Optimal Transmission Switching for Short-Circuit Current Limitation Based on Deep Reinforcement Learning

Sirui Tang, Ting Li, Youbo Liu, Yunche Su, Yunling Wang, Fang Liu and Shuyu Gao ()
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Sirui Tang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Ting Li: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Youbo Liu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yunche Su: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Yunling Wang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Fang Liu: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Shuyu Gao: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2022, vol. 15, issue 23, 1-11

Abstract: The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies.

Keywords: transmission network planning; short-circuit current limitation; maximum loadability; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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