Reinforcement Learning for the Unit Commitment Problem

Gal Dalal; Shie Mannor

doi:10.1109/ptc.2015.7232646

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arXiv · 1507.05268

Reinforcement Learning for the Unit Commitment Problem

Gal Dalal·Shie Mannor

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Abstract

In this work we solve the day-ahead unit commitment (UC) problem, by formulating it as a Markov decision process (MDP) and finding a low-cost policy for generation scheduling. We present two reinforcement learning algorithms, and devise a third one. We compare our results to previous work that uses simulated annealing (SA), and show a 27% improvement in operation costs, with running time of 2.5 minutes (compared to 2.5 hours of existing state-of-the-art).

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Gal Dalal, Shie Mannor. 2015-07-19. Reinforcement Learning for the Unit Commitment Problem. https://doi.org/10.1109/ptc.2015.7232646

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