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).
@article{arxiv.1507.05268,
title = {Reinforcement Learning for the Unit Commitment Problem},
author = {Gal Dalal and Shie Mannor},
journal= {arXiv preprint arXiv:1507.05268},
year = {2016}
}
Comments
Accepted and presented in IEEE PES PowerTech, Eindhoven 2015, paper ID 462731