Deep Q-network based battery energy storage system control strategy with charging/discharging times considered

Jun Cai, Maowen Fu, Ying Yan, Zhong Chen and Xin Zhang

Applied Energy, 2025, vol. 398, issue C, No S0306261925011146

Abstract: The Battery Energy Storage System (BESS) plays a pivotal role in maintaining the balance of electricity supply and demand on the user side. This paper proposes an energy management system (EMS) for the BESS based on the Deep Q-Network (DQN) algorithm that takes into account the battery charging and discharging times. Initially, a mathematical model of the EMS is established. Subsequently, the optimal decision-making process of EMS is formulated as Markov Decision Process (MDP), and based on this, the MDP formula and DQN algorithm are designed to design charging/discharging schedules based on load conditions. Finally, an experimental study was conducted based on the actual load data of a certain line in Zunyi, Guizhou, China. The test results show that the optimization method proposed in this study reduces the maximum variance of power grid fluctuations to 49 % of the original variance, while reducing the number of battery charging and discharging cycles to the range of 1/3 to 1/2 of the initial value. This delays the battery aging process, improving the economic and practical efficiency of energy management strategies.

Keywords: Battery energy storage system (BESS); Energy management system (EMS); Deep Q-network (DQN); Battery charging and discharging times (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126384

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