| Title |
Power System Topology Control via Option-Critic Deep Reinforcement Learning |
| Authors |
왕천(Chen Wang) ; 장호천(Haotian Zhang) ; 이민주(Minju Lee) ; 이명훈(Myoung Hoon Lee) ; 문준(Jun Moon) |
| DOI |
https://doi.org/10.5370/KIEE.2025.74.6.1030 |
| Keywords |
Deep reinforcement learning; option-critic framework; topology control; smart grid. |
| Abstract |
In recent years, the integration of renewable energy sources into power systems has increased their complexity, making automated control and management more challenging. To address this issue, we propose OC-LSTM, a deep reinforcement learning (DRL) algorithm which integrates option-critic DRL with the long short-term memory (LSTM) neural network to efficiently manage power systems. The OC-LSTM algorithm extracts temporal features from the power system using the LSTM network and leverages the option-critic (OC) framework in DRL to learn policies for adjusting the system's topology, ensuring secure and efficient power transmission. Experimental results demonstrate that the OC-LSTM algorithm outperforms standard DRL algorithms during training, and ablation studies further confirm the effectiveness of LSTM in extracting power system features. Additionally, the OC-LSTM algorithm allows stable operation of the IEEE 5-Bus, IEEE 14-Bus and L2RPN WCCI 2020 power systems for 60 consecutive hours without the need for human intervention. |