论文标题
基于Alphazero的传单后维修人员调度分配网格还原
AlphaZero Based Post-Storm Repair Crew Dispatch for Distribution Grid Restoration
论文作者
论文摘要
诸如风暴之类的自然灾害通常会给分销网格带来重大损害。本文调查了公用车的最佳路由，以尽可能快地恢复分配网格中的停电。首先，使用多个多功能车进行了Stystorm修复人员调度任务，作为顺序随机优化问题。在公式的优化模型中，根据客户的电话和公用事业收集的信息，更新电网的信念状态。其次，开发了基于Alphazero [1]的实用车辆路由（Alphazero-UVR）方法，以实现维修人员的实时调度。提出的Alphazero-UVR方法将深层神经网络与随机的蒙特卡洛树搜索（MCT）结合在一起，以做出lookahead搜索决策，可以在没有人类指导的情况下学习维修人员。仿真结果表明，所提出的方法可以有效地导航机组人员来修复所有中断。
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 poststorm 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[1] 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 deep neural networks with stochastic Monte-Carlo tree search (MCTS) 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.
