Identifying, assessing, and mitigating electric power grid vulnerabilities is a growing focus in short-term operational planning of power systems. Through illustrated application, this important guide surveys state-of-the-art methodologies for the assessment and enhancement of power system security in short term operational planning and real-time operation. The methodologies employ advanced methods from probabilistic theory, data mining, artificial intelligence, and optimization, to provide knowledge-based support for monitoring, control (preventive and corrective), and decision making tasks.
Key features:
- Introduces behavioural recognition in wide-area monitoring and security constrained optimal power flow for intelligent control and protection and optimal grid management.
- Provides in-depth understanding of risk-based reliability and security assessment, dynamic vulnerability assessment methods, supported by the underpinning mathematics.
- Develops expertise in mitigation techniques using intelligent protection and control, controlled islanding, model predictive control, multi-agent and distributed control systems
- Illustrates implementation in smart grid and self-healing applications with examples and real-world experience from the WAMPAC (Wide Area Monitoring Protection and Control) scheme.
Dynamic Vulnerability Assessment and Intelligent Control for Power Systems is a valuable reference for postgraduate students and researchers in power system stability as well as practicing engineers working in power system dynamics, control, and network operation and planning.
Table of Contents
List of Contributors xv
Foreword xix
Preface xxi
1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment 1
Jaime C. Cepeda and José Luis Rueda-Torres
1.1 Introduction 1
1.2 Power System Vulnerability 2
1.2.1 Vulnerability Assessment 2
1.2.2 Timescale of Power System Actions and Operations 4
1.3 Power System Vulnerability Symptoms 5
1.3.1 Rotor Angle Stability 6
1.3.2 Short-Term Voltage Stability 7
1.3.3 Short-Term Frequency Stability 7
1.3.4 Post-Contingency Overloads 7
1.4 Synchronized Phasor Measurement Technology 8
1.4.1 Phasor Representation of Sinusoids 8
1.4.2 Synchronized Phasors 9
1.4.3 Phasor Measurement Units (PMUs) 9
1.4.4 Discrete Fourier Transform and Phasor Calculation 10
1.4.5 Wide Area Monitoring Systems 10
1.4.6 WAMPAC Communication Time Delay 12
1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment 13
1.6 Concluding Remarks 16
2 Steady-state Security 21
Evelyn Heylen, Steven De Boeck, Marten Ovaere, Hakan Ergun, and Dirk Van Hertem
2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control 22
2.1.1 Reliability Assessment 23
2.1.2 Reliability Control 24
2.2 Reliability Under Various Timeframes 31
2.3 Reliability Criteria 33
2.4 Reliability and Its Cost as a Function of Uncertainty 34
2.4.1 Reliability Costs 34
2.4.2 Interruption Costs 35
2.4.3 Minimizing the Sum of Reliability and Interruption Costs 36
3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems 41
Bart W. Tuinema, Nikoleta Kandalepa, and José Luis Rueda-Torres
3.1 Introduction 41
3.2 Time Horizons in the Planning and Operation of Power Systems 42
3.2.1 Time Horizons 42
3.2.2 Overlapping and Interaction 42
3.2.3 Remedial Actions 42
3.3 Reliability Indicators 45
3.3.1 Security-of-Supply Related Indicators 45
3.3.2 Additional Indicators 47
3.4 Reliability Analysis 49
3.4.1 Input Information 49
3.4.2 Pre-calculations 50
3.4.3 Reliability Analysis 50
3.4.4 Output: Reliability Indicators 53
3.5 Application Example: EHV Underground Cables 53
3.5.1 Input Parameters 54
3.5.2 Results of Analysis 56
4 An Enhanced WAMS-based Power System Oscillation Analysis Approach 63
Qing Liu, Hassan Bevrani, and Yasunori Mitani
4.1 Introduction 63
4.2 HHT Method 65
4.2.1 EMD 65
4.2.2 Hilbert Transform 65
4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum 66
4.2.4 HHT Issues 67
4.3 The Enhanced HHT Method 71
4.3.1 Data Pre-treatment Processing 71
4.3.2 Inhibiting the Boundary End Effect 75
4.3.3 Parameter Identification 80
4.4 Enhanced HHT Method Evaluation 81
4.4.1 Case I 81
4.4.2 Case II 84
4.4.3 Case III 85
4.5 Application to RealWide Area Measurements 88
5 Pattern Recognition-Based Approach for Dynamic Vulnerability Status Prediction 95
Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich
5.1 Introduction 95
5.2 Post-contingency Dynamic Vulnerability Regions 96
5.3 Recognition of Post-contingency DVRs 97
5.3.1 N-1 Contingency Monte Carlo Simulation 98
5.3.2 Post-contingency Pattern Recognition Method 100
5.3.3 Definition of Data-TimeWindows 103
5.3.4 Identification of Post-contingency DVRs - Case Study 104
5.4 Real-Time Vulnerability Status Prediction 109
5.4.1 Support Vector Classifier (SVC) Training 112
5.4.2 SVC Real-Time Implementation 113
5.5 Concluding Remarks 115
6 Performance Indicator-Based Real-Time Vulnerability Assessment 119
Jaime C. Cepeda, José Luis Rueda-Torres, Delia G. Colomé, and István Erlich
6.1 Introduction 119
6.2 Overview of the Proposed Vulnerability Assessment Methodology 120
6.3 Real-Time Area Coherency Identification 122
6.3.1 Associated PMU Coherent Areas 122
6.4 TVFS Vulnerability Performance Indicators 125
6.4.1 Transient Stability Index (TSI) 125
6.4.2 Voltage Deviation Index (VDI) 128
6.4.3 Frequency Deviation Index (FDI) 131
6.4.4 Assessment of TVFS Security Level for the Illustrative Examples 131
6.4.5 Complete TVFS Real-Time Vulnerability Assessment 133
6.5 Slower Phenomena Vulnerability Performance Indicators 137
6.5.1 Oscillatory Index (OSI) 137
6.5.2 Overload Index (OVI) 141
6.6 Concluding Remarks 145
7 Challenges Ahead Risk-Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems 149
Florin Capitanescu
7.1 Chapter Overview 149
7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) 150
7.2.1 Introduction 150
7.2.2 Abstract Mathematical Formulation of the OPF Problem 150
7.2.3 OPF Solution via Interior-Point Method 151
7.2.4 Illustrative Example 154
7.3 Risk-Based OPF 158
7.3.1 Motivation and Principle 158
7.3.2 Risk-Based OPF Problem Formulation 159
7.3.3 Illustrative Example 160
7.4 OPF Under Uncertainty 162
7.4.1 Motivation and Potential Approaches 162
7.4.2 Robust Optimization Framework 162
7.4.3 Methodology for Solving the R-OPF Problem 163
7.4.4 Illustrative Example 164
7.5 Advanced Issues and Outlook 169
7.5.1 Conventional OPF 169
7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid 172
8 Modeling Preventive and Corrective Actions Using Linear Formulation 177
Tom Van Acker and Dirk Van Hertem
8.1 Introduction 177
8.2 Security Constrained OPF 178
8.3 Available Control Actions in AC Power Systems 178
8.3.1 Generator Redispatch 179
8.3.2 Load Shedding and Demand Side Management 179
8.3.3 Phase Shifting Transformer 179
8.3.4 Switching Actions 180
8.3.5 Reactive Power Management 180
8.3.6 Special Protection Schemes 180
8.4 Linear Implementation of Control Actions in a SCOPF Environment 180
8.4.1 Generator Redispatch 181
8.4.2 Load Shedding and Demand Side Management 182
8.4.3 Phase Shifting Transformer 183
8.4.4 Switching 184
8.5 Case Study of Preventive and Corrective Actions 185
8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) 186
8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) 187
8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3) 190
9 Model-based Predictive Control for Damping Electromechanical Oscillations in Power Systems 193
DaWang
9.1 Introduction 193
9.2 MPC BasicTheory & Damping Controller Models 194
9.2.1 What is MPC? 194
9.2.2 Damping Controller Models 196
9.3 MPC for Damping Oscillations 198
9.3.1 Outline of Idea 198
9.3.2 Mathematical Formulation 199
9.3.3 Proposed Control Schemes 200
9.4 Test System & Simulation Setting 204
9.5 Performance Analysis of MPC Schemes 204
9.5.1 Centralized MPC 204
9.5.2 Distributed MPC 209
9.5.3 Hierarchical MPC 209
9.6 Conclusions and Discussions 213
10 Voltage Stability Enhancement by Computational Intelligence Methods 217
Worawat Nakawiro
10.1 Introduction 217
10.2 Theoretical Background 218
10.2.1 Voltage Stability Assessment 218
10.2.2 Sensitivity Analysis 219
10.2.3 Optimal Power Flow 220
10.2.4 Artificial Neural Network 220
10.2.5 Ant Colony Optimisation 221
10.3 Test Power System 223
10.4 Example 1: Preventive Measure 224
10.4.1 Problem Statement 224
10.4.2 Simulation Results 225
10.5 Example 2: Corrective Measure 226
10.5.1 Problem Statement 226
10.5.2 Simulation Results 227
11 Knowledge-Based Primary and Optimization-Based Secondary Control of Multi-terminal HVDCGrids 233
Adedotun J. Agbemuko, Mario Ndreko, Marjan Popov, José Luis Rueda-Torres, and Mart A.M.M van der Meijden
11.1 Introduction 234
11.2 Conventional Control Schemes in HV-MTDC Grids 234
11.3 Principles of Fuzzy-Based Control 236
11.4 Implementation of the Knowledge-Based Power-Voltage Droop Control Strategy 236
11.4.1 Control Scheme for Primary and Secondary Power-Voltage Control 237
11.4.2 Input/Output Variables 238
11.4.3 Knowledge Base and Inference Engine 241
11.4.4 Defuzzification and Output 241
11.5 Optimization-Based Secondary Control Strategy 242
11.5.1 Fitness Function 242
11.5.2 Constraints 244
11.6 Simulation Results 245
11.6.1 Set Point Change 245
11.6.2 Constantly Changing Reference Set Points 246
11.6.3 Sudden Disconnection ofWind Farm for Undefined Period 246
11.6.4 Permanent Outage of VSC 3 247
12 Model Based Voltage/Reactive Control in Sustainable Distribution Systems 251
Hoan Van Pham and Sultan Nasiruddin Ahmed
12.1 Introduction 251
12.2 BackgroundTheory 252
12.2.1 Voltage Control 252
12.2.2 Model Predictive Control 253
12.2.3 Model Analysis 255
12.2.4 Implementation 257
12.3 MPC Based Voltage/Reactive Controller – an Example 258
12.3.1 Control Scheme 258
12.3.2 Overall Objective Function of the MPC Based Controller 259
12.3.3 Implementation of the MPC Based Controller 261
12.4 Test Results 262
12.4.1 Test System and Measurement Deployment 262
12.4.2 Parameter Setup and Algorithm Selection for the Controller 263
12.4.3 Results and Discussion 263
12.5 Conclusions 266
13 Multi-Agent based Approach for Intelligent Control of Reactive Power Injection in Transmission Systems 269
Hoan Van Pham and Sultan Nasiruddin Ahmed
13.1 Introduction 269
13.2 System Model and Problem Formulation 270
13.3 Multi-Agent Based Approach 275
13.3.1 Augmented Lagrange Formulation 275
13.3.2 Implementation Algorithm 275
13.4 Case Studies and Simulation Results 277
13.4.1 Case Studies 277
13.4.2 Simulation Results 277
14 Operation of Distribution SystemsWithin Secure Limits Using Real-Time Model Predictive Control 283
Hamid Soleimani Bidgoli, Gustavo Valverde, Petros Aristidou, Mevludin Glavic, and Thierry Van Cutsem
14.1 Introduction 283
14.2 Basic MPC Principles 285
14.3 Control Problem Formulation 285
14.4 Voltage CorrectionWith Minimum Control Effort 288
14.4.1 Inclusion of LTC Actions as Known Disturbances 289
14.4.2 Problem Formulation 290
14.5 Correction of Voltages and Congestion Management with Minimum Deviation from References 291
14.5.4 Problem Formulation 295
14.6 Test System 296
14.7 Simulation Results: Voltage Correction with Minimal Control Effort 298
14.8 Simulation Results: Voltage and/or Congestion Corrections with Minimum Deviation from Reference 302
15 Enhancement of Transmission System Voltage Stability through Local Control of Distribution Networks 311
Gustavo Valverde, Petros Aristidou, and Thierry Van Cutsem
15.1 Introduction 311
15.2 Long-Term Voltage Stability 313
15.2.1 Countermeasures 314
15.3 Impact of Volt-VAR Control on Long-Term Voltage Stability 316
15.3.1 Countermeasures 318
15.4 Test System Description 319
15.4.1 Test System 319
15.4.2 VVC Algorithm 321
15.4.3 Emergency Detection 322
15.5 Case Studies and Simulation Results 323
15.5.1 Results in Stable Scenarios 323
15.5.2 Results in Unstable Scenarios 326
15.5.3 Results with Emergency Support From Distribution 328
16 Electric Power Network Splitting Considering Frequency Dynamics and Transmission Overloading Constraints 337
Nelson Granda and Delia G. Colomé
16.1 Introduction 337
16.1.1 Stage One: Vulnerability Assessment 337
16.1.2 Stage Two: Islanding Process 338
16.2 Network Splitting Mechanism 340
16.2.1 Graph Modeling, Update, and Reduction 341
16.2.2 Graph Partitioning Procedure 342
16.2.3 Load Shedding/Generation Tripping Schemes 343
16.2.4 Tie-Lines Determination 344
16.3 Power Imbalance Constraint Limits 344
16.3.1 Reduced Frequency ResponseModel 345
16.3.2 Power Imbalance Constraint Limits Determination 347
16.4 Overload Assessment and Control 348
16.5 Test Results 349
16.5.1 Power System Collapse 349
16.5.2 Application of Proposed Methodology 351
16.5.3 Performance of Proposed ACIS 354
16.6 Conclusions and Recommendations 356
17 High-Speed Transmission Line Protection Based on Empirical Orthogonal Functions 361
Rommel P. Aguilar and Fabián E. Pérez-Yauli
17.1 Introduction 361
17.2 Empirical Orthogonal Functions 363
17.2.1 Formulation 363
17.3 Applications of EOFs for Transmission Line Protection 365
17.3.1 Fault Direction 366
17.3.2 Fault Classification 367
17.3.3 Fault Location 369
17.4 Study Case 369
17.4.1 Transmission Line Model and Simulation 369
17.4.2 The Power System and Transmission Line 370
17.4.3 Training Data 370
17.4.4 Training Data Matrix 370
17.4.5 Signal Conditioning 373
17.4.6 Energy Patterns 373
17.4.7 EOF Analysis 376
17.4.8 Evaluation of the Protection Scheme 379
17.4.9 Fault Classification 380
17.4.10 Fault Location 382
17.5 Conclusions 383
Study Cases:WECC 9-bus, ATPDrawModels and Parameters 384
18 Implementation of a Real Phasor Based Vulnerability Assessment and Control Scheme: The Ecuadorian WAMPAC System 389
Pablo X. Verdugo, Jaime C. Cepeda, Aharon B. De La Torre, and Diego E. Echeverría
18.1 Introduction 389
18.2 PMU Location in the Ecuadorian SNI 390
18.3 Steady-State Angle Stability 391
18.4 Steady-State Voltage Stability 395
18.5 Oscillatory Stability 398
18.5.1 Power System Stabilizer Tuning 402
18.6 Ecuadorian Special Protection Scheme (SPS) 407
18.6.1 SPS Operation Analysis 409
18.7 Concluding Remarks 410
Index 413
