Adaptive Monte Carlo Methods for Estimating Rare Events in Power Grids

Abstract

This paper presents a comprehensive study on rare event estimation in power grids, focusing on state-of-the-art adaptive Monte Carlo algorithms. Building upon IEEE benchmarks, we analyze the pros and cons of each adaptive method and investigate their beneficial combinations. In summary, the adaptive effort subset simulation (aE-SuS) method and particle integration methods (PIMs) are promising for high-dimensional reliability analysis. Additionally, we introduce a hybrid approach that combines the strengths of both aE-SuS and annealed PIM. Although this method is not as efficient as aE-SuS, it significantly outperforms crude Monte Carlo and is unbiased. We then employ the aE-SuS method and this hybrid approach for risk assessment of the Texas synthetic power grid, which comprises over 5,000 components, thus showcasing scalability for practical applications.