A Risk Characteristic-Oriented Renewable Energy Scenario Reduction Method For Power System Risk Assessment

Abstract

Aiming at the operational risk assessment of the power system with the integration of large-scale renewable energy sources, a novel risk characteristics-oriented clustering method to reduce renewable energy scenarios is proposed for power system risk assessment. In order to improve the validity of the information contained in typical scenarios and the accuracy of risk assessment, and speed up the calculation efficiency of risk assessment, a scenario reduction method based on risk characteristics orientation is proposed by combining K-means clustering method with the risk assessment method: First, we use analytical methods to obtain representative risk data for each new energy net load scenario; Then, according to the characteristics of the impact of representative data on risk assessment, K-means clustering method is adopted for clustering. Finally high-risk scenarios with higher contributions to the risk index are screened out, and the system risk is evaluated by Monte Carlo Simulation(MCS). Case studies are performed on the IEEE RBTS-6 node system and verify the accuracy and efficiency of the proposed method.