AI 中文总结
本文提出量子启发式QUBO辅助ALNS框架，在IEEE 123节点测试馈线的100 m/s风速场景中，可降低停电指数、缺供电量并缩短恢复工期，适用于飓风后配电网络恢复。
AI 中文摘要
飓风后配电系统恢复需考虑馈线拓扑、现场后勤及电气可行性，制定快速抢修计划。本文提出量子启发式二次无约束二元优化（QUBO）辅助自适应大邻域搜索（ALNS）框架：在每个恢复阶段，局部CPU模拟退火采样器对 energized 前沿附近的单个抢修任务及多任务组合排序；确定性解码器保障抢修队卡车后勤、实现抢修队满负荷利用，并排除不可行批次；最终计划通过OpenDSS复现验证。该框架在无分布式电源的IEEE 123节点测试馈线，于80、90、100 m/s风速场景下评估。100 m/s强风测试中，与传统 energized ALNS 相比，所提方法将平均系统停电持续时间指数、缺供电量分别降低2.24%、恢复工期缩短50.71%，结果表明，严重损坏产生更大组合抢修空间时，QUBO辅助价值最大。
英文摘要
Post hurricane distribution system restoration requires rapid repair scheduling subject to feeder topology, field logistics, and electrical feasibility. This paper presents a quantum inspired quadratic unconstrained binary optimization (QUBO) assisted adaptive large neighborhood search (ALNS) framework. At each restoration stage, a local CPU simulated annealing sampler ranks individual repairs and multi job combinations near the energized frontier. A deterministic decoder preserves crew truck logistics, enforces full useful crew utilization, and rejects infeasible batches. Final schedules are validated through OpenDSS replay. The framework is evaluated on the IEEE 123 node test feeder without distributed generation under 80, 90, and 100 m/s wind scenarios. In the 100 m/s stress test, the proposed method reduces mean system average interruption duration index and energy not supplied by 2.24% and restoration makespan by 50.71% relative to classical energized ALNS. Results show that QUBO assistance is most valuable when severe damage creates a larger combinatorial repair space.
Comments6 pages, 6 figures, 4 tables. Submitted to the 24th National Power System Conference (NPSC 2026), Track T8: Grid Flexibility and Resiliency
