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Whereas maintenance has been recognized as an important and effective meansfor risk management in power systems, it turns out to be intractable ifcascading blackout risk is considered due to the extremely high computationalcomplexity. In this paper, based on the inference from the blackout simulationdata, we propose a methodology to efficiently identify the most influentialcomponent(s) for mitigating cascading blackout risk in a large power system. Tothis end, we first establish an analytic relationship between maintenancestrategies and blackout risk estimation by inferring from the data of cascadingoutage simulations. Then we formulate the component maintenance decision-makingproblem as a nonlinear 0-1 programming. Afterwards, we quantify the credibilityof blackout risk estimation, leading to an adaptive method to determine theleast required number of simulations, which servers as a crucial parameter ofthe optimization model. Finally, we devise two heuristic algorithms to findapproximate optimal solutions to the model with very high efficiency. Numericalexperiments well manifest the efficacy and high efficiency of our methodology.

Mitigating Blackout Risk via Maintenance : Inference from Simulation Data