2019/04/28 by Hanchen Xu, Xiao Li, Xu, Hanchen +5
Engineering · #Electric Power System Optimization #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microgrid Control and Optimization #Optimization and Control (math.OC) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.1904.12232
openalex publication_date 2019/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.