دانلود کتاب Cascading Failures in Power Grids: Risk Assessment, Modeling, and Simulation (Power Electronics and Power Systems)

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Preface
Contents
1 Introduction of Cascading Failures
1.1 Introduction
1.2 Blackouts
1.2.1 Historical Blackouts
1.2.2 Learnings
1.3 Prevention
1.3.1 Contingency Analysis
1.3.2 Interdependence of Failures
1.4 Mitigation
1.4.1 Protection and Remedial Actions
1.4.2 Wide-Area Measurements
1.4.3 Controlled System Separation
1.5 Restoration
1.6 Risk Assessment
1.6.1 Risk Indices
1.6.2 Challenges
1.6.3 Micro-perspectives on Risk
1.7 Modeling
1.7.1 Requirements
1.7.2 Challenges
1.7.3 Probabilistic Models
1.7.4 Physical Models
1.8 Simulation
1.8.1 Challenges
1.8.2 Simulation of Multi-timescale Dynamics
1.9 Introduction of the Book
References
2 Analyzing Cascading Failures and Blackouts Using Utility Outage Data
2.1 Analysis of Particular Blackouts
2.2 Probability and Risk of Large Blackouts Estimated from Data
2.2.1 How the Blackout Probability Decreases as Size Increases
2.3 Processing Utility Outage Data
2.3.1 Transmission Utility Outage Data
2.3.1.1 Utility Outage Data
2.3.1.2 Extracting Events from Detailed Outage Data
2.3.2 Statistical Patterns in Number of Outages and Generations
2.4 Probabilistic Models Directly Driven by Utility Data
2.4.1 Contingency Motifs for Multiple Outages Initiating Cascading
2.4.2 Influence/Interaction Graphs Driven by Utility Data
2.4.3 Sampling from Utility Data to Replace Simulation
2.4.4 Statistical Models of Outage and Restore Processes
2.4.5 Validating and Calibrating Simulations with Statistical Data
2.4.6 Researcher Access to Utility Data and the Path Forward
References
3 Interaction Models for Analysis and Mitigation of CascadingFailures
3.1 Cascading Failure Interaction Analysis Approach
3.2 Cascading Failure Data Sources for Interaction Analysis
3.2.1 Simulated Data
3.2.2 Utility Outage Data
3.2.3 Data Format
3.2.3.1 Data Format for Line Outages
3.2.3.2 Data Format for Line Outages and the Load Shed
3.3 Formulation of Component Failure Interactions
3.3.1 Interaction Matrix for Line Outages
3.3.2 Coupled Interaction Matrix
3.4 EM Algorithm
3.4.1 A Coin-Flipping Example
3.4.2 Mathematical Foundation
3.5 Estimating Component Failure Interactions
3.5.1 Interaction Estimation for Simulated Line Outage Data
3.5.2 Interaction Estimation for Utility Line Outage Data
3.5.3 Interaction Estimation for Coupled Interaction Matrix
3.6 Identifying Components Critical for Outage Propagation
3.6.1 Expected Number of Outages Following a Component Outage in Generation g
3.6.2 Existence of Unique Positive Solution for (3.31)
3.6.3 Metric Based on Expected Number of Outages
3.7 Identifying Critical Components Considering Spatial Propagation
3.8 Interaction Model
3.8.1 Basic Interaction Model
3.8.2 Generation-Dependent Interaction Model
3.8.2.1 Comparison of Distribution of the Number of Line Outages
3.8.2.2 Comparison of Offspring Mean of Branching Process
3.8.3 Coupled Interaction Model
3.9 Cascading Failure Mitigation
3.9.1 Cascading Failure Mitigation for Utility Line Outage Data
3.9.2 Cascading Failure Mitigation Considering Spatial Propagation
3.9.3 Cascading Failure Mitigation on Coupled Interaction Network
Appendix: Discretization Unit for Each Load Bus
References
4 Probabilistic Analytics of Cascading Failures: Modeling, Assessment, and Application
4.1 Introduction
4.1.1 Probabilistic Models for Cascading Failure
4.1.1.1 Markov Chain Models
4.1.1.2 CASCADE Model
4.1.1.3 Branching Process Model
4.1.2 Sampling Techniques in Probabilistic Models
4.1.2.1 Monte Carlo Simulation
4.1.2.2 Splitting Method
4.1.2.3 Importance Sampling
4.1.2.4 Sequential Importance Sampling
4.1.3 Applications of Cascading Failure Probabilistic Model
4.1.3.1 Critical Components Selection
4.1.3.2 Risk Control
4.1.4 Summary
4.2 Probabilistic Modeling of Cascading Failures
4.2.1 Monte Carlo Simulations for Cascading Failure Analysis
4.2.2 Markov-Sequence-Based Cascading Failure Analysis
4.2.2.1 Mathematical Formulation
4.2.2.2 Sequential Implementation of the Markov Model
4.2.3 Example: The OPA Model
4.3 Cascading Failures Probabilistic Analysis
4.3.1 Importance Sampling for Cascading Failure
4.3.2 Sequential Importance Sampling-Based Probabilistic Analysis
4.3.3 Example
4.3.3.1 Efficiency of Probability Distribution Estimation
4.3.3.2 Variance of Probability Distribution Estimation
4.3.3.3 Impacts of SIS Parameters η
4.3.3.4 Blackout Risk Estimation
4.4 Cascading Failures Probability and Blackout Risk
4.4.1 Relationship Between CoFPFs and Blackout Risk
4.4.1.1 A Generic Formulation of CoFPFs
4.4.1.2 Relationship Construction
4.4.2 Sample-Induced Semi-analytic Characterization
4.4.2.1 Unbiased Estimation of Blackout Risk
4.4.2.2 Sample-Induced Semi-analytic Characterization
4.4.3 Estimating Blackout Risks with Varying CoFPFs
4.4.3.1 Changing a Single CoFPF
4.4.3.2 Changing Multiple CoFPFs
4.4.3.3 Unbiased Estimation of Blackout Risks
4.4.3.4 Some Implications
4.4.4 Example
4.4.4.1 Setting
4.4.4.2 Unbiasedness of the Blackout Risk Estimation
4.4.4.3 Parameter Changes in CoFPFs
4.5 An Application of Probabilistic Analytics to Blackout Risk Mitigation
4.5.1 Preliminaries
4.5.1.1 DTR
4.5.1.2 DTR Function in Cascading Failures
4.5.1.3 Submodular Function
4.5.2 DTR-Based Risk Mitigation Model
4.5.2.1 Modeling of Cascading Failures
4.5.2.2 Risk Model Considering Cascading Failures
4.5.3 DTR-Based Risk Mitigation Model
4.5.3.1 DTR Placement in a Single Line
4.5.3.2 DTR Placement in a Set of Lines
4.5.3.3 Important Sampling Weight Technique
4.5.4 Braess Paradox in Failure Risk Mitigation
4.5.5 Submodular Optimization of Risk Mitigation
4.5.5.1 Optimization Construction
4.5.5.2 Submodular Optimization Approach
4.5.5.3 Estimation Error Analysis
4.5.6 Risk Mitigation Solving Algorithm
4.5.7 Example
4.5.7.1 Important Sampling Weight Approximation
4.5.7.2 Impacts of Weather and System Factors on DTR Risk Mitigation
4.5.7.3 Performance Comparison with Different Placement Strategies
4.6 Summary and Conclusions
References
5 Modeling Cascading Failures in Power Systems: Quasi-Steady-State Models and Dynamic Models
Nomenclature
5.1 Modeling Cascading Failures in Power Systems: Quasi-Steady-State Models and Dynamic Models
5.1.1 Introduction
5.1.2 Metrics to Benchmark Experiments with QSS and Dynamic Simulators
5.1.3 A QSS Model Example
5.1.4 A Dynamic Model Example
5.1.5 Benchmark Experiments
5.1.5.1 Size of Blackouts: Load Distributions
5.1.5.2 Line Distributions
5.1.5.3 Summary of Statistical Similarities and Differences Between dcsimsep and COSMIC
5.1.5.4 Cascade Sequence Benchmarks
5.1.5.5 Rank of Top 5 Critical Components Involved in Initial Outages
5.1.5.6 Rank of Top 5 Critical Components Involved in Subsequent Outages
5.1.6 Conclusions and Future Work
References
6 Multi-timescale Simulation, Risk Assessment, and Mitigation of Cascading Outages
6.1 Multi-timescale Quasi-dynamic Simulation of Cascading Outages
6.1.1 Quasi-dynamic Framework: Simulate Interactions Among All Timescales
6.1.2 Detailed Modeling of Timescales
6.1.2.1 Long-Timescale Processes
6.1.2.2 Mid-Timescale Processes
6.1.2.3 Short-Timescale Processes
6.1.3 Simulating Cascading Outages in US–Canada Northeast Grid Model
6.2 Markovian Tree Model of Cascading Outages
6.2.1 Markovian Tree Model
6.2.2 Modeling of Grid Dispatch Behavior
6.2.3 Modeling of Fast Cascade Processes
6.2.4 An Illustrative Example of Markovian Tree
6.2.5 Discussion on Probability Quantification
6.3 Tree Search for Efficient Risk Assessment
6.3.1 Searching, Instead of Sampling
6.3.2 Convergence Criteria of Risk Assessment
6.4 Risk Estimation and Forward–Backward Search Algorithm
6.4.1 Risk Estimation Index
6.4.1.1 Risk Index Term of System Separation
6.4.1.2 Risk Index Term of Overloading
6.4.1.3 Secondary Risk
6.4.1.4 Computation Complexity of Risk Estimation Index
6.4.2 Forward Searching Using Risk Estimation Index
6.4.3 Backward Updating Risk Estimation Indices
6.4.4 Procedures of Risk Assessment with Markovian Tree Search
6.5 Risk Assessment Case Study on RTS-96 System
6.5.1 Performance of Risk Estimation Index
6.5.2 Efficiency Test of Risk Assessment
6.6 Cascading Outage Mitigation: A Markovian Tree Perspective
6.7 Gradient Formulation on Markovian Tree
6.7.1 Derivative Chain of States on the MT
6.7.1.1 Mid-Term Random Outage
6.7.1.2 Short-Timescale Process
6.7.1.3 Re-dispatch
6.8 Risk Mitigation Based on Tree Search
6.8.1 Efficient Forward–Backward Algorithm for Risk Gradient
6.8.1.1 Iterative Calculation of Terms in Risk Gradient
6.8.1.2 Recursive Form of Risk Gradient
6.8.1.3 Forward–Backward Scheme of Risk Gradient Calculation
6.8.2 Implementation of Risk Management
6.8.2.1 Full Optimization Model of Risk Mitigation (RM)
6.8.2.2 Iterative Risk Mitigation (IRM)
6.8.2.3 Framework of RM/IRM Application
6.9 Use Cases of MT-Based Risk Mitigation
6.9.1 Example of MT-Based Risk Gradient Calculation
6.9.1.1 Convergence of Risk Gradient
6.9.1.2 Effectiveness of Risk Management Based on Risk Gradient
6.9.2 Case Studies of Iterative Risk Mitigation
6.9.2.1 RTS-96 System
6.9.2.2 US–Canada Northeast System
6.9.2.3 1354-Bus Mid-European System josz2016ac
6.10 Summary and Discussions
References
7 Steady-State Simulation of Cascading Outages Considering Frequency
7.1 Introduction
7.2 Approach of SSCOF
7.2.1 Dynamic Load Flow Model
7.2.2 AC Optimal Power Flow Model Considering Frequency
7.2.3 Under-Frequency Load Shedding Scheme in the SSCOF Approach
7.2.4 Protections of Generator Frequency and Transmission Line
7.2.5 Simulation Procedure of the SSCOF Approach
7.3 Case Studies and Analysis
7.3.1 Case Study on the Two-Area System
7.3.2 Case Study on the IEEE 39-Bus System
7.3.2.1 Verification of Steady-State Frequency with the DLF Model
7.3.2.2 UFLS Scheme and Generator Frequency Protection Module
7.3.2.3 Study of Active Power Generation Limits on Frequency Deviation
7.3.2.4 Statistical Comparison Between the SSCOF and the Conventional Approaches
7.3.3 Case Study on the NPCC 140-Bus System
7.3.3.1 Verification of Steady-State Frequency by the DLF Model
7.3.3.2 Detailed Comparison Between the SSCOF and the Conventional Approaches
7.3.3.3 Statistical Comparison Between the SSCOF and the Conventional Approaches
7.4 Conclusion
References
8 Industrial Practices and Criteria Against Cascading Failures
8.1 Introduction
8.2 Operating States
8.3 NERC Standards Related to Cascading
8.4 Planning and Operating Cases and Study Assumptions
8.5 Cascading Methodologies
8.6 Industry Practices in the Analysis of Cascading Outages
8.6.1 IEEE CAMS CFWG Cascading Survey
8.6.2 Prevention of Cascading Outages in Con Edison\'s Network
8.6.3 Applications of RAS by Industry Worldwide to Mitigate Cascading
8.6.3.1 Remedial Action Schemes at Western Electricity Coordinating Council (WECC)
8.6.3.2 Remedial Action Schemes at BPA and CAISO
8.6.3.3 Remedial Action Schemes at Electric Reliability Council of Texas (ERCOT)
8.6.3.4 Remedial Action Schemes at Hydro-Quebec and BC Hydro
8.6.3.5 Remedial Action Schemes in Italy
8.6.3.6 Remedial Action Schemes at ENTSOE
8.6.3.7 Remedial Action Schemes in Brazil
8.6.3.8 Remedial Action Schemes in China
8.6.4 Cascading Analysis at Idaho Power Company (IPC)
8.6.4.1 Prediction and Prevention of Cascading Outages in Idaho Power Network
8.6.4.2 Assessing the Cascading Effects of Extreme Contingencies with Respect to Standards TPL-001-4 and CIP 014-1
8.6.4.3 IPC Experience of Implementing Cascade Analysis Study Using the Node/Breaker Model
8.6.5 ERCOT Experience in Analysis of Cascading Outages
8.6.6 ISONE Experience with Online Cascading Analysis
8.6.7 Cascading Event Reported to NERC in 2018
8.6.8 Practice of Cascading Analysis in Other US Companies
8.6.9 Practice of Cascading Analysis in Countries Outside of North America
8.7 Conclusions
References
Index