Hybrid Reinforcement Learning for Power Transmission Network Self-Healing Considering Wind Power
Runjia Sun, Yutian Liu
Abstract
Transmission network self-healing considering uncertain wind power becomes crucial with increasing penetration of wind power. A hybrid reinforcement learning (HRL) method combining offline self-learning with online Monte Carlo tree search (MCTS) is designed to deal with the strong uncertainty induced by wind power restoration. The HRL method trains a policy network with offline self-learning based on historical wind and transmission system data. It then applies the policy network to guide MCTS to realize step-by-step transmission network self-healing based on real-time and forecast data in different wind power scenarios. Besides, a model predictive control method for active power dispatch is proposed to improve wind power generation credibility during self-healing. Simulation results of both test and real-life power systems demonstrate that the proposed method can realize online transmission system self-healing reliably. Comparisons among different reinforcement learning methods indicate that the number of scenarios dominated by HRL is more than twice that dominated by MCTS and a dozen times that dominated by deep Q-network. Meanwhile, the online method is more flexible in uncertain wind power scenarios than optimization methods.