递归恢复精化：电力系统近优恢复优先级的快速启发式算法
Recursive Restoration Refinement: A Fast Heuristic for Near-Optimal Restoration Prioritization in Power Systems
摘要：
大规模电力系统停电后恢复措施的优先顺序对电力恢复的速度起着关键作用。它已被证明，快速和直观的恢复优先级的算法最经常导致低质量的恢复计划。同时，数学优化工具，找到高质量的恢复计划是太慢，适用于恢复规划问题的实际利益。这项工作是一个重要的一步，在关闭这个质量与计算时间差距，提出了递归恢复细化启发式电力系统恢复。这种启发式产生接近最佳的恢复计划高达1,000倍的速度比其他国家的最先进的解决方案的方法在一系列的测试用例，高达500总线和700损坏的组件。这种新的启发式的潜在影响表现出高质量的恢复计划的主要特征的初步分析。在这项工作中探索的递归恢复细化算法和其他方法已作为开源软件包PowerModelsRestoration的一部分提供，以支持正在进行的电力恢复算法研究。
英文摘要：
The prioritization of restoration actions after large power system outages plays a key role in how quickly power can be restored. It has been shown that fast and intuitive heuristics for restoration prioritization most often result in low-quality restoration plans. Meanwhile, mathematical optimization tools that find high-quality restoration plans are too slow to be applied to restoration planning problems of practical interest. This work makes a significant step in closing this quality vs compute time gap by proposing the Recursive Restoration Refinement heuristic for power system restoration. This heuristic is shown to produce near-optimal restoration plans up to 1,000 times faster than other state-of-the-art solution methods on a range of test cases with up to 500 buses and 700 damaged components. The potential impact of this new heuristic is demonstrated by a preliminary analysis of the key features of high-quality restoration plans. The recursive restoration refinement algorithm and other methods explored in this work have been made available as part of the open-source software package, PowerModelsRestoration, to support ongoing research in power restoration algorithms.
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