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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sirui Tang & Ting Li & Youbo Liu & Yunche Su & Yunling Wang & Fang Liu & Shuyu Gao, 2022. "Optimal Transmission Switching for Short-Circuit Current Limitation Based on Deep Reinforcement Learning," Energies, MDPI, vol. 15(23), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9200-:d:993466
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