Post-storm repair crew dispatch for distribution grid restoration using stochastic Monte Carlo tree search and deep neural networks
Post-storm repair crew dispatch for distribution grid restoration using stochastic Monte Carlo tree search and deep neural networks
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DOI：
10.1016/j.ijepes.2022.108477
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
H. Shuai;Fangxing Li;Buxin She;Xiaofei Wang;Jin Zhao
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文献类型：
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作者：
H. Shuai;Fangxing Li;Buxin She;Xiaofei Wang;Jin Zhao
风暴等自然灾害通常会对配电网造成重大破坏。本文研究了在暴雨后，为了尽快恢复配电网的停电，公用事业车辆的最优路线。首先，将多辆多用途车辆的暴雨后抢修队调度任务表述为一个顺序随机优化问题。在所建立的优化模型中，电网的信念状态根据客户的来电和公用事业车辆收集的信息进行更新。其次，提出了一种基于AlphaZero的多用途车辆调度方法（AlphaZero- uvr），实现了维修人员的实时调度。提出的AlphaZero-UVR方法将随机蒙特卡罗树搜索（MCTS）与深度神经网络相结合，给出前瞻性搜索决策，可以在没有人类指导的情况下学习导航维修人员。仿真结果表明，该方法能有效地引导机组人员对所有故障进行维修。
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.