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• W4295598045 abstract "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." @default.
• W4295598045 created "2022-09-14" @default.
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• W4295598045 date "2023-01-01" @default.
• W4295598045 modified "2023-10-14" @default.
• W4295598045 title "Post-storm repair crew dispatch for distribution grid restoration using stochastic Monte Carlo tree search and deep neural networks" @default.
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• W4295598045 doi "https://doi.org/10.1016/j.ijepes.2022.108477" @default.
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