摘要
Increasingly natural disasters and man-made malicious attacks threaten the power systems.Improving the resilience has become an inevitable requirement for the development of power systems.The importance assessment of components is of significance for resilience improvement,since it plays a crucial role in strengthening grid structure,designing restoration strategy,and improving resource allocation efficiency for disaster prevention and mitigation.This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms.Firstly,the component failure rate model under wind storms is established.According to the model,system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method.For each system state,an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching.The distribution functions of component repair moment can be obtained after a sufficient system state sampling.And Copeland ranking method is adopted to rank the component importance.Finally,the feasibility of the proposed approach is validated by extensive case studies.
Increasingly natural disasters and man-made malicious attacks threaten the power systems.Improving the resilience has become an inevitable requirement for the development of power systems. The importance assessment of components is of significance for resilience improvement, since it plays a crucial role in strengthening grid structure, designing restoration strategy,and improving resource allocation efficiency for disaster prevention and mitigation. This paper proposes a component importance assessment approach of power systems for improving resilience under wind storms. Firstly, the component failure rate model under wind storms is established.According to the model, system states under wind storms can be sampled by the non-sequential Monte Carlo simulation method. For each system state, an optimal restoration model is then figured out by solving a component repair sequence optimization model considering crew dispatching. The distribution functions of component repair moment can be obtained after a sufficient system state sampling. And Copeland ranking method is adopted to rank the component importance. Finally, the feasibility of the proposed approach is validated by extensive case studies.
基金
supported by Science and Technology Project of State Grid Corporation of China(No.5202011600UG).
