Post-storm repair crew dispatch for distribution grid restoration using stochastic Monte Carlo tree search and deep neural networks

• Published in: International journal of electrical power & energy systems Vol. 144; p. 108477
• Main Authors: Shuai, Hang, Li, Fangxing, She, Buxin, Wang, Xiaofei, Zhao, Jin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: AlphaZero; Crew dispatch; Deep neural network (DNN); Distribution grid restoration; Power system resilience; Crew dispatch; AlphaZero; Power system resilience; Distribution grid restoration; 1111; Deep neural network (DNN)
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.108477

Natural disasters such as storms usually bring significant damages to distribution grids. This paper investigates the optimal routing of utility vehicles to restore outages in the distribution grid as fast as possible after a storm. First, the post-storm repair crew dispatch task with multiple utility vehicles is formulated as a sequential stochastic optimization problem. In the formulated optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by utility vehicles. Second, an AlphaZero based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair crews. The proposed AlphaZero-UVR approach combines stochastic Monte-Carlo tree search (MCTS) with deep neural networks to give a lookahead search decisions, which can learn to navigate repair crews without human guidance. Simulation results show that the proposed approach can efficiently navigate crews to repair all outages. •Formulating a post-storm repair crew dispatch model with multiple vehicles.•Proposing AlphaZero based post-storm utility vehicle routing strategy.•Modifying the original AlphaZero algorithm by combining DNN with stochastic MCTS.•The proposed DRL based method outperforms traditional MCTS methods.