Risk Modeling, Analysis and Control of Multi-energy Systems

Preface
Contents
1 Multi-energy Systems and Risk Evaluation
1.1 Descriptions of Multi-energy Systems
1.2 Risk Issues of Multi-energy Systems
1.3 Challenges in Risk Modeling and Analysis of Multi-energy Systems
1.4 Organization of This Book for Risk Analysis and Control of Multi-energy Systems
References
2 A Framework for Risk Modeling of Integrated Electricity and Gas Systems Utilizing Universal Generating Function Techniques
2.1 Introduction
2.2 Risk Modeling of the Natural Gas System
2.2.1 Risk Model for Gas Source
2.2.2 Risk Model for Gas Compressor
2.2.3 Risk Model for Gas Storage
2.2.4 Risk Analysis of Natural Gas System
2.2.5 Risk Indices for Gas System
2.3 Risk Modeling of Power System Considering the Impacts of the Gas System
2.3.1 Risk Model for GPP Based on GTP Operator
2.3.2 Risk Model for Coal-Fired Generators
2.3.3 Risk Model for Generation Provider
2.3.4 Risk Analysis of Power System
2.3.5 Risk Indices for Power System
2.4 System Studies
2.5 Conclusions
References
3 Short-Term Risk Evaluation of Integrated Electricity and Gas Systems Considering Dynamics of Gas Flow
3.1 Introduction
3.2 Short-Term Risk Models of IEGS Components Considering Gas Flow Dynamics
3.2.1 Short-Term Risk Model of the Gas Source
3.2.2 Gas Flow Dynamics in the Pipeline
3.2.3 Short-Term Risk Models of the GFU and Traditional Fossil Unit
3.3 Multi-stage Risk Management Scheme for IEGS Considering Gas Flow Dynamics
3.3.1 Framework of the Multi-stage Risk Management
3.3.2 First Stage: Re-dispatch in the Contingency State Using Integrated Electricity-Gas Optimal Power Flow
3.3.3 Second Stage: Operating Condition of the Gas System Using TSA
3.3.4 Third Stage: Operating Condition of the Electricity System Using Optimal Power Flow
3.4 Short-Term Risk Evaluation Procedures
3.4.1 Computation Time Reduction Techniques in the TSMCS
3.4.2 Risk Evaluation Procedures
3.5 Case Studies
3.5.1 Case 1: Illustration of Gas Flow Dynamics in a Single Pipeline
3.5.2 Case 2: Impact of Gas Flow Dynamics on the Failure Propagation in a Representative Scenario
3.5.3 Case 3: Short-Term Risk Indices
3.6 Conclusion
References
4 Risk Evaluation of Integrated Electricity and Gas Systems Considering Cascading Effects
4.1 Introduction
4.2 Description of Cascading Effects in Integrated Electricity and Gas Systems
4.3 Modeling Dynamic Cascading Effects in Integrated Electricity and Gas Systems
4.3.1 Initial Failures
4.3.2 Coupled Components Modeling
4.3.3 Re-dispatch Model of Gas Systems
4.3.4 Re-dispatch Model of Power System
4.3.5 Stopping Criterion of Cascading Effects
4.4 Risk Analysis of IEGS Considering Cascading Effects Using MCS Techniques
4.4.1 Risk Indices
4.4.2 Simulation Procedures
4.5 System Studies
4.5.1 Case 1: Impacts of Cascading Effects on Nodal Risk
4.5.2 Case 2: Test on the Larger Scale Systems
4.5.3 Computation Time of Risk Evaluation
4.6 Conclusion
Appendix 1
Appendix 2
Appendix 3
References
5 Definitions and Risk Modeling of Two-Interdependent-Performance Multi-state System and Its Application for CHP Units
5.1 Introduction
5.2 Definition of Two-Interdependent-Performance Multi-state Components
5.3 The Two-Interdependent-Performance Universal Generating Function Technique
5.3.1 Risk Modeling of the System with a Parallel Structure
5.3.2 Risk Modeling of the System with Series Structure
5.4 Risk Evaluation of the TIP-MSS Through the TIP-UGF Technique
5.4.1 Availability Criterion Based on the TIP-UGF Representation
5.4.2 Illustrative Examples
5.5 Conclusions
References
6 Operational Risk Assessment of Integrated Electricity and Heating Systems with CHP Units
6.1 Introduction
6.2 Risk Modeling of the Coupling Devices
6.2.1 Risk Model for the HP Units
6.2.2 Risk Model for the CHP Units
6.2.3 State Combination Based on the UGF Technique
6.3 Risk Evaluation Based on the CHPD Model
6.3.1 Modeling of the Uncertain Energy Loads
6.3.2 Formulation of the CHPD Model
6.3.3 Algorithm to Solving the CHPD Model
6.3.4 Risk Indices Calculation
6.4 Case Studies
6.4.1 Test System and Parameters
6.4.2 Risk Analysis of the Test System
6.4.3 Impacts of the Optimism Parameter on the Results
6.4.4 The Computational Efficiency of the Proposed Technique
6.5 Conclusions
References
7 Operational Risk of Multi-energy Customers Considering Service-Based Self-scheduling
7.1 Introduction
7.2 General Description of Multi-energy Customers and Energy-Related Services
7.2.1 Introduction to Multi-energy Customers and Energy-Related Services
7.2.2 Chronological Multi-state Model for Multi-energy Supply and Demand
7.3 Optimal Self-scheduling of Multi-energy Flexible Service
7.3.1 Chronological Characteristics of Service Curtailment and Service Shifting
7.3.2 Uncertainties of Service Deployment Among Alternative Energies
7.3.3 Formulation of Optimal Self-scheduling of Multi-energy Flexible Service
7.4 Operational Risk Evaluation Procedures Using Time-Sequential Monte Carlo Simulation
7.5 Case Studies and Discussions
7.5.1 Case 1: Chronological Characteristics of Multi-energy Flexible Services and the Operational Risk of Multi-energy Customers Considering Self-scheduling
7.5.2 Case 2: Impacts of Multiple Uncertainties on the Operational Risk of Multi-energy Customers
7.5.3 Case 3: Validation of the Proposed Technique Using a Practical Case
7.6 Conclusions
Appendix: Mathematical Descriptions of Multi-energy Customers
References
8 Multi-phase Risk Modeling and Evaluation of Multi-energy Systems Under Windstorms
8.1 Introduction
8.2 Risk of Multi-energy Systems Under Windstorms
8.2.1 Illustration of Multi-phase Risk in Multi-energy Systems
8.2.2 The Outline to Evaluate the Multi-phase Risk of Multi-energy Systems
8.3 Risk Modeling of MESs Under Windstorms
8.3.1 Phase I: Pre-disturbance Phase
8.3.2 Phase II: Disturbance Progress Phase
8.3.3 Phase III: Post-event Degraded Phase
8.3.4 Phase IV: Restoration Phase
8.4 Framework for Risk Evaluation of MESs Utilizing Monte Carlo Simulation
8.4.1 Nodal Risk Metrics
8.4.2 Simulation Procedures
8.5 Case Studies and Discussions
8.5.1 Descriptions of Test Systems and Simulation Data
8.5.2 Case Studies
8.6 Conclusions
References
9 Long-Term Reserve Expansion of Integrated Electricity and Gas Systems for Risk Mitigation
9.1 Introduction
9.2 The Relationship Between Long-Term Reserve Planning and Failure Propagation
9.2.1 Impacts of Cross-sectorial Failure Propagation on Long-Term Reserve Planning of IEGSs
9.2.2 The Outline of This Chapter to Determine Long-Term Reserve
9.3 Fuzzy Models to Characterize Load and Generation Uncertainties
9.3.1 Fuzzy Component Operation State Curve to Characterize Generation Uncertainties
9.3.2 Fuzzy Load Duration Curve Model to Characterize Load Uncertainties
9.4 Reserve Expansion Model Considering Multifactor-Influenced Reliability Constraints
9.4.1 Objective Function
9.4.2 Multifactor-Influenced Reliability Constraints Considering Failure Propagation
9.4.3 State and Construction Constraints
9.4.4 System Operation Constraints
9.5 Solution Methodology
9.5.1 The Treatment of Fuzzy Parameters
9.5.2 The Solution of the Reliability-Constrained Reserve Expansion Model
9.5.3 Solution Procedures of the Proposed Model
9.6 Case Study
9.6.1 Effectiveness Analysis of the Proposed Model Compared to Conventional Existing Models
9.6.2 Sensitivity Analysis of Reliability Requirements on Planning Results
9.6.3 The Impacts of Optimism Values on Planning Results
9.6.4 Coordination Analysis Between P2G Facilities and Gas Suppliers
9.7 Conclusion
References
10 Outlook of Incorporating Integrated Demand Response in Risk Control of Multi-energy Systems
10.1 Introduction
10.2 Flexibility Illustration of Integrated Demand Response Provided by Industrial Loads
10.2.1 The Structure of Distributed Multi-energy Systems with Industrial Loads
10.2.2 The Basic Concept of Integrated Flexibility
10.3 Operation Modeling of Industrial Loads Considering Energy Conversion and Production Tasks
10.3.1 Modeling of the DESS
10.3.2 Modeling of the IL
10.3.3 Modeling of the DMSI by Combing the DESS and the IL
10.4 The Method to Determine the Flexible Region of Industrial Loads
10.4.1 Modeling of the Integrated Flexible Region
10.4.2 The Calculation Method
10.4.3 Case Studies and Discussions
10.5 Outlook of Incorporating Integrated Demand Response in Risk Control of Multi-energy Systems
References