Combining absolute, relative, and forecast price features in the state for Double DQN agents improves arbitrage performance and cross-zone transfer in pumped-storage hydro trading compared to single feature families.
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning
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abstract
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.
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cs.LG 1years
2026 1verdicts
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State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Combining absolute, relative, and forecast price features in the state for Double DQN agents improves arbitrage performance and cross-zone transfer in pumped-storage hydro trading compared to single feature families.