最优输电切换：利用启发式方法提升求解器性能
Optimal Transmission Switching: Improving Solver Performance Using Heuristics
AI总结：
本文针对NP难的最优输电切换问题，提出一种异步并行算法架构，将精确规划与并行启发式候选解注入相结合以加速分支定界求解，并在大规模pglib-opf算例上验证了性能提升和良好可扩展性。
摘要：
最优输电切换问题（OTSP）是一个已确立的问题，即通过控制输电线路的切换状态来改变电网拓扑，从而获得更优的运行状态。该问题已被证明是NP难问题。基于混合整数规划的现有求解技术能够保证全局最优解，但在现实规模的电网中可能难以求解。启发式方法无法保证全局最优性，但能提供可求解的近似方案。本文提出将精确规划与并行启发式方法相结合来求解OTSP，其中启发式方法生成优质候选解，以加速传统的分支定界算法。本工作的创新之处在于一种新的异步并行算法架构：一个求解器实例求解完整的OTSP规划，同时并行运行另一个进程，在运行过程中异步生成解并注入到完整的OTSP求解流程中。我们的方法在pglib-opf库的14个算例上进行了测试，其中最大问题包含13659个母线和20467条支路。结果表明，该方法在大规模算例上表现良好，相比现成求解器性能有持续提升。我们发现该方法随着并行处理器数量的增加具有良好的可扩展性。
英文摘要：
The optimal transmission switching problem (OTSP) is an established problem of changing a power grid's topology to obtain an improved operation by controlling the switching status of transmission lines. This problem was proven to be NP-hard. Proposed solution techniques based on mixed-integer formulations can guarantee globally optimal solutions but are potentially intractable in realistic power grids. Heuristics methods cannot guarantee global optimality but can provide tractable solution approaches. This paper proposes solving the OTSP using exact formulations alongside parallel heuristics that generate good candidate solutions to speed up conventional branch-and-bound algorithms. The innovative aspect of this work is a new asynchronous parallel algorithmic architecture. A solver instance solving the full OTSP formulation is run in parallel to another process that asynchronously generates solutions to be injected into the full OTSP solution procedure during run time. Our method is tested on 14 instances of the pglib-opf library: The largest problem consisting of 13659 buses and 20467 branches. Our results show a good performance for large problem instances, with consistent improvements over off-the-shelf solver performance. We find that the method scales well with an increase in parallel processors.
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