Factored Markov Decision Process Models for Stochastic Unit Commitment

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In this paper, we consider stochastic unit commitment problems where power demand and the output of some generators are random variables. We represent stochastic unit commitment problems in the form of factored Markov decision process models, and propose an approximate algorithm to solve such models. By incorporating a risk component in the cost function, the algorithm can achieve a balance between the operational costs and blackout risks. The proposed algorithm outperformed existing non-stochastic approaches on several problem instances, resulting in both lower risks and operational costs.