Risk-oriented renewable energy scenario clustering for power system reliability assessment and tracing

Abstract

The integration of large-scale renewable energy significantly increases the computation time of reliability assessment and tracing. To solve this problem, the traditional methods cluster the scenarios directly based on renewable energy data. However, it could lead to errors in reliability assessment due to scenarios with similar risks. In this paper, a multi-scenario risk-oriented clustering algorithm considering renewable energy is proposed. The enumeration method is used to calculate the risk for different scenarios. According to the risk of each scenario, the Fuzzy C-means clustering method is adopted to cluster the scenarios, which maximizes the similarity of scenarios in the same cluster. The high-risk scenarios that contribute more to the reliability index are retained. The system reliability assessment and tracing are conducted based on the clustered scenarios. Case studies on the IEEE RBTS-6 and RTS-79 systems verify the accuracy and efficiency of the proposed method.