A Study on Renewable Energy Scenario Clustering Technology for Power System Cascading Failure Assessment
With the integration of large-scale renewable energy sources,the computation time for risk assessment and tracking of cascading failures in power systems increases significantly.To address the issue that traditional methods may lead to evaluation errors by directly clustering scenarios based on renewable energy data,this paper proposes a new multi-scenario risk-oriented clustering algorithm that specifically considers the characteristics of renewable energy sources.In this algorithm,the cascading failure risk of different scenarios is first calculated using the enumeration method,and then,based on the cascading failure risk of each scenario,the fuzzy C-means clustering method is used to cluster the scenarios,maximizing the similarity within the same scenario and retaining high-risk scenarios that contribute significantly to cascading failure risk.Based on these clustered scenarios,the system's cascading failure risk assessment is conducted.Through a case study of the IEEE RTS-24 system,the accuracy and efficiency of the proposed method are verified.
cascading assessmentfuzzy C-means clusteringrenewable energy scenario
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