Cluster-based stratified sampling for fast reliability evaluation of composite power systems based on sequential Monte Carlo simulation

•A model is proposed to speed up the calculation of reliability indices.•The proposed model can obtain system reliability indices with high accuracy.•The proposed model preserves probability distribution of the reliability indices.•The proposed approach is applied to highly reliable electrical power systems. An essential aspect in the operation and expansion of the electricity infrastructure is to ensure a reliable power supply, despite uncertainty (mainly caused by random failures, variability of renewable sources, and load growth). Reliability evaluation of composite power systems is a valuable tool for identifying possible deficiencies in operation. Power systems operation is becoming more variable and stochastic. Consequently, there is an urgent need to update the tools used to analyze their reliability. This article presents a novel method based on cluster-based stratified sampling (CBSS) and sequential Monte Carlo simulations (SMCS) to improve the reliability evaluation of composite power systems. Here, clustering algorithms are applied to reduce the number of observations required by traditional SMCS. The proposed approach is applied to RTS-79, RTS-96, and RTS-GMLC electrical networks to verify its accuracy and speed. The results obtained demonstrate that CBSS increase calculation speed in highly reliable networks, while preserving information on the probability distribution of the reliability indices.