Composite reliability evaluation with renewable sources based on quasi-sequential Monte Carlo and cross entropy methods

This paper presents a new approach to assess reliability indices in composite generation and transmission systems, considering high penetration of renewable energy. The main idea is to combine a Cross-Entropy (CE)-based optimization approach and quasi-sequential Monte Carlo simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can converge faster and with fewer samples, leading to significant speed-ups, especially when dealing with very reliable system network configurations. The results obtained for the IEEE Reliability Test System - 1996 will be presented and discussed.