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Probabilistic power system expansion planning with renewable energy resources and energy storage systems
- Author / Creator
- Available as
- Online
- Summary
-
"This book presents innovative approaches to the probabilistic planning of generation and transmission systems under uncertainties. It includes renewables and energy storage calculations in using p...
"This book presents innovative approaches to the probabilistic planning of generation and transmission systems under uncertainties. It includes renewables and energy storage calculations in using probabilistic and deterministic reliability techniques to assess system performance from a long-term expansion planning viewpoint. It is divided into two sections. The first covers topics related to Generation Expansion Planning (GEP). This includes chapters on cost assessment, methodology and optimization, renewable energy generation, and more. The second part provides information on Transmission System Expansion Planning (TEP). This part explores TEP with reliability constraints, probabilistic production cost simulation for TEP, optimal reliability criteria, and more"--Provided by publisher.
Details
- Creator
- Jaeseok Choi, Kwang Y. Lee
- Format
- Books
- Language
- English
- Contributors
- Publication
-
- First edition
- Hoboken, New Jersey : John Wiley and Sons, Incorporated, [2021]
- Physical Details
-
- 1 online resource
- data file
- ISBNs
- 9781119819042, 1119819040, 9781119684190, 1119684196, 9781119684152, 1119684153, 9781119684138, 9781119684138
- OCLC
- on1267402994
- Includes bibliographical references and index.
- About the authors -- Preface -- Acknowledgements -- PART I Generation Expansion Planning -- Chapter 1. Introduction -- 1.1 Electricity Outlook -- 1.2 Renewables -- 1.3 Power System Planning -- Chapter 2. Background on Generation Expansion Planning -- 2.1 Methodology and Issues -- 2.2 Formulation of the Least-Cost Generation Expansion Planning Problem -- Chapter 3. Cost Assessment and Methodologies in Generation Expansion Planning -- 3.1 Basic Cost Concepts -- 3.1.1. Annual Effective Discount Rate -- 3.1.2. Present Value -- 3.1.3. Relationship Between Salvage Value and Depreciation Cost -- 3.2 Methodologies -- 3.2.1. Dynamic Programming -- 3.2.2. Linear Programming -- 3.2.3. Integer Programming -- 3.2.4. Multi-objective Linear Programming -- 3.2.5. Genetic Algorithm -- 3.2.6. Game Theory -- 3.2.7. Reliability Worth -- 3.2.8. Maximum Principle -- 3.3 Conventional Approach for Load Modeling -- 3.3.1. Load Duration Curve -- Chapter 4. Load Model and Generation Expansion Planning -- 4.1 Introduction -- 4.2 Analytical Approach for Long-Term Generation Expansion Planning -- 4.2.1. Representation of Random Load Fluctuations -- 4.2.2. Available Generation Capacities -- 4.2.3. Expected Plant Outputs -- 4.2.4. Expected Annual Energy -- 4.2.5. Reliability Measures -- 4.2.6. Expected Annual Cost -- 4.2.7. Expected Marginal Values -- 4.3 Optimal Utilization of Hydro Resources -- 4.3.1. Introduction -- 4.3.2. Conventional Peak-Shaving Operation and Its Problems -- 4.3.3. Peak-Shaving Operation Based on Analytical Production Costing Model -- 4.3.4. Optimization Procedure for Peak-Shaving Operation -- 4.4 Long-Range Generation Expansion Planning -- 4.4.1. Statement of Long-Range Generation Expansion Planning Problem -- 4.4.2. Optimization Procedures -- 4.5 Case Studies -- 4.5.1. Test for Accuracy of Formulas -- 4.5.2. Test for Solution Convergence and Computing Efficiency -- 4.6 Conclusions -- Chapter 5. Probabilistic Production Simulation Model -- 5.1 Introduction -- 5.2 Effective Load Distribution Curve -- 5.3 Case Studies -- 5.3.1. Case Study I -- 5.3.2. Case Study II -- 5.3.3. Case Study III -- 5.4 Probabilistic Production Simulation Algorithm -- 5.4.1. Hartley Transform -- Chapter 6. Decision Maker's Satisfaction using Fuzzy Set Theory -- 6.1 Introduction -- 6.2 Fuzzy Dynamic Programming -- 6.3 Best Generation Mix -- 6.3.1. Problem Statement -- 6.3.2. Objective Functions -- 6.3.3. Constraints -- 6.3.4. Membership Functions -- 6.3.5. The Proposed Fuzzy Dynamic Programming-Based Solution Procedure -- 6.4 Case Study -- 6.4.1. Results and Discussion -- 6.5. Conclusions -- Chapter 7. Best Generation Mix Considering Air Pollution Constraints -- 7.1 Introduction -- 7.2 Concept of Flexible Planning -- 7.3 LP Formulation of Best Generation Mix -- 7.3.1. Problem Statement -- 7.3.2. Objective Functions -- 7.4 Fuzzy LP Formulation of Flexible Generation Mix -- 7.4.1. The Optimal Decision Theory by Fuzzy Set Theory -- 7.4.2. The Function of Fuzzy Linear Programming -- 7.5 Case Studies -- 7.5.1. Results by Non-Fuzzy Model -- 7.5.2. Results in Fuzzy Model -- 7.6 Conclusions -- Chapter 8. Generation System Expansion Planning with Renewable Energy -- 8.1 Introduction -- 8.2 LP Formulation of Best Generation Mix -- 8.2.1. Problem Statement -- 8.2.2. Objective Functions -- 8.3 Fuzzy LP Formulation of Flexible Generation Mix-I -- 8.3.1. The Optimal Decision Theory by Fuzzy Set Theory -- 8.3.2. The Function of Fuzzy Linear Programming -- 8.4 Fuzzy LP Formulation of Flexible Generation Mix-II -- 8.5 Case Studies -- 8.5.1. Test Results -- 8.5.2. Sensitivity Analysis -- 8.6 Conclusions -- Chapter 9. Reliability Evaluation for Power System Planning with Wind Generators and Multi Energy Storage Systems -- 9.1 Introduction -- 9.2 Probabilistic Reliability Evaluation by Monte Carlo Simulation -- 9.2.1. Probabilistic Operation Model of Generator 1 -- 9.2.2. Probabilistic Operation Model of Generator 2 -- 9.3 Probabilistic Output Prediction Model of WTG -- 9.4 Multi-Energy Storage System Operational Model -- 9.4.1. Constraints of ESS control (EUi,k) -- 9.5 Multi-ESS Operation Rule -- 9.6 Reliability Evaluation with Energy Storage System -- 9.7 Case Studies -- 9.7.1. Power System of Jeju Island -- 9.7.2. Reliability Evaluation of Single-ESS -- 9.7.3. Reliability Evaluation of Multi-ESS -- 9.7.4. Comparison of System A and System B -- 9.8 Conclusions -- 9.9 Appendices -- 9.9.1. Single-ESS Model -- 9.9.2. Multi-ESS Model -- 9.9.3. Operation of Multi-ESS Models -- 9.9.4. A Comparative Analysis of Single-ESS and Multi-ESS Models -- Chapter 10. Genetic Algorithm for Generation Expansion Planning and Reactive Power Planning -- 10.1 Introduction -- 10.2 Generation Expansion Planning -- 10.3 The Least-Cost GEP Problem -- 10.4 Simple Genetic Algorithm -- 10.4.1. String Representation -- 10.4.2. Genetic Operations -- 10.5 Improved GA for the Least-Cost GEP -- 10.5.1. String Structure -- 10.5.2. Fitness Function -- 10.5.3. Creation of an Artificial Initial Population -- 10.5.4. Stochastic Crossover, Elitism, and Mutation -- 10.6 Case Studies -- 10.6.1. Test Systems Description -- 10.6.2. Parameters for GEP and IGA -- 10.6.3. Numerical Results -- 10.6.4. Summary -- 10.7 Reactive Power Planning -- 10.8 Decomposition of Reactive Power Planning Problem -- 10.8.1. Investment-Operation Problem -- 10.8.2. Benders Decomposition Formulation -- 10.9 Solution Algorithm for VAR Planning -- 10.10 Simulation Results -- 10.10.1. The 6-bus System -- 10.10.2. IEEE 30-bus System -- 10.10.3. Summary -- 10.11 Conclusions -- References -- PART II Transmission System Expansion Planning -- Chapter 11. Transmission Expansion Planning Problem -- 11.1 Introduction -- 11.2 Long-Term Transmission Expansion Planning -- 11.3 Yearly Transmission Expansion Planning -- 11.3.1. Power Flow Model -- 11.3.2. Optimal Operation Cost Model -- 11.3.3. Probability of Line Failures -- 11.3.4. Expected Operation Cost -- 11.3.5. Annual Expected Operation Cost -- 11.4 Long-Term Transmission Planning Problem -- 11.4.1. Long-term Transmission Planning Model -- 11.4.2. Solution Technique for the Planning Problem -- 11.5 Case Study -- 11.6 Conclusions -- Chapter 12. Models and Methodologies -- 12.1 Introduction -- 12.2 Transmission System Expansion Planning Problem -- 12.3 Cost Evaluation for TEP Considering Electricity Market -- 12.4 Model Development History for TEP Problem -- 12.5 General DC Power Flow Based Formulation of TEP Problem -- 12.5.1. Linear Programming -- 12.5.2. Dynamic Programming -- 12.5.3. Integer Programming (IP) -- 12.5.4. Genetic Algorithm by Mixed Integer Programming (MIP) -- 12.6 Branch and Bound Algorithm -- 12.6.1. Branch and Bound Algorithm and Flow Chart -- 12.6.2. Sample System Study by Branch and Bound -- Chapter 13. Probabilistic Production Cost Simulation for TEP -- 13.1 Introduction -- 13.2 Modeling of Extended Effective Load for Composite Power System -- 13.3 Probability Distribution Function of Synthesized Fictitious Equivalent Generator -- 13.4 Reliability Evaluation and Probabilistic Production Cost Simulation at Load Points -- 13.5 Case Studies -- 13.5.1. Numerical Calculation of a Simple Example -- 13.5.2. Case Study: Modified Roy Billinton Test System -- 13.6 Conclusions -- Chapter 14. Reliability Constraints -- 14.1 Deterministic Reliability Constraint using Contingency Constraints -- 14.1.1. Introduction -- 14.1.2. Transmission Expansion Planning Problem -- 14.1.3. Maximum Flow under Contingency Analysis for Security Constraint -- 14.1.4. Alternative Types of Contingency Criteria -- 14.1.5. Solution Algorithm -- 14.1.6. Case Studies -- 14.1.7. Conclusions -- 14.1.8. Appendix -- 14.2 Deterministic Reliability Constraints -- 14.2.1. Introduction -- 14.2.2. Transmission System Expansion Planning Problem -- 14.2.3. Maximum Flow under Contingency Analysis for Security Constraint -- 14.2.4. Solution Algorithm -- 14.2.5. Case Studies -- 14.2.6. Conclusions -- 14.3 Probabilistic Reliability Constraints -- 14.3.1. Introduction -- 14.3.2. Transmission System Expansion Planning Problem -- 14.3.3. Composite Power System Reliability Evaluation -- 14.3.4. Solution Algorithm -- 14.3.5. Case Study -- 14.3.6. Conclusions -- 14.4 Outage Cost Constraints -- 14.4.1. Introduction -- 14.4.2. The Objective Function -- 14.4.3. Constraints -- 14.4.4. Outage Cost Assessment of Transmission System -- 14.4.5. Reliability Evaluation of Transmission System -- 14.4.6. Outage Cost Assessment -- 14.4.7
- Solution Algorithm -- 14.4.8. Case Study -- 14.4.9. Conclusions -- 14.5 Deterministic-Probabilistic (D-P) Criteria -- Chapter 15. Fuzzy Decision Making for TEP -- 15.1 Introduction -- 15.2 Fuzzy Transmission Expansion Planning Problem -- 15.3 Equivalent Crisp Integer Programming and Branch and Bound Method -- 15.4 Membership Functions -- 15.5 Solution Algorithm -- 15.6 Testing -- 15.6.1. Discussion of Results -- 15.6.2. Solution Sensitivity to Reliability Criterion -- 15.6.3. Sensitivity to Budget for Construction Cost -- 15.7 Case Study -- 15.8 Conclusions -- 15.9 Appendix -- 15.9.1. Network Modeling of Power System -- 15.9.2. Definition -- 15.9.3. Fuzzy Integer Programming (FIP) -- Chapter 16. Optimal Reliability Criteria for TEP -- 16.1 Introduction -- 16.2 Probabilistic Optimal Reliability Criterion -- 16.2.1. Introduction -- 16.2.2. Optimal Reliability Criterion Determination -- 16.2.3. Optimal Composite Power System Expansion Planning -- 16.2.4. Composite Power System Reliability Evaluation and Outage Cost Assessment -- 16.2.5. Case Study -- 16.2.6. Conclusions -- 16.3 Deterministic Reliability Criterion for Composite Power System Expansion Planning -- 16.3.1. Introduction -- 16.3.2. Optimal Reliability Criterion Determination -- 16.3.3. Optimal Composite Power System Expansion Planning -- 16.3.4. Com ..