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

by Choi, Jaeseok; Lee, Kwang Y.

• ISBN13: 9781119684138
• ISBN10: 1119684137
• Edition: 1st
• Format: Hardcover
• Copyright: 2021-10-12
• Publisher: Wiley-IEEE Press

Summary

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

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:

• A thorough discussion of generation expansion planning, including cost assessment, methodology and optimization, and probabilistic production cost
• An exploration of transmission system expansion planning, including the branch and bound algorithm, probabilistic production cost simulation for TEP, and TEP with reliability constraints
• An examination of fuzzy decision making applied to transmission system expansion planning
• A treatment of probabilistic reliability-based grid expansion planning of power systems including wind turbine generators

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.

Author Biography

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. 

Table of Contents

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