Mitigating Blackout Risk via Maintenance : Inference from Simulation Data

TL;DR

This paper introduces a data-driven methodology to efficiently identify critical components for blackout risk mitigation in power systems, using simulation data and heuristic algorithms to optimize maintenance strategies.

Contribution

It develops an inference-based approach linking maintenance decisions to blackout risk, formulates a nonlinear optimization model, and proposes heuristics for high-efficiency solutions.

Findings

Method effectively identifies influential components for risk reduction.

High efficiency of heuristic algorithms demonstrated in numerical experiments.

Adaptive simulation requirement improves estimation credibility.

Abstract

Whereas maintenance has been recognized as an important and effective means for risk management in power systems, it turns out to be intractable if cascading blackout risk is considered due to the extremely high computational complexity. In this paper, based on the inference from the blackout simulation data, we propose a methodology to efficiently identify the most influential component(s) for mitigating cascading blackout risk in a large power system. To this end, we first establish an analytic relationship between maintenance strategies and blackout risk estimation by inferring from the data of cascading outage simulations. Then we formulate the component maintenance decision-making problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility of blackout risk estimation, leading to an adaptive method to determine the least required number of simulations, which servers as a crucial parameter of the optimization model. Finally, we devise two heuristic algorithms to find approximate optimal solutions to the model with very high efficiency. Numerical experiments well manifest the efficacy and high efficiency of our methodology.