Fictitious Equivalent Generator 26313.4 Reliability Evaluation and Probabilistic Production Cost Simulation at Load Points 26513.5 Case Studies 26613.5.1 Numerical Calculation of a Simple Example 26613.5.2 Case Study: Modified Roy Billinton Test System 27413.6 Conclusion 28814 Reliability Constraints 29114.1 Deterministic Reliability Constraint Using Contingency Constraints 29114.1.1 Introduction 29114.1.2 Transmission Expansion Planning Problem 29214.1.3 Maximum Flow Under Contingency Analysis for Security Constraint 29714.1.4 Alternative Types of Contingency Criteria 29814.1.5 Solution Algorithm 29914.1.6 Case Studies 30014.1.7 Conclusion 316Appendix 31914.2 Deterministic Reliability Constraints 32214.2.1 Introduction 32214.2.2 Transmission System Expansion Planning Problem 32314.2.3 Maximum Flow Under Contingency Analysis for Security Constraint 32514.2.4 Solution Algorithm 32514.2.5 Case Studies 32614.2.6 Conclusion 33114.3 Probabilistic Reliability Constraints 33314.3.1 Introduction 33314.3.2 Transmission System Expansion Planning Problem 33814.3.3 Composite Power System Reliability Evaluation 34014.3.4 Solution Algorithm 34314.3.5 Case Study 34414.3.6 Conclusion 35714.4 Outage Cost Constraints 35714.4.1 Introduction 35714.4.2 The Objective Function 35814.4.3 Constraints 35914.4.4 Outage Cost Assessment of Transmission System 36014.4.5 Reliability Evaluation of Transmission System 36314.4.6 Outage Cost Assessment 36314.4.7 Solution Algorithm 36414.4.8 Case Study 36514.4.9 Conclusion 36914.5 Deterministic-Probabilistic (D-P) Criteria 37315 Fuzzy Decision Making for TEP 37515.1 Introduction 37515.2 Fuzzy Transmission Expansion Planning Problem 37715.3 Equivalent Crisp Integer Programming and Branch and Bound Method 37915.4 Membership Functions 38015.5 Solution Algorithm 38115.6 Testing 38215.6.1 Discussion of Results 38415.6.2 Solution Sensitivity to Reliability Criterion 38715.6.3 Sensitivity to Budget for Construction Cost 38915.7 Case Study 39015.8 Conclusion 39615.A Appendix 39615.A.1 Network Modeling of Power System 39615.A.2 Definition 39715.A.3 Fuzzy Integer Programming (FIP) 39816 Optimal Reliability Criteria for TEP 40116.1 Introduction 40116.2 Probabilistic Optimal Reliability Criterion 40116.2.1 Introduction 40116.2.2 Optimal Reliability Criterion Determination 40316.2.3 Optimal Composite Power System Expansion Planning 40316.2.3.1 The Objective Function 40316.2.3.2 Constraints 40516.2.4 Composite Power System Reliability Evaluation and Outage Cost Assessment 40616.2.4.1 Reliability Evaluation at HLI 40616.2.4.2 Reliability Evaluation at HLII (Composite Power System) 40716.2.4.3 Flow Chart of the Proposed Methodology for Optimal Reliability Criterion Determination in Transmission System Expansion Planning 40916.2.5 Case Study 41016.2.6 Conclusion 41616.3 Deterministic Reliability Criterion for Composite Power System Expansion Planning 41616.3.1 Introduction 41616.3.2 Optimal Reliability Criterion Determination 41916.3.3 Optimal Composite Power System Expansion Planning 41916.3.3.1 Composite Power System Expansion Planning Formulation in CmExpP.For 41916.3.3.2 Flow Chart 42116.3.4 Composite Power System Reliability Evaluation 42116.3.4.1 Reliability Indices at Load Points 42216.3.4.2 Reliability Indices of the Bulk System 42316.3.5 DMR Evaluation using Maximum Flow Method 42416.3.6 Flow Chart of Optimal Reliability Criterion Determination 42416.3.7 Case Study 42516.3.7.1 Basic Input Data 42516.3.7.2 Results of Construction Costs of Cases 42816.3.7.3 Reliability Evaluation 42816.3.8 Conclusion 43117 Probabilistic Reliability-Based Expansion Planning with Wind Turbine Generators 43317.1 Introduction 43317.2 The Multistate Operation Model of WTG 43417.2.1 WTG Power Output Model 43417.2.2 Wind Speed Model 43517.2.3 The Multistate Model of WTG using Normal ProbabilityDistribution Function 43517.3 Reliability Evaluation of a Composite Power System with WTG 43817.3.1 Reliability Indices at Load Buses 44017.3.2 System Reliability Indices 44017.4 Case Study 44117.5 Conclusion 44817.A Appendix 44818 Probabilistic Reliability-Based HVDC Expansion Planning with Wind Turbine Generators 44918.1 The Status of HVDC 44918.2 HVDC Technology for Energy Efficiency and Grid Reliability 45118.3 HVDC Impacts on Transmission System Reliabili ty 45518.4 Case Study 455References 465Index 469

O autorze

JAESEOK CHOI, PHD, is Full Professor at Gyeongsang National University and is a Fellow of the Korean Institute of Electrical Engineers. He is a senior member of the IEEE Power Engineering Society and participates in the Reliability, Risk, and Probability Applications Subcommittee.KWANG Y. LEE, PHD, is Professor and Chair of Electrical and Computer Engineering at Baylor University and a Life Fellow of IEEE. He is a member of the Intelligent Systems Subcommittee and Station Control Subcommittee of the IEEE Power and Energy Society.