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Discover how modern techniques have shaped complex power system expansion planning with this one-stop resource from two experts in the field
Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems delivers a comprehensive collection of innovative approaches to the probabilistic planning of generation and transmission systems under uncertainties. The book includes renewables and energy storage calculations when using probabilistic and deterministic reliability techniques to assess system performance from a long-term expansion planning viewpoint.
Divided into two sections, the book first covers topics related to Generation Expansion Planning, with chapters on cost assessment, methodology and optimization, and more. The second and final section provides information on Transmission System Expansion Planning, with chapters on reliability constraints, probabilistic production cost simulation, and more.
Probabilistic Power System Expansion Planning compares the optimization and methodology across dynamic, linear, and integer programming and explores the branch and bound algorithm. Along with case studies to demonstrate how the techniques described within have been applied in complex power system expansion planning problems, readers will enjoy:
Perfect for power and energy systems designers, planners, operators, consultants, practicing engineers, software developers, and researchers, Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems will also earn a place in the libraries of practicing engineers who regularly deal with optimization problems.
Jaeseok Choi, PhD, is Full Professor at Gyeongsang National University and is a senior member of the Korean Institute of Electrical Engineers. He is an active 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. He is a member of the Intelligent Systems Subcommittee and Station Control Subcommittee of the IEEE Power and Energy Society.
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. Composite Power System Reliability Evaluation
16.3.5. DMR Evaluation using Maximum Flow Method
16.3.6. Flow Chart of Optimal Reliability Criterion Determination
16.3.7. Case Study
16.3.8. Conclusions
Chapter 17. Probabilistic Reliability Based Expansion Planning with Wind Turbine Generators
17.1 Introduction
17.2 The Multi-State Operation Model of WTG
17.2.1. WTG Power Output Model
17.2.2. Wind Speed Model
17.2.3. The Multi-State Model of WTG using Normal Probability Distribution Function
17.3 Reliability Evaluation of a Composite Power System with WTG
17.3.1. Reliability Indices at Load Buses
17.3.2. System Reliability Indices
17.4 Case Study
17.5 Conclusions
17.6 Appendix
Chapter 18. Probabilistic Reliability Based HVDC Expansion Planning with Wind Turbine Generators
18.1 The Status of HVDC
18.2 HVDC Technology for Energy Efficiency and Grid Reliability
18.3 HVDC Impacts on Transmission System Reliability
18.4 Case Study
References
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