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, addressing a common gap in knowledge by presenting a series of stability-constrained optimization methodologies for power system operation and planning with the aim of power system stability enhancement. Unlike other resources, which focus mainly on the dynamic modeling, stability analysis, and controller design of power systems, this book is instead dedicated to operational and planning methods for power system stability enhancement, including power system stability preliminaries, stability-constrained operational dispatch, and stability-constrained network reinforcement planning.
Authored by experts with established track records in both research and industry, Stability-Constrained Optimization for Modern Power System Operation and Planning covers sample topics such as:
- The definition, classification, and phenomenon of power system stability, recent large-scale blackouts in the world, and mathematical models and analysis tools for power system stability assessment
- Transient stability-constrained optimal power flow (TSC-OPF), hybrid solution methods for TSC-OPF, data-driven solution methods for TSC-OPF, and transient stability constrained-unit commitment (TSC-UC)
- TSC-OPF under wind power uncertainties, trajectory sensitivity-based preventive dispatch, preventive-corrective coordinated TSC-OPF, and optimal event-based load shedding
- Voltage stability indies, dynamic VAR resources (STATCOM and SVC), candidate bus selection for dynamic VAR allocation, and multi-objective dynamic VAR planning
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 model formulations and solution methodologies for stability enhancement. It also provides key practical operational dispatch and network reinforcement planning methods to power system operators, planners, and decision-makers in the power utilities and related industries.
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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