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Randomized Adaptive Vehicle Decomposition for Large-Scale Power Restoration

机译:用于大规模功率恢复的随机自适应车辆分解

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This paper considers the joint repair and restoration of the electrical power system after significant disruptions caused by natural disasters. This problem is computationally challenging because, when the goal is to minimize the size of the blackout, it combines a routing and a power restoration component, both of which are difficult on their own. The joint repair/restoration problem has been successfully approached with a 3-stage decomposition, whose last step is a multiple-vehicle, pickup-and-delivery routing problem with precedence and capacity constraints whose goal is to minimize the sum of the delivery times (PDRPPCCDT). Experimental results have shown that the PDRPPCCDT is a bottleneck and this paper proposes a Randomized Adaptive Vehicle Decomposition (RAVD) to scale to very large power outages. The RAVD approach is shown to produce significant computational benefits and provide high-quality results for infrastructures with more than 24000 components and 1200 damaged items, giving rise to PDRPPCCDT with more than 2500 visits.
机译:本文考虑了自然灾害造成的重大破坏之后的电力系统的联合维修和恢复。这个问题在计算上具有挑战性,因为当目标是最大程度地减少停电时,它将路由和电源恢复组件结合在一起,而这两者本身就很难实现。联合维修/修复问题已通过三阶段分解成功解决,其最后一步是具有优先级和容量限制的多车,取送路线问题,其目的是最大程度地减少交货时间( PDRPPCCDT)。实验结果表明,PDRPPCCDT是一个瓶颈,本文提出了一种随机自适应车辆分解(RAVD)来适应非常大的停电。 RAVD方法显示出显着的计算优势,并为包含24000多个组件和1200个损坏项目的基础架构提供了高质量的结果,导致PDRPPCCDT的访问量超过2500次。

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