Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning
This addresses energy market efficiency for grid operators and storage owners, but appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of controlling energy storage systems for arbitrage in real-time electricity markets under price uncertainty using deep reinforcement learning, and verified its effectiveness with real-time prices from PJM, though no concrete numbers were provided.
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.