Optimizing Energy Storage Systems Using Deep Reinforcement Learning in Smart Grids


Hajin Noh and Yujin Lim, Journal of Information Processing Systems Vol. 21, No. 2, pp. 204-215, Apr. 2025  

https://doi.org/10.3745/JIPS.04.0345
Keywords: Energy Storage System, Reinforcement Learning, Smart Grid
Fulltext:

Abstract

With the progress of IT technology, it has become possible to reduce consumer costs in the power market by using energy storage systems (ESS) in smart grids. Traditional algorithms proposed to solve optimization of ESS problems are difficult to apply to dynamic situations, hence adaptable and relatively simple designs such as deep reinforcement learning (DRL) techniques have begun to be used instead. In this study, a Markov decision process is designed to determine the charging and discharging amounts within a certain range to extend the lifespan of the ESS. Furthermore, DRL techniques such as deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor-critic (SAC) were trained, and their performances were compared for analysis.


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Cite this article
[APA Style]
Lim, H. (2025). Optimizing Energy Storage Systems Using Deep Reinforcement Learning in Smart Grids. Journal of Information Processing Systems, 21(2), 204-215. DOI: 10.3745/JIPS.04.0345.

[IEEE Style]
H. N. a. Y. Lim, "Optimizing Energy Storage Systems Using Deep Reinforcement Learning in Smart Grids," Journal of Information Processing Systems, vol. 21, no. 2, pp. 204-215, 2025. DOI: 10.3745/JIPS.04.0345.

[ACM Style]
Hajin Noh and Yujin Lim. 2025. Optimizing Energy Storage Systems Using Deep Reinforcement Learning in Smart Grids. Journal of Information Processing Systems, 21, 2, (2025), 204-215. DOI: 10.3745/JIPS.04.0345.