Power Grid Resilience against Natural Disasters: Preparedness, Response, and Recovery (IEEE Press)

POWER GRID RESILIENCE AGAINST NATURAL DISASTERS How to protect our power grids in the face of extreme weather events The field of structural and operational resilience of power systems, particularly against natural disasters, is of obvious importance in light of climate change and the accompanying increase in hurricanes, wildfires, tornados, frigid temperatures, and more. Addressing these vulnerabilities in service is a matter of increasing diligence for the electric power industry, and as such, targeted studies and advanced technologies are being developed to help address these issues generally―whether they be from the threat of cyber-attacks or of natural disasters. Power Grid Resilience against Natural Disasters provides, for the first time, a comprehensive and systematic introduction to resilience-enhancing planning and operation strategies of power grids against extreme events. It addresses, in detail, the three necessary steps to ensure power grid success: the preparedness prior to natural disasters, the response as natural disasters unfold, and the recovery after the event. Crucially, the authors put forward state-of-the-art methods towards improving today’s practices in managing these three arenas. Power Grid Resilience against Natural Disasters readers will also find: Data, tables, and illustrations to supplement and clarify the points put forward in each chapter Case studies on realistic power systems and industry standards and practices related to the topics covered Potential to be a supplementary text in advanced level power engineering courses Power Grid Resilience against Natural Disasters will be of interest to specialists and engineers, as well as planners and operators from industry. It can also be a useful resource for senior undergraduate students, postgraduate students, researchers, and research libraries. More, it will appeal to all readers with a strong background in power system analysis, operation and control, optimization methods, the Markov decision process, and probability and statistics. Cover Title Page Copyright Contents About the Authors Preface Acknowledgments Part I Introduction Chapter 1 Introduction 1.1 Power Grid and Natural Disasters 1.2 Power Grid Resilience 1.2.1 Definitions 1.2.2 Importance and Benefits 1.2.2.1 Dealing with Weather‐Related Disastrous Events 1.2.2.2 Facilitating the Integration of Renewable Energy Sources 1.2.2.3 Dealing with Cybersecurity‐Related Events 1.2.3 Challenges 1.3 Resilience Enhancement Against Disasters 1.3.1 Preparedness Prior to Disasters 1.3.1.1 Component‐Level Resilience Enhancement 1.3.1.2 System‐Level Resilience Enhancement 1.3.2 Response as Disasters Unfold 1.3.2.1 System State Acquisition 1.3.2.2 Controlled Separation 1.3.3 Recovery After Disasters 1.3.3.1 Conventional Recovery Process 1.3.3.2 Microgrids for Electric Service Recovery 1.3.3.3 Distribution Grid Topology Reconfiguration 1.4 Coordination and Co‐Optimization 1.5 Focus of This Book 1.6 Summary References Part II Preparedness Prior to a Natural Disaster Chapter 2 Preventive Maintenance to Enhance Grid Reliability 2.1 Component‐ and System‐Level Deterioration Model 2.1.1 Component‐Level Deterioration Transition Probability 2.1.2 System‐Level Deterioration Transition Probability 2.1.3 Mathematical Model without Harsh External Conditions 2.2 Preventive Maintenance in Consideration of Disasters 2.2.1 Potential Disasters Influencing Preventive Maintenance 2.2.2 Preventive Maintenance Model with Disasters Influences 2.2.2.1 Probabilistic Model of Repair Delays Caused By Harsh External Conditions 2.2.2.2 Activity Vectors Corresponding to Repair Delays 2.2.2.3 Expected Cost 2.3 Solution Algorithms 2.3.1 Backward Induction 2.3.2 Search Space Reduction Method 2.4 Case Studies 2.4.1 Data Description 2.4.2 Case I: Verification of the Proposed Model 2.4.2.1 Verifying the Model Using Monte Carlo Simulations 2.4.2.2 Selection of Optimal Maintenance Activities 2.4.2.3 Influences of Harsh External Conditions on Maintenance 2.4.3 Case II: Results Simulating the Zhejiang Electric Power Grid 2.5 Summary and Conclusions Nomenclature References Chapter 3 Preallocating Emergency Resources to Enhance Grid Survivability 3.1 Emergency Resources of Grids against Disasters 3.2 Mobile Emergency Generators and Grid Survivability 3.2.1 Microgrid Formation 3.2.2 Preallocation and Real‐Time Allocation 3.2.3 Coordination with Conventional Restoration Procedures 3.3 Preallocation Optimization of Mobile Emergency Generators 3.3.1 A Two‐Stage Stochastic Optimization Model 3.3.2 Availability of Mobile Emergency Generators 3.3.3 Connection of Mobile Emergency Generators 3.3.4 Coordination of Multiple Flexibility in Microgrids 3.4 Solution Algorithms 3.4.1 Scenario Generation and Reduction 3.4.2 Dijkstra's Shortest‐Path Algorithm 3.4.3 Scenario Decomposition Algorithm 3.5 Case Studies 3.5.1 Test System Introduction 3.5.2 Demonstration of the Proposed Dispatch Method 3.5.3 Capacity Utilization Rate 3.5.4 Importance of Considering Traffic Issue and Preallocation 3.5.5 Computational Efficiency 3.6 Summary and Conclusions Nomenclature References Chapter 4 Grid Automation Enabling Prompt Restoration 4.1 Smart Grid and Automation Systems 4.2 Distribution System Automation and Restoration 4.3 Prompt Restoration with Remote‐Controlled Switches 4.4 Remote‐Controlled Switch Allocation Models 4.4.1 Minimizing Customer Interruption Cost 4.4.2 Minimizing System Average Interruption Duration Index 4.4.3 Maximizing System Restoration Capability 4.5 Solution Method 4.5.1 Practical Candidate Restoration Strategies 4.5.2 Model Transformation 4.5.3 Linearization and Simplification Techniques 4.5.4 Overall Solution Process 4.6 Case Studies 4.6.1 Illustration on a Small Test System 4.6.1.1 Results of the CIC‐oriented Model 4.6.1.2 Results of the SAIDI‐oriented Model 4.6.1.3 Results of the RL‐oriented Model 4.6.1.4 Comparisons 4.6.2 Results on a Large Test System 4.7 Impacts of Remote‐Controlled Switch Malfunction 4.8 Consideration of Distributed Generations 4.9 Summary and Conclusions Nomenclature of RCS‐Restoration Models Nomenclature of RCS Allocation Models References Part III Response as a Natural Disaster Unfolds Chapter 5 Security Region‐Based Operational Point Analysis for Resilience Enhancement 5.1 Resilience‐Oriented Operational Strategies 5.2 Security Region during an Unfolding Disaster 5.2.1 Sequential Security Region 5.2.2 Uncertain Varying System Topology Changes 5.3 Operational Point Analysis Resilience Enhancement 5.3.1 Sequential Security Region 5.3.2 Sequential Security Region with Uncertain Varying Topology Changes 5.3.3 Mapping System Topology Changes 5.3.4 Bilevel Optimization Model 5.3.5 Solution Process 5.4 Case Studies 5.5 Summary and Conclusions Nomenclature References Chapter 6 Proactive Resilience Enhancement Strategy for Transmission Systems 6.1 Proactive Strategy Against Extreme Weather Events 6.2 System States Caused by Unfolding Disasters 6.2.1 Component Failure Rate 6.2.2 System States on Disasters' Trajectories 6.2.3 Transition Probabilities Between Different System States 6.3 Sequentially Proactive Operation Strategy 6.3.1 Sequential Decision Processes 6.3.2 Sequentially Proactive Operation Strategy Constraints 6.3.3 Linear Scalarization of the Model 6.3.4 Case Studies 6.3.4.1 IEEE 30‐Bus System 6.3.4.2 A Practical Power Grid System 6.4 Summary and Conclusions Nomenclature References Chapter 7 Markov Decision Process‐Based Resilience Enhancement for Distribution Systems 7.1 Real‐Time Response Against Unfolding Disasters 7.2 Disasters' Influences on Distribution Systems 7.2.1 Markov States on Disasters' Trajectories 7.2.2 Transition Probability Between Markov States 7.3 Markov Decision Processes‐Based Optimization Model 7.3.1 Markov Decision Processes‐based Recursive Model 7.3.2 Operational Constraints 7.3.2.1 Radiality Constraint 7.3.2.2 Repair Constraint 7.3.2.3 Power Flow Constraint 7.3.2.4 Power Balance Constraint 7.3.2.5 Line Capacity Constraint 7.3.2.6 Voltage Constraint 7.4 Solution Algorithms – Approximate Dynamic Programming 7.4.1 Solution Challenges 7.4.2 Post‐decision States 7.4.3 Forward Dynamic Algorithm 7.4.4 Proposed Model Reformulation 7.4.5 Iteration Process 7.5 Case Studies 7.5.1 IEEE 33‐Bus System 7.5.1.1 Data Description 7.5.1.2 Estimated Values of Post‐Decision States 7.5.1.3 Dispatch Strategies with Estimated Values of Post‐Decision States 7.5.2 IEEE 123‐Bus System 7.5.2.1 Data Description 7.5.2.2 Simulated Results 7.6 Summary and Conclusions Nomenclature References Part IV Recovery After a Natural Disaster Chapter 8 Microgrids with Flexible Boundaries for Service Restoration 8.1 Using Microgrids in Service Restoration 8.2 Dynamically Formed Microgrids 8.2.1 Flexible Boundaries in Microgrid Formation Optimization 8.2.2 Radiality Constraints and Topological Flexibility 8.3 Mathematical Formulation of Radiality Constraints 8.3.1 Loop‐Eliminating Model 8.3.2 Path‐Based Model 8.3.3 Single‐Commodity Flow‐Based Model 8.3.4 Parent–Child Node Relation‐Based Model 8.3.5 Primal and Dual Graph‐Based Model 8.3.6 Spanning Forest‐Based Model 8.4 Adaptive Microgrid Formation for Service Restoration 8.4.1 Formulation and Validity 8.4.2 Tightness and Compactness 8.4.3 Applicability and Application 8.5 Case Studies 8.5.1 Illustration on a Small Test System 8.5.2 Results on a Large Test System 8.5.3 LinDistFlow Model Accuracy 8.6 Summary and Conclusions 8.A.1 Proof of Theorem 8.1 8.A.2 Proof of Proposition 8.1 Nomenclature of Spanning Tree Constraints Nomenclature of MG Formation Model References Chapter 9 Microgrids with Mobile Power Sources for Service Restoration 9.1 Grid Survivability and Recovery with Mobile Power Sources 9.2 Routing and Scheduling Mobile Power Sources in Microgrids 9.3 Mobile Power Sources and Supporting Facilities 9.3.1 Availability 9.3.2 Grid‐Forming Functions 9.3.3 Cost‐Effectiveness 9.4 A Two‐Stage Dispatch Framework 9.4.1 Proactive Pre‐Dispatch 9.4.2 Dynamic Routing and Scheduling 9.5 Solution Method 9.5.1 Column‐and‐Constraint Generation Algorithm 9.5.2 Linearization Techniques 9.6 Case Studies 9.6.1 Illustration on a Small Test System 9.6.1.1 Results of MPS Proactive Pre‐positioning 9.6.1.2 Results of MPS Dynamic Dispatch 9.6.2 Results on a Large Test System 9.7 Summary and Conclusions Nomenclature References Chapter 10 Co‐Optimization of Grid Flexibilities in Recovery Logistics 10.1 Post‐Disaster Recovery Logistics of Grids 10.1.1 Power Infrastructure Recovery 10.1.2 Microgrid‐Based Service Restoration 10.1.3 A Co‐Optimization Approach 10.2 Flexibility Resources in Grid Recovery Logistics 10.2.1 Routing and Scheduling of Repair Crews 10.2.2 Routing and Scheduling of Mobile Power Sources 10.2.3 Grid Reconfiguration and Operation 10.3 Co‐Optimization of Flexibility Resources 10.4 Solution Method 10.4.1 Pre‐assigning Minimal Repair Tasks 10.4.2 Selecting Candidate Nodes to Connect Mobile Power Sources 10.4.3 Linearization Techniques 10.5 Case Studies 10.5.1 Illustration on a Small Test System 10.5.2 Results on a Large Test System 10.5.3 Computational Efficiency 10.5.4 LinDistFlow Model Accuracy 10.6 Summary and Conclusions 10.A.1 Proof of Proposition 10.1 References Index EULA "Learn to protect our power grids in the face of extreme weather events The field of structural and operational resilience of power systems, particularly against natural disasters, is of obvious importance in light of climate change and the accompanying increase in hurricanes, wildfires, tornados, frigid temperatures, and more. Addressing these vulnerabilities in service is a matter of increasing diligence for the electric power industry, and as such, targeted studies and advanced technologies are being developed to help address these issues generally--whether they be from the threat of cyber-attacks or of natural disasters. Power Grid Resilience against Natural Disasters provides, for the first time, a comprehensive and systematic introduction to resilience-enhancing planning and operation strategies of power grids against extreme events. It addresses, in detail, the three necessary steps to ensure power grid success: the preparedness prior to natural disasters, the response as natural disasters unfold, and the recovery after the event. Crucially, the authors put forward state-of-the-art methods towards improving today's practices in managing these three arenas"-- Provided by publisher