About the Authors xi
Foreword by Professor Chongqing Kang xv
Foreword by Professor Ian Dobson xvii
Preface xix
1 From Reliability to Resilience 1
1.1 Why Power System Resilience? 1
1.2 The Historical Approaches to Power System Reliability 3
1.2.1 The Historical N-k Network Security Criterion 3
1.2.2 The More Advanced Probabilistic Network Security 3
1.3 Need for a (Tail)Risk-Aware Approach 4
1.4 Inspiration from Other Economic and Engineering Areas 5
1.4.1 Risk Hedging and Portfolio Optimization 5
1.4.2 Risk-Aware Design in Structural and Civil Engineering 6
1.5 From Reliability to Resilience Paradigm 7
1.5.1 Key Differentiators Between Reliability and Resilience 7
1.5.2 Expanding Resilience Practices 8
1.5.2.1 Broadening the Scope of Contingencies 8
1.5.2.2 Focusing on High-Risk Scenarios 8
1.5.2.3 Incorporating Environmental Interactions 8
1.5.2.4 Tailoring Strategies to Natural Threats 8
1.5.2.5 Integrating Diverse Measures and Technologies 9
1.5.2.6 Emphasizing Dynamics and Recovery 9
1.6 Fundamentals of Power System Resilience 9
1.6.1 Contribution to the Field 10
1.6.2 Closing Remarks 10
References 11
2 Conceptualizing and Contextualizing Power Grid Resilience 13
2.1 Shocks and Stresses on Critical Power Infrastructure 13
2.2 Defining Power System Resilience 15
2.3 Resilience Capacities and Features 18
2.4 Comparing and Clarifying Reliability and Resilience 20
2.5 Conceptualizing Power System Resilience to External Shocks 22
2.6 Domains of Resilience 26
2.6.1 Infrastructure Resilience 26
2.6.2 Operational Resilience 27
2.6.3 Organizational Resilience 27
2.6.4 Community Resilience 27
References 28
3 System Resilience Assessment and Quantification 31
3.1 Needed Resilience Metrics for Power Systems 31
3.2 Quantifying the Resilience Trapezoid 32
3.2.1 The FLEP and Area Metric Systems 32
3.2.2 Fragility-Driven Resilience Assessment Against Windstorms 35
3.3 CVaR-Driven Resilience Assessment 43
3.3.1 Illustrative Example of CVaR Resilience Assessment Against Wind Events 44
3.4 AC Cascading Modeling for Resilience Applications 45
3.4.1 Recursive Application of Protection Mechanisms 48
3.4.2 Obtaining a Solvable PF 50
3.4.3 Implementation of Protection Mechanisms 50
3.4.3.1 Cascade Visualization 54
3.4.4 Integration into Resilience Metric Frameworks 54
References 58
4 Addressing Data Granularity and Ambiguity 61
4.1 Understanding Outage Models on Power Systems 61
4.2 Homogeneous Versus Distributed Representations of Failure Hazard on Overhead Lines 66
4.2.1 Homogeneous Representations of Outage Risk on Lines 67
4.3 Distributed Failure Risk Representation on Lines 67
4.4 Method for Representing Spatial Risk of a Natural Hazard 70
4.5 Data Acquisition 70
4.5.1 Calculating Exposure of System to Natural Hazards 71
4.5.2 Modeling Failure Probability 73
4.5.3 Correcting Wind Speed Data for Different Heights of Asset 76
4.5.4 Interpolation of Datasets for the Purposes of Resilience Studies 77
4.5.5 A Tractable Method for Calculating Wind Power Outputs in Extreme Wind Events 78
4.5.6 A Case Study Using Demonstrated Methods on the Northern Scottish Transmission System 79
4.6 Exercises 84
4.7 Correlated Natural Hazards 86
4.8 Incorporating Imprecise Fragility Curves in Decision-Making Models 90
4.9 Summary 91
Acknowledgments 92
Reference 92
5 Resilient Investment Planning 95
5.1 Network Investment Decisions: Unraveling the Plannerâs Trilemma 95
5.1.1 Expanding and Hardening Solutions 95
5.1.2 Digitalized, Non-Wire and Smart Grid Solutions 96
5.1.3 The Resilience Enhancement Trilemma 97
5.2 Resilience and Risk Metrics for Risk-Averse Decision-Making 98
5.2.1 A Probabilistic Risk Metric 100
5.2.2 Risk Versus Resilience Metrics 101
5.2.3 Dependencies and Common Mode 101
5.2.4 Example of Probability Capacity Tables Accounting for Dependencies in a Double Circuit 102
5.2.5 Deterministic or âRobustâ Approach to Risk 103
5.2.6 Distributionally Robust Approach to Risk 104
5.3 Mathematical Frameworks to Plan Resilient Grids 104
5.3.1 Possible Theoretical Formulations: From Robust to Stochastic Network Planning 104
5.3.1.1 Two-Stage Framework 104
5.3.1.2 Multi-Stage Framework with Two Layers of Uncertainty 105
5.3.2 CVaR Formulation 106
5.3.3 Optimization via Simulation Approach: A Two-Stage Stochastic Model 106
5.3.4 Advanced Resilient Investment Planning Models 109
5.4 Merging Resilience and Reliability Criteria for Practical Decisions 110
5.4.1 Illustrative Case Study 110
5.4.2 Reliability I: Adequacy 110
5.4.3 Reliability II: Security 110
5.4.4 Resilience 111
5.4.5 Combining Reliability and Resilience 113
5.5 Realistic Application to Earthquakes in Chile 113
5.5.1 Case Study Description 114
5.5.2 Results: Portfolio Solutions for Resilience Enhancement 115
5.6 Main Grid Resilience Investments Versus DER 116
5.6.1 A Suitable Methodology to Design DER Portfolios Against Wildfires 116
5.6.2 Illustrative Case Study Example 118
5.6.3 The Classic Nâ"1 Design 119
5.6.4 The Resilient Design 120
5.6.5 The Actual Risks Associated with the Current Security Standards 120
5.7 Investment Versus Operational Measures 121
5.7.1 A Comment on Resilience and CVaR Effects on Portfolio Diversification 122
5.8 Fairness Considerations in Resilience 122
5.9 Beyond Networks: Long-Term Duration Energy Storage to Enhance Future Energy System Resilience Against Prolonged Low Output of RES 124
References 127
6 Operational Resilience Planning 131
Nomenclature 131
6.1 Introduction 132
6.2 Smart Operational Measures 135
6.3 Preventive Unit Commitment to Enhance Power System Resilience Under Extreme Weather Events 136
6.3.1 Resilience Preventive Unit Commitment Using Robust Optimization 138
6.3.1.1 Mathematical Formulation 138
6.3.1.2 Problem Reformulation and Solution Algorithm 140
6.3.1.3 Case Study Applications 141
6.3.2 Machine Learning Techniques to Deal with Multiple Line Failures Under Extreme Events 145
6.3.2.1 Mathematical Formulation 145
6.3.2.2 Machine Learning Assisted Stochastic Unit Commitment 147
6.3.2.3 Case Study Applications 148
6.4 Defensive Islanding to Enhance Power System Resilience Under Extreme Weather Events 151
6.4.1 Defensive Islanding Algorithm 151
6.4.2 Approach for Determining when to Apply Defensive Islanding 153
6.4.3 Case Study Applications 154
6.5 Resilience Enhancement in Low-Carbon, Low-Inertia Power Systems 160
6.5.1 Dynamic Network Model 161
6.5.2 Mathematical Formulation 162
6.5.3 Case Study Applications 163
6.5.3.1 Base Case Results 164
6.5.3.2 SECOPF Results 165
6.6 Appendix 167
6.6.1 Reformulation and Solution Algorithm of Tri-level Problems 167
6.6.2 Solution Algorithm 168
References 168
7 Resilience by Distributed Energy Resources and Microgrids 177
7.1 Local and Bulk System Resilience 177
7.1.1 Overview of Microgrids 178
7.1.2 Resilient Decarbonization of Multi-Energy Microgrids 179
7.1.3 Electrified Transport Sector and Mobile Sources Enhancing Resilience 181
7.2 Resilience Support by Real-World Microgrids 181
7.2.1 Princeton Microgrid Against Hurricanes â" United States 181
7.2.2 The Sendai Microgrid During and After the Tsunami and Large-Scale Earthquake â" Japan 182
7.2.3 Blue Lake Rancheria Microgrid Against Wildfires â" United States 182
7.2.4 Texas Microgrids Against Winter Storms â" United States 183
7.2.5 Louisiana Microgrids Against Hurricanes â" United States 184
7.2.6 Microgrids Against Earthquakes â" Haiti 185
7.2.7 The Kythnos Microgrid During Natural Disasters â" Greece 185
7.2.8 Support from Mobile Generators â" Ad Hoc Microgrids Against Wildfires â" Greece 186
7.2.9 Support from Local Generation Islanded Operation of Constituci³n Against Wildfires â" Chile 186
7.3 DER/Microgrids Role in Strengthening Resilience 187
7.3.1 Resilience Enhancement at All Power System Levels 187
7.3.2 Resilience Enhancement in All Resilience Phases 187
7.4 Preventive and Corrective Microgrid Formation 189
7.4.1 The Base Model 189
7.4.1.1 Partitioning Constraints 190
7.4.1.2 Operating Constraints 194
7.4.1.3 Objective Function 194
7.4.1.4 Numerical Results 195
7.4.2 Advances of the Base Model 197
7.4.2.1 Reformulating the Partitioning Constraints 197
7.4.2.2 Further Advances in MGs Formulation 199
7.4.2.3 Integration with DER Scheduling 200
7.5 Microgrids for Resilience-Oriented Restoration 201
7.5.1 Microgrids Aided Distribution System Restoration 201
7.5.1.1 Operating Constraints 202
7.5.1.2 Connectivity and Sequence Constraints 203
7.5.1.3 Topological Constraints 204
7.5.1.4 Initial Condition Constraints 204
7.5.1.5 Optimization Formulation 204
7.5.2 Restoration of a Microgrid 205
7.5.2.1 Microgrid Restoration Sequence 205
7.5.2.2 Protection Considerations 206
7.5.2.3 Decentralized Techniques 206
7.6 Resilience-Oriented Preventive and Emergency DER Scheduling 207
7.6.1 Problem Formulation 207
7.6.2 Study Case Application 209
7.6.2.1 Test Network and Simulation Data 209
7.6.2.2 Operation Against Approaching Wildfire 211
References 215
8 Resilience of Low-Carbon Power Systems: Standards, Market, Regulatory, and Policy Aspects 221
8.1 Introduction 221
8.2 Weather and Low-Carbon Grid Reliability 221
8.3 Weather and Low-Carbon Grid Resilience 222
8.4 From Reliability to Resilience in Low-Carbon Grids: Need for New Security Standards 223
8.5 Toward a New Regulatory Framework for Resilience 225
8.6 Measuring Power System Resilience 227
8.7 The Economics of Resilience: âValue of Customer Resilienceâ and Risk Aversion 229
8.8 The Economics of Resilience: Costâ"Benefit Analysis and Risk Aversion 232
8.9 The Economics of Resilience: Cost-Effectiveness Analysis 232
8.10 Regulation, Policy, and Markets: Who Should Provide Resilience? 233
8.11 Regulation, Policy and Markets: Who Should Pay for Resilience? 235
8.12 Resilience, Decarbonization Policies, and Digitalization Paradigm 235
8.13 A Regulatory Perspective on Resilience: Final Considerations 237
References 238
Index 241
