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

Author

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

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.

Suggested Citation

  • Cai, Jun & Fu, Maowen & Yan, Ying & Chen, Zhong & Zhang, Xin, 2025. "Deep Q-network based battery energy storage system control strategy with charging/discharging times considered," Applied Energy, Elsevier, vol. 398(C).
    • Handle: RePEc:eee:appene:v:398:y:2025:i:c:s0306261925011146
    DOI: 10.1016/j.apenergy.2025.126384
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    References listed on IDEAS

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    1. Zhibin Liu & Feng Guo & Jiaqi Liu & Xinyan Lin & Ao Li & Zhaoyan Zhang & Zhiheng Liu, 2023. "A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System," Energies, MDPI, vol. 16(1), pages 1-19, January.
    2. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
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