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Randomized Adaptive Vehicle Decomposition for Large-Scale Power Restoration

机译:大型功率恢复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:本文考虑了在自然灾害引起的显着中断后的电力系统的联合修复和恢复。此问题在计算上具有挑战性,因为当目标是最小化停电的大小时,它结合了路由和功率恢复组件,这两者都是困难的。联合修复/恢复问题已成功接近3阶段分解,其最后一步是一种多载体,拾取和交付路由问题,优先级和容量约束,其目标是最小化交货时间的总和( pdrppccdt)。实验结果表明,PDRPPCCDT是一个瓶颈,本文提出了随机的自适应车辆分解(RAVD),以规模到非常大的停电。 RAVD方法显示出显着的计算效益,为具有超过24000个组件和1200个损坏物品的基础设施提供高质量的结果,从而推出超过2500次访问的PDRPPCCDT。

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