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In recent years, power system failure events caused by high-risk and low-probability extreme events have occurred frequently in the world, and resilience is used to describe the system's ability to adapt and recover from extreme events. In order to accurately evaluate the resilience of the distribution network, resilience metrics and evaluation method considering the system operation duration and risk are proposed and verified. Firstly, the resilience metrics of the distribution network considering the operation duration and risk are proposed. Then, the probability distribution of different distributed renewable energy sources and loads is fitted by kernel density estimation (KDE) method. To reflect the uncertainty of distributed renewable energy and load, the scenes are generated by latin hypercube sampling (LHS) and cholesky method, and simultaneous backward reduction (SBR) method is used to reduce the generated scenes. Secondly, a two-layer optimization model of system operation duration and operational risk, which takes into account the uncertainty of renewable energy generation is proposed. According to the internal and external evaluation results, the resilience of the system under a given risk constraint can be evaluated. Finally, based on the improved IEEE33 node system, the proposed metrics and methods are verified, and the system resilience under different risks is analyzed.
Inspec keywords: renewable energy sources; statistical distributions; distributed power generation; power distribution reliability; optimisation
Subjects: Optimisation techniques; Distribution networks; Other topics in statistics; Distributed power generation; Reliability; Energy resources