Probabilistic power system expansion planning with renewable energy resources and energy storage systems 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 Makers 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 ... 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