AI 中文总结
针对多区域机组组合面临的量子计算攻击威胁，提出基于定制Benders分解的方法及多层量子抗性安全聚合协议，实现后量子安全分布式优化，具有轻量级开销且能回收成本，相比有噪声的ADMM有优势。
AI 中文摘要
具有备用共享的多区域机组组合需要跨不同管辖系统运营商进行协调优化，这使敏感成本曲线、拓扑结构和调度决策易受推理攻击。量子计算的加速发展加剧了这种威胁。随着量子硬件成熟，当前经典加密数据流易受追溯解密影响。为实现后量子安全的分布式优化，我们提出一种基于定制Benders分解的方法，具有全局求和结构以共享聚合割集和变量。通过利用此结构，我们进一步开发了一种多层量子抗性安全聚合协议，包括用于信息理论内容隐私的加法掩码、隐藏单个敏感数据流的仿射变量变换，以及提供针对主动对手的抗性的基于揭示边界格的零知识证明。仿真结果表明，所提出的方法实现了0.09%-0.22%的平均次优性，计算开销轻量级，通过区域间备用共享可回收高达51%的系统成本，且不存在可测量的成本-质量权衡，而有噪声的ADMM在收紧隐私预算下单调退化，在组合密集系统上变得结构不可行。
英文摘要
Multi-region unit commitment with reserve sharing requires coordinated optimization across jurisdictionally distinct system operators, exposing sensitive cost curves, topology, and dispatch decisions to inference attacks. The accelerating progress of quantum computing further compounds this threat. As quantum hardware matures, current classically-encrypted data flow becomes vulnerable to retrospective decryption. To enable post-quantum-secure distributed optimization, we propose a customized Benders decomposition-based approach with the global summation structure to share aggregated cuts and variables. By exploiting this structure, we further develop a multi-layer quantum-resilient secure aggregation protocol comprising additive masking for information-theoretic content privacy, affine variable transformation hiding individual sensitive data flows, and reveal-bound lattice-based zero-knowledge proofs providing resilience against active adversaries. Simulation results show that the proposed approach achieves the mean suboptimality of 0.09%-0.22% with lightweight computational overhead, recovers up to 51% of system cost via inter-regional reserve sharing, and imposes no measurable cost-quality trade-off, whereas the noisy ADMM degrades monotonically under tightening privacy budgets and becomes structurally infeasible on combinatorially dense systems.
