Stability-Constrained Optimization for Modern Power System Operation and Planning
Description
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.
<b>Stability-Constrained Optimization for Modern Power System Operation and Planning</b>
<b>Comprehensive treatment of an aspect of stability constrained operations and planning, including the latest research and engineering practices</b>
<i>Stability-Constrained Optimization for Modern Power System Operation and Planning </i>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, <i>Stability-Constrained Optimization for Modern Power System Operation and Planning </i>covers three parts:
<ul><li>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</li><li>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</li><li>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</li></ul><i>Stability-Constrained Optimization for Modern Power System Operation and Planning </i>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.
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Yuan Chi received B.E. degree from Southeast University, Nanjing, China, in 2009, and the M.E. degree from Chongqing University, Chongqing, China, in 2012, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2021. From 2012 to 2017, he worked as an Electrical Engineer of Power System Planning consecutively with State Grid Chongqing Electric Power Research Institute and Chongqing Economic and Technological Research Institute. He is currently a Research Associate with Chongqing University. His research interests include planning, resilience, and voltage stability of power systems.
Heling Yuan received B.E., M.Sc., and Ph.D. degrees from North China Electric Power University, Beijing, China, and the University of Manchester, and Nanyang Technological University (NTU), Singapore, in 2016, 2017, and 2022, respectively. She is currently a Research Fellow at Rolls-Royce @ NTU Corporate Lab, Singapore. Her research interests include modeling, optimization, stability analysis and control of power systems.
<b>Yan Xu </b>obtained B.E. and M.E. degrees from South China University of Technology, China, and the Ph.D. from University of Newcastle, Australia, in 2008, 2011, and 2013, respectively. He conducted postdoctoral research with the University of Sydney Postdoctoral Fellowship, and then joined Nanyang Technological University (NTU) with the Nanyang Assistant Professorship. He is now an Associate Professor at the School of Electrical and Electronic Engineering, and a Cluster Director at the Energy Research Institute, Nanyang Technological University, Singapore (ERI@N). His research interests include power system stability, microgrid, and data analytics for smart grid applications. He is an Editor for <i>IEEE Trans. Smart Grid </i>and <i>IEEE Trans. Power Systems</i>.
<b>Yuan Chi </b>received B.E. degree from Southeast University, Nanjing, China, in 2009, and the M.E. degree from Chongqing University, Chongqing, China, in 2012, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2021. From 2012 to 2017, he worked as an Electrical Engineer of Power System Planning consecutively with State Grid Chongqing Electric Power Research Institute and Chongqing Economic and Technological Research Institute. He is currently a Research Associate with Chongqing University. His research interests include planning, resilience, and voltage stability of power systems.
<b>Heling Yuan </b>received B.E., M.Sc., and Ph.D. degrees from North China Electric Power University, Beijing, China, and the University of Manchester, and Nanyang Technological University (NTU), Singapore, in 2016, 2017, and 2022, respectively. She is currently a Research Fellow at Rolls-Royce @ NTU Corporate Lab, Singapore. Her research interests include modeling, optimization, stability analysis and control of power systems.
Content
About the Authors xvii
Foreword xix
Preface xxi
<b>Part I Power System Stability Preliminaries 1</b>
<b>1 Power System Stability: Definition, Classification, and Phenomenon 5</b>
1.1 Introduction 5
1.2 Definition 6
1.3 Classification 6
1.4 Rotor Angle Stability 7
1.5 Voltage Stability 10
1.6 Frequency Stability 12
1.7 Resonance Stability 14
1.8 Converter-Driven Stability 16
<b>2 Mathematical Models and Analysis Methods for Power System Stability 19</b>
2.1 Introduction 19
2.2 General Mathematical Model 19
2.3 Transient Stability Criteria 20
2.4 Time-Domain Simulation 21
2.5 Extended Equal-Area Criterion (EEAC) 23
2.6 Trajectory Sensitivity Analysis 26
<b>3 Recent Large-Scale Blackouts in the World 33</b>
3.1 Introduction 33
3.2 Major Blackouts in the World 33
<b>Part II Transient Stability-Constrained Dispatch and Operational Control 45</b>
<b>4 Power System Operation and Optimization Models 49</b>
4.1 Introduction 49
4.2 Overview and Framework of Power System Operation 49
4.3 Mathematical Models for Power System Optimal Operation 51
4.4 Power System Operation Practices 59
<b>5 Transient Stability-Constrained Optimal Power Flow (TSC-OPF): Modeling and Classic Solution Methods 65</b>
5.1 Mathematical Model 65
5.2 Discretization-based Method 66
5.3 Direct Method 68
5.4 Evolutionary Algorithm-based Method 70
<b>6 Hybrid Method for Transient Stability-Constrained Optimal Power Flow 79</b>
6.1 Introduction 79
6.2 Proposed Hybrid Method 80
6.3 Technical Specification 83
6.4 Case Studies 85
<b>7 Data-Driven Method for Transient Stability-Constrained Optimal Power Flow 97</b>
7.1 Introduction 97
7.2 Decision Tree-based Method 98
7.3 Pattern Discovery-based Method 103
7.4 Case Studies 110
<b>8 Transient Stability-Constrained Unit Commitment (TSCUC) 133</b>
8.1 Introduction 133
8.2 TSC-UC model 134
8.3 Transient Stability Control 135
8.4 Decomposition-based Solution Approach 137
8.5 Case Studies 140
<b>9 Transient Stability-Constrained Optimal Power Flow under Uncertainties 155</b>
9.1 Introduction 155
9.2 TSC-OPF Model with Uncertain Dynamic Load Models 157
9.3 Case Studies for TSC-OPF Under Uncertain Dynamic Loads 164
9.4 TSC-OPF Model with Uncertain Wind Power Generation 170
9.5 Case Studies for TSC-OPF Under Uncertain Wind Power 175
9.6 Discussions and Concluding Remarks 189
<b>10 Optimal Generation Rescheduling for Preventive Transient Stability Control 195</b>
10.1 Introduction 195
10.2 Trajectory Sensitivity Analysis for Transient Stability 196
10.3 Transient Stability Preventive Control Based on Critical OMIB 198
10.4 Case Studies of Transient Stability Preventive Control Based on the Critical OMIB 202
10.5 Transient Stability Preventive Control Based on Stability Margin 213
10.6 Case Studies of Transient Stability Preventive Control Based on Stability Margin 217
<b>11 Preventive-Corrective Coordinated Transient Stability-Constrained Optimal Power Flow under Uncertain Wind Power 233</b>
11.1 Introduction 233
11.2 Framework of the PC--CC Coordinated TSC-OPF 234
11.3 PC--CC Coordinated Mathematical Model 235
11.4 Solution Method for the PC--CC Coordinated Model 239
11.5 Case Studies 243
<b>12 Robust Coordination of Preventive Control and Emergency Control for Transient Stability Enhancement under Uncertain Wind Power 255</b>
12.1 Introduction 255
12.2 Mathematical Formulation 255
12.3 Transient Stability Constraint Construction 260
12.4 Solution Approach 261
12.5 Case Studies 264
<b>Part III Voltage Stability-Constrained Dynamic VAR Resources Planning 281</b>
<b>13 Dynamic VAR Resource Planning for Voltage Stability Enhancement 285</b>
13.1 Framework of Power System VAR Resource Planning 285
13.2 Mathematical Models for Optimal VAR Resource Planning 285
13.3 Power System Planning Practices 288
<b>14 Voltage Stability Indices 293</b>
14.1 Conventional Voltage Stability Criteria 293
14.2 Steady-State and Short-term Voltage Stability Indices 297
14.3 Time-Constrained Short-term Voltage Stability Index 301
<b>15 Dynamic VAR Resources 311</b>
15.1 Fundamentals of Dynamic VAR Resources 311
15.2 Dynamic Models of Dynamic VAR Resources 314
<b>16 Candidate Bus Selection for Dynamic VAR Resource Allocation 319</b>
16.1 Introduction 319
16.2 General Framework of Candidate Bus Selection 320
16.3 Zoning-based Candidate Bus Selection Method 321
16.4 Correlated Candidate Bus Selection Method 327
16.5 Case Studies 338
<b>17 Multi-objective Dynamic VAR Resource Planning 361</b>
17.1 Introduction 361
17.2 Multi-objective Optimization Model 362
17.3 Decomposition-based Solution Method 365
17.4 Case Studies 368
<b>18 Retirement-Driven Dynamic VAR Resource Planning 375</b>
18.1 Introduction 375
18.2 Equipment Retirement Model 376
18.3 Retirement-Driven Dynamic VAR Planning Model 378
18.4 Solution Method 380
18.5 Case Studies 381
<b>19 Multi-stage Coordinated Dynamic VAR Resource Planning 389</b>
19.1 Introduction 389
19.2 Coordinated Planning and Operation Model 390
19.3 Solution Method 408
19.4 Case Studies 411
<b>20 Many-objective Robust Optimization-based Dynamic VAR Resource Planning 429</b>
20.1 Introduction 429
20.2 Robustness Assessment of Planning Decisions 430
20.3 Many-objective Dynamic VAR Planning Model 436
20.4 Many-objective Optimization Algorithm 439
20.5 Case Studies 445
Nomenclature 452
References 455
Index 459