Research on multi-time-scale flexible operation method of distribution network considering deep reinforcement learning

Ping Dong, Shiqi Liu, Mingbo Liu, Kunming Sui, Run He, Sai Zhang, Wu Xie,
Research on multi-time-scale flexible operation method of distribution network considering deep reinforcement learning,
International Journal of Electrical Power & Energy Systems,
Volume 172,
2025,
111209,
ISSN 0142-0615,
https://doi.org/10.1016/j.ijepes.2025.111209.
(https://www.sciencedirect.com/science/article/pii/S0142061525007574)
Abstract: To fully exploit continuous and discrete devices in the distribution network, this paper studies a multi-time-scale optimal operation method for the distribution network that considers deep reinforcement learning. Firstly, this method comprehensively considers the response times and action characteristics of different regulating devices in the distribution network and then matches them to different time scales for optimization. Then, during the intra-day stage, in order to address the issue of insufficient ESS capacity release, the global time-window construction method based on the sliding time domain for ESS is adopted. In the real-time stage, typical scenario clustering of loads is completed, and it is combined with DRL for decision-making. Finally, a case study is conducted in the modified IEEE 33-node distribution network, and an extensibility analysis is conducted in the modified IEEE 118-node distribution network. The results show that the proposed method can effectively improve the voltage deviation of the distribution network, reduce the operating cost, and at the same time, it has good real-time performance and extensibility.
Keywords: Continuous devices; Discrete devices; Multi-time-scale; Distribution network; Deep reinforcement learning