论文标题
递归修复精炼：一种快速的启发式启发式，用于电力系统中近乎最佳的恢复优先级
Recursive Restoration Refinement: A Fast Heuristic for Near-Optimal Restoration Prioritization in Power Systems
论文作者
论文摘要
大型电力系统中断之后的恢复动作的优先级在恢复速度的速度方面起着关键作用。已经表明，恢复优先次序的快速，直观的启发式方法最常导致低质量的恢复计划。同时，发现高质量恢复计划的数学优化工具太慢，无法应用于实际兴趣的恢复计划问题。这项工作通过提出递归恢复的启发式启发式启发式启发式启发式启发式恢复，在结束这一质量与计算时间差距方面迈出了重要一步。该启发式措施显示出在一系列最高500辆公共汽车和700个损坏的组件的测试用例上，产生的近乎最佳的修复计划比其他最先进的解决方案方法快1000倍。对高质量恢复计划的关键特征的初步分析证明了这种新启发式的潜在影响。作为开源软件包PowerModelSrestoration的一部分，已提供了递归修复算法和此工作中探索的其他方法，以支持正在进行的Power Restoration算法研究。
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.
