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Adaptive Monte Carlo methods for estimating rare events in power grids
  • +2
  • Jianpeng Chan,
  • Roger Paredes,
  • Iason Papaioannou,
  • Leonardo Duenas-Osorio,
  • Daniel Straub
Jianpeng Chan

Corresponding Author:[email protected]

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Roger Paredes
Iason Papaioannou
Leonardo Duenas-Osorio
Daniel Straub


This paper presents a comprehensive study on rare event estimation in power grids, focusing on state-of-the-art adaptive Monte Carlo algorithms. We compare these methods for the optimal power flow problem in various IEEE benchmark models. Based on the results of our study, we analyze the pros and cons of each adaptive method and investigate their beneficial combinations. Overall, the adaptive effort subset simulation (aE-SuS) method and particle integration methods (PIMs) are promising for high-dimensional reliability analysis. By building on IEEE benchmarks, we provide challenging examples for comparing different emerging methods in static network reliability assessment while revealing improvements for these methods. In particular, 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.
26 Jan 2024Submitted to TechRxiv
29 Jan 2024Published in TechRxiv