AI 中文总结
针对含断连母线的输电扩展规划，提出LSPC图算法以多项式时间生成紧致big-M系数和有效不等式，优于传统最长路径方法。
AI 中文摘要
可再生能源渗透率的不断提高和电力需求的持续增长，推动着将新母线和输电线路接入输电网的需求。这些趋势正在重塑输电扩展规划（TEP），并激励开发有效的方法来管理由此产生的复杂性。本文提出了最长最短路径连接（LSPC）算法，这是一种基于图的方法，用于利用有效不等式（VIs）增强TEP的混合整数线性规划析取公式。在断连的TEP网络中，确定big-M系数的传统方法通常依赖于求解计算密集的最长路径问题（LPP）。相比之下，LSPC通过在扩展网络内高效识别断连母线之间的相关潮流路径，规避了这些限制。我们证明了由这些识别路径生成的有效不等式在支配性上优于基于LPP的方法及其他现有方法。
英文摘要
The increasing penetration of renewable energy and rising electricity demand are driving the need to integrate new buses and transmission lines into transmission grids. These trends are reshaping transmission expansion planning (TEP), motivating the development of effective methodologies to manage the resulting complexity. This paper introduces the longest shortest-path connection (LSPC) algorithm, a graph-based method to enhance the mixed-integer linear programming disjunctive formulation of TEP using valid inequalities (VIs). Traditional approaches for determining big-M coefficients in disconnected TEP networks typically rely on solving the computationally intensive longest path problem (LPP). In contrast, LSPC circumvents these limitations by efficiently identifying relevant power-flow paths between disconnected buses within the expansion network. We demonstrate that the VIs generated from these identified paths dominate those derived from LPP-based methods and other existing approaches.
Comments16 pages, 8 figures. Published in Optimization Letters
Journal refOptimization Letters (2026)
DOI：10.1007/s11590-026-02333-6
