Smart Grid Resilience: Extreme Weather, Cyber-Physical Security, and System Interdependency

Description

This book provides a comprehensive overview and in-depth discussion of smart grid resilience. It covers the three most critical resilience problems facing smart grids―resilience against extreme weather, resilience against cyber-physical attacks, and resilience under system inter-dependency. Each of these topics increases the risk of large-scale system-wide cascading failures. In-depth chapters allow the reader to define and quantify the smart grid’s ability to deal with extreme events and the critical infrastructures systems that connect it. Methods for improving system design are introduced along with effective strategies for protecting the system with minimal disruption of power supply and economic and social losses in extreme conditions. Smart Grid Resilience: Extreme Weather, Cyber-Physical Security, and System Interdependency is an essential guide for a broad audience of practicing professionals, including policymakers, electric utility engineers, and transmission and distribution system operators. It will also be a valuable reference for students and researchers. Preface Contents Part I Extreme Weather and Cascading Failure 1 Cascading Failures Under Extreme Temperatures 1.1 Introduction 1.2 Ambient Temperature in Blackout Modeling 1.2.1 Temperature Disturbance 1.2.2 Load Change Under Temperature Disturbances 1.2.3 Dynamic Line Rating 1.2.4 Probability of Line Tripping 1.2.5 Probability of Generator Tripping 1.3 Modeling Protection and Control Strategies 1.3.1 Undervoltage Load Shedding 1.3.2 Operator Re-dispatch 1.4 Timing of Events 1.5 Voltage Stability Margin Calculation 1.6 Blackout Model Considering Temperature 1.7 Simulation Results 1.7.1 Model Implementation and Parameter Settings 1.7.2 Typical Simulation Run Without Operator Re-dispatch 1.7.3 Typical Simulation Run with Operator Re-dispatch 1.7.4 Number of Simulations 1.7.5 Impact of Temperature Disturbances and Size of Selected Area 1.7.6 Identifying the Most Vulnerable Buses/Locations 1.7.7 Impact of Control Strategies References 2 Cascading Failure Interaction Analysis 2.1 Introduction 2.2 Estimating Interactions Between Component Failures 2.3 EM Algorithm 2.3.1 A Coin-Flipping Example 2.3.2 Mathematical Foundation 2.4 Estimating Component Failure Interactions by EM Algorithm 2.5 Determining the Number of Cascades Needed 2.5.1 Lower Bound of M 2.5.2 Lower Bound of Mu 2.6 Results 2.6.1 Number of Cascades Needed 2.6.2 Interaction Matrix and Interaction Network 2.6.3 Identified Key Links and Key Components 2.6.4 Validation of Estimated Interactions 2.6.5 Cascading Failure Mitigation 2.6.6 Efficiency Improvement References 3 Integrated Preventive and Emergency Responses 3.1 Introduction 3.2 Integrated Resilience Response 3.3 Mathematical Formulation 3.3.1 Preventive Response 3.3.2 Damage From Natural Disasters 3.3.3 Emergency Response 3.3.4 Integration of Preventive and Emergency Responses 3.4 Solution Methodology 3.4.1 NC&CG Decomposition-Based Algorithm 3.4.2 Computational Efficiency Improvement Techniques 3.5 Results on PJM Five-Bus System 3.6 Results on IEEE One-Area RTS-96 System 3.7 Results on IEEE Three-Area RTS-96 System Appendix 1: PJM Five-Bus System Appendix 2: IEEE One-Area RTS-96 System References Part II Cybersecurity of Smart Grid Monitoring 4 Risk Mitigation against Cyber Attacks Based on Dynamic State Estimation 4.1 Introduction 4.2 Power System Dynamic Model 4.2.1 10th-Order Nonlinear Power System Model 4.2.2 Linearized Power System Model 4.3 Unknown Inputs & Attack-Threat Model 4.3.1 Modeling Unknown Inputs 4.3.2 Modeling Cyber Attacks 4.4 DSE under UIs and CAs 4.4.1 Sliding-Mode Observer for Power Systems 4.4.2 SMO Dynamics & Design Algorithm 4.5 Asymptotic Reconstruction of UIs & CAs 4.5.1 Estimating Unknown Inputs 4.5.2 Estimating CAs 4.5.3 Attack Detection Filter 4.6 Risk Mitigation—A Dynamic Response Model 4.6.1 Weighted Deterministic Threat Level Formulation 4.6.2 Dynamic Risk Mitigation Optimization Problem 4.6.3 Dynamic Risk Mitigation Algorithm 4.7 Case Studies 4.7.1 Scenario I: Dynamic Reconstruction of UI & DSE 4.7.2 Scenario II: DSE Under UIs & CAs References 5 Comparing Kalman Filters and Observers Against Cyber Attacks 5.1 Introduction 5.2 4th-Order Nonlinear Power System Model 5.3 Model Uncertainty and Cyber Attacks 5.3.1 Model Uncertainty 5.3.2 Cyber Attacks 5.4 DSE Algorithms 5.4.1 Kalman Filters for Power System DSE 5.4.1.1 EKF 5.4.1.2 UKF 5.4.1.3 CKF 5.4.2 Nonlinear Observers for Power System DSE 5.5 Numerical Results 5.5.1 Scenario 1: Data Integrity Attack 5.5.2 Scenario 2: DoS Attack and Scenario 3: Replay Attack 5.5.3 Discussion on Model Uncertainty Estimation 5.5.4 Discussion on Cyber Attack Detection 5.5.5 Non-Gaussian Measurement Noise 5.5.6 Computational Efficiency 5.6 Summary References 6 Self-Healing PMU Network Against Cyber Attacks 6.1 Introduction 6.2 Motivation for Self-Healing PMU Network 6.3 System Model 6.3.1 Power System Observability 6.3.2 Rules in Network Switches 6.4 Optimization Formulation 6.4.1 PMU Connection Status Constraints (PCSCs) 6.4.2 Power System Observability Constraints (PSOCs) 6.4.3 PDC Connection Space Constraints (PSCs) 6.4.4 PMU Reconnection Constraints (PRCs) 6.4.5 Switch Rule Space Capacity Constraints (SRSCCs) 6.4.6 Routing Policy Constraints (RPCs) 6.4.7 Endpoint Policy Constraints (EPCs) 6.4.8 Optimization for Self-Healing Mechanism 6.4.8.1 Stage 1: Recover System Observability 6.4.8.2 Maximize System Observability 6.5 Greedy Heuristic Algorithm 6.6 Case Study on IEEE 30-Bus System 6.7 Performance Evaluation of Stage 1 6.7.1 Impact of Scale of Attacks 6.7.2 Impact of Hardware Resources 6.8 Performance Evaluation of Stage 2 References Part III Cyber-Physical Security for Distributed Energy Resources 7 Cyber-Physical Security Research Framework for Distributed Energy Resources 7.1 Introduction 7.2 Cyber-Physical Power System with Large-Scale DER Deployments 7.2.1 Generic Architecture of Power Systems with DERs 7.2.2 Challenges of Maintaining DER Cybersecurity 7.3 Overview of DER Cyber-Physical Security Research Framework 7.4 Potential Cyber Attacks on Cyber-Physical Power System with DERs 7.4.1 Cyber-Physical-Threat Modeling 7.4.2 Threat Scenarios Targeting DER 7.4.3 Attack Threat Ranking 7.5 Attack Impact Analysis and Metrics 7.6 DER Cyber-Physical Security Design Principles 7.7 Attack Resilience at Cyber, Physical Device, and Utility Layers 7.7.1 Cyber Layer Attack Resilience 7.7.1.1 Cyber Layer Attack Prevention 7.7.1.2 Cyber Layer Attack Detection 7.7.1.3 Cyber Layer Attack Response 7.7.2 Physical Device Layer Attack Resilience 7.7.2.1 Physical Device Layer Attack Prevention 7.7.2.2 Physical Device Layer Attack Detection 7.7.2.3 Physical Device Layer Attack Response 7.7.3 Utility Layer Attack Resilience 7.7.3.1 Utility Layer Attack Prevention 7.7.3.2 Utility Layer Attack Detection 7.7.3.3 Utility Layer Attack Response References 8 Distributed Load Sharing Under Cyber Attacks 8.1 Introduction 8.2 Inverter-Based Microgrid Structure 8.2.1 Physical Layer 8.2.2 Cyber Layer 8.3 System Dynamic Model 8.3.1 Small-Signal Model 8.3.2 Active Power Reference 8.4 System Performance Under Attack 8.4.1 FDI Attack Against Distributed Load Sharing Control 8.4.2 Effects of FDI Attack on Microgrid Performance 8.5 Case Studies 8.5.1 Stable Region 8.5.2 System Performance Under Attack Strategy 1 8.5.3 System Performance under Attack Strategy 2 References 9 Deep Learning Based Attack Detection for Microgrid Control 9.1 Introduction 9.2 Distributed Control and FDI Attack 9.2.1 Cyber-Physical Representation of AC Microgrids 9.2.2 Secondary Control Problem Formulation 9.2.3 Distributed Control Algorithm 9.2.4 FDI Attack Against Distributed Control 9.3 Deep Learning Based Multi-label Attack Detection 9.3.1 Multi-label Classification Problem Formation 9.3.2 Data Preparation and Preprocessing 9.3.3 Deep Learning Models 9.4 Performance Evaluation 9.4.1 Test System and Control Performance 9.4.2 Deep Learning Performance Metrics 9.4.3 FDI Attack Detection Results References Part IV Smart Grid Resilience Under System Interdependency 10 Interdependency Between Power System Outages by Branching Process 10.1 Introduction 10.2 Estimating Multi-type Branching Process Parameters 10.3 Estimating Joint Probability Distribution of Total Outages 10.3.1 n-Type Branching Process 10.3.2 Two-Type Branching Process 10.3.3 Validation of Estimated Joint Distribution 10.4 Number of Cascades Needed 10.4.1 Determining Lower Bound for M 10.4.2 Determining Lower Bound for Mu 10.5 Estimated Parameters of Branching Processes 10.6 Estimated Joint Distribution of Total Outages 10.7 Predicted Joint Distribution from One Type of Outage 10.8 Estimated Propagation of Three Types of Outages References 11 Interdependency Between Power System Outages by Coupled Interaction Model 11.1 Introduction 11.2 Coupled Interaction Matrix 11.2.1 Definition of Coupled Interaction Matrix 11.2.2 Definition of an Auxiliary Matrix 11.3 Estimating Coupled Interaction Matrix by EM Algorithm 11.4 Coupled Interaction Model for Cascading Failure Simulation 11.5 Critical Link Identification 11.6 Coupled Interaction Network of IEEE 300-Bus System 11.7 Validation of Coupled Interaction Model 11.8 Choosing Critical Links for Mitigation 11.9 Cascading Failure Mitigation Appendix: Discretization Unit for Each Load Bus References 12 Interdependency Between Smart Grid and Transportation Network 12.1 Introduction 12.2 Mathematical Modeling 12.2.1 Renewable Energy Investor Modeling 12.2.2 Conventional Generators 12.2.3 ISO Modeling 12.2.4 Driver Modeling 12.2.5 Market Clearing Conditions 12.3 Computational Approach 12.4 Results on Three-Node Test System 12.4.1 Effects on Equilibrium Prices 12.4.2 Effects on Renewable Investment 12.4.3 Effects on System Costs 12.4.4 Effects on Flow Distribution 12.5 Results on Sioux Falls Road Network and IEEE 39-Bus Test System Appendix: Proofs References Index