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Deep Reinforcement Learning Based Autonomous Control Approach for Power System Topology Optimization

With the rapid development of modern power systems, the penetration rates of power electronic equipment and renewable energy are increasing, which bring challenges to stable operation. Due to the strong randomness and uncertainty, the conventional method based on mathematical model can not control modern power systems. So in this paper, a deep reinforcement learning (DRL) based method is proposed to realize the stable autonomous control of power systems. Specific, soft actor-critic (SAC) is used to reroute power flow in transmission lines via autonomous topology optimization control. Besides, to solve huge action space in topology switching and the vulnerability of DRL agents in power systems, a pre-trained scheme based on imitation learning (IL) is presented to use in SAC. Simulations on the IEEE 118-bus system for topology optimization are carried out under random perturbations and common adversarial perturbations to validate the effectiveness and robustness of the proposed methods. The results show that our methods have outstanding performance.