Towards High-Efficiency Cascading Outage Simulation and Analysis in Power Systems: A Sequential Importance Sampling Approach

TL;DR

This paper introduces a sequential importance sampling method for power system cascading outage analysis, significantly improving computational efficiency and estimation accuracy over traditional Monte Carlo approaches.

Contribution

It formulates cascading outages as a Markov chain and develops a SIS-based simulation strategy with theoretical validation for efficient blackout risk estimation.

Findings

SIS reduces the number of simulations needed.

SIS lowers estimation variance.

Method outperforms traditional Monte Carlo simulations.

Abstract

This paper addresses how to improve the computational efficiency and estimation reliability in cascading outage analysis. We first formulate a cascading outage as a Markov chain with specific state space and transition probability by leveraging the Markov property of cascading outages. It provides a rigorous formulation that allows analytic investigation on cascading outages in the framework of standard mathematical statistics. Then we derive a sequential importance sampling (SIS) based simulation strategy for cascading outage simulation and blackout risk analysis with theoretical justification. Numerical experiments manifest that the proposed SIS strategy can significantly bring down the number of simulations and reduce the estimation variance of cascading outage analysis compared with the traditional Monte Carlo simulation strategy.