In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.
@article{arxiv.1904.12232,
title = {Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning},
author = {Hanchen Xu and Xiao Li and Xiangyu Zhang and Junbo Zhang},
journal= {arXiv preprint arXiv:1904.12232},
year = {2019}
}