Stability-Constrained Optimization for Modern Power System Operation and Planning

Comprehensive treatment of an aspect of stability constrained operations and planning, including the latest research and engineering practices

Stability-Constrained Optimization for Modern Power System Operation and Planning focuses on the subject of power system stability. Unlike other books in this field, which focus mainly on the dynamic modeling, stability analysis, and controller design for power systems, this book is instead dedicated to stability-constrained optimization methodologies for power system stability enhancement, including transient stability-constrained power system dispatch and operational control, and voltage stability-constrained dynamic VAR Resources planning in the power grid.

Authored by experts with established track records in both research and industry, Stability-Constrained Optimization for Modern Power System Operation and Planning covers three parts:

• Overview of power system stability, including definition, classification, phenomenon, mathematical models and analysis tools for stability assessment, as well as a review of recent large-scale blackouts in the world
• Transient stability-constrained optimal power flow (TSC-OPF) and transient stability constrained-unit commitment (TSC-UC) for power system dispatch and operational control, including a series of optimization model formulations, transient stability constraint construction and extraction methods, and efficient solution approaches
• Optimal planning of dynamic VAR Resources (such as STATCOM and SVC) in power system for voltage stability enhancement, including a set of voltage stability indices, candidate bus selection methods, multi-objective optimization model formulations, and high-quality solution approaches

Stability-Constrained Optimization for Modern Power System Operation and Planning provides the latest research findings to scholars, researchers, and postgraduate students who are seeking optimization methodologies for power system stability enhancement, while also offering key practical methods to power system operators, planners, and optimization algorithm developers in the power industry.

Yan Xu obtained B.E. and M.E. degrees from South China University of Technology, Guangzhou, China, and a PhD from University of Newcastle, Australia, in 2008, 2011, and 2013, respectively. He is now an Associate Professor at the School of EEE, and a Cluster Director at the Energy Research Institute @ NTU (ERI@N). Dr. Xu leads the SODA (Stability, Optimization & Data-Analytics) group, which consists of 14 PhD students and 6 Post-doctoral Fellows, focusing on power system stability, microgrid, and data-analytics topics.

Yuan Chi received B.E. and M.E degrees from Southeast University, China, in 2009 and Chongqing University, Chongqing, China, in 2012. Currently, he works at the School of Electrical Engineering, Chongqing University. His research interests include planning and resilience of power systems and voltage stability.

Heling Yuan received B.E. and M.Sc. degrees from North China Electric Power University and the University of Manchester, U.K., in 2016 and 2017, respectively. She is currently working towards a Ph.D. degree at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Her research interests include power system stability and control and power system operations.

Part I Power System Stability Preliminaries

Chapter 1: Power System Stability: Definition, Classification, and Phenomenon

1.1          Introduction

1.2          Definition

1.3          Classification

1.4          Rotor Angle Stability

1.5          Voltage Stability

1.6          Frequency Stability

1.7          Resonance Stability

1.8          Converter Driven Stability

Chapter 2: Mathematical Models and Analysis Methods for Power System Stability

2.1          Introduction

2.2          General mathematical model

2.3          Transient stability criterion

2.4          Time-domain simulation

2.5          Extended Equal-Area Criterion (EEAC)

2.6          Trajectory sensitivity analysis

Chapter 3: Recent large-scale blackouts in the world

3.1          Introduction

3.2          Major blackouts around the world

Part II Transient Stability-Constrained Dispatch and Operational Control

Chapter 4: Power System Operation and Optimization Models

4.1          Introduction

4.2          Overview and framework of power system operation

4.3          Mathematical models for power system optimal operation

4.4          Power system operation practices

Chapter 5: Transient stability-constrained Optimal Power Flow (TSC-OPF): Modeling and Classic Solution Methods

5.1          Mathematical model

5.2          Discretization-based method

5.3          Direct method

5.4          Evolutionary Algorithm-based method

5.5          Discussion and summary

Chapter 6: Hybrid Method for Transient Stability-constraint Optimal Power Flow

6.1          Introduction

6.2          Proposed method

6.3          Technical specification

6.4          Case study

Chapter 7: Data-driven Method for Transient Stability-constrained Optimal Power Flow

7.1          Introduction

7.2          Decision Tree-based method

7.3          Pattern discovery-based method

7.4          Case study

Chapter 8: Transient stability Constrained-Unit Commitment (TSCUC)

8.1          Introduction

8.2          TSC-UC model

8.3          Transient stability control

8.4          Decomposition-based solution approach

8.5          Case study

Chapter 9: Transient Stability-constrained Optimal Power Flow under Uncertainties

9.1          Introduction

9.2          TSC-OPF model with uncertain dynamic load models

9.3          Case studies for TSC-OPF under uncertain dynamic loads

9.4          TSC-OPF model with uncertain wind power generation

9.5          Case studies for TSC-OPF under uncertain wind power generation 

Chapter 10: Optimal Generation Rescheduling for Preventive Transient Stability Control

10.1        Introduction

10.2        Trajectory sensitivity analysis for transient stability

10.3        Transient stability control based on the critical OMIB

10.4        Case study of transient stability control based on the critical OMIB

10.5        Transient stability control based on stability margin

10.6        Case study of transient stability control based on stability margin

Chapter 11: Preventive-Corrective Coordinated Transient Stability-constrained Optimal Power Flow under Uncertain Wind Power

11.1        Introduction

11.2        PC-CC coordinated mathematical model

11.3        Solution method for the PC-CC coordinated model

11.4        Case study

Chapter 12: Robust Coordination of Preventive Control and Emergency Control for Transient Stability Enhancement under Uncertain Wind Power

12.1        Introduction

12.2        Mathematical Formulation

12.3        Transient Stability Constraint Construction

12.4        Solution Algorithm

12.5        Case studies

Part III Voltage Stability-Constrained Dynamic VAR Resources Planning

Chapter 13: Dynamic VAR resource planning for voltage stability enhancement 

13.1        Framework of Power System VAR source Planning

13.2        Mathematical Models for Optimal VAR source Planning 

13.3        Power System Planning Practices

Chapter 14: Voltage stability indices

14.1        Conventional voltage stability criteria

14.2        Steady-state and short-term voltage stability indices

14.3        Time-constrained short-term voltage stability index

Chapter 15: Dynamic VAR resources

15.1        Fundamentals of dynamic VAR resources

15.2        Dynamic models of dynamic VAR resources

Chapter 16: Candidate bus selection for dynamic VAR resource allocation

16.1        Introduction

16.2        General framework of candidate bus selection

16.3        Zoning-based candidate bus selection method

16.4        Correlated candidate bus selection method

16.5        Case studies

Chapter 17: Multi-objective dynamic VAR resource planning

17.1        Introduction

17.2        Multi-objective Optimization Model

17.3        Decomposition-based solution method

17.4        Case studies

Chapter 18: Retirement-driven dynamic VAR resource planning

18.1        Introduction

18.2        Equipment retirement model

18.3        Retirement-driven dynamic VAR planning model

18.4        Solution method

18.5        Case studies

Chapter 19: Multi-stage coordinated dynamic VAR resource planning

19.1        Introduction

19.2        Coordinated planning and operation model

19.3        Solution method

19.4        Case studies

Chapter 20: Many-objective robust optimization based dynamic VAR resource planning

20.1        Introduction

20.2        Robustness assessment of planning decisions

20.3        Many-objective dynamic VAR planning model

20.4        Many-objective optimization algorithm

20.5        Case studies

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