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AlphaZero Based Post-Storm Vehicle Routing for Distribution Grid Restoration

Natural disasters such as storms usually bring significant damages to distribution grids. However, plenty of distribution grids are not installed sensors that can pinpoint the location of damaged equipments (lines, transforms, etc.), which greatly increased the difficulty of the outage restoration. This paper investigates the optimal routing of repair vehicles to restore outages in distribution grid as fast as possible after a storm. First, the vehicle routing problem 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 the repair vehicles. Second, an AlphaZero based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair vehicle. The proposed AlphaZero-UVR approach combines deep neural networks with a Monte-Carlo tree search (MCTS) to give a lookahead search decisions, which can learn to navigate the repair vehicle without human guidance. Simulation results show that the proposed approach can effectively navigate the vehicle to repair all outages.