FMOPF：用于交流最优潮流的具有约束感知交互先验的潜在流匹配
FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow
AI总结：
研究交流最优潮流计算问题，提出FMOPF框架，通过潜在流匹配和解耦压缩与生成，并利用约束感知交互先验网络建模负荷 - 状态耦合，实现有效热启动，降低尾部风险，可扩展到大型系统。
AI中文摘要：
交流最优潮流在非线性功率平衡约束下确定最小成本发电调度，在电力市场运营中每日求解数千次。学习从负荷条件到最优潮流解决方案的直接映射可加速计算，但随着可再生能源渗透率提高，单一最优调度不足。监督神经网络提供快速点预测但无法捕捉条件分布。基于扩散的生成模型原则上可采样多样解，但现有方法在原始状态空间操作时解质量下降且无法扩展到中型以上系统。本文提出FMOPF框架，通过潜在流匹配将压缩与生成解耦，并通过约束感知交互先验网络显式建模负荷 - 状态耦合来解决此问题。在四个IEEE测试系统上的实验表明，FMOPF提供最有效的牛顿 - 拉夫逊热启动，在生成方法中实现最低尾部风险，是首个可扩展到数百母线系统并保持完全可行性的方法。消融研究证实潜在生成管道是物理可行性的必要条件，交互先验起到后期尾部风险控制器的作用。
英文摘要：
AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily in electricity market operations. Learning a direct mapping from load conditions to OPF solutions can accelerate this computation, yet with deepening renewable penetration, a single optimal dispatch is no longer sufficient. Operators require a characterization of the distribution of feasible near-optimal solutions for risk quantification, sensitivity analysis, and multi-objective trade-off assessment. Supervised neural networks provide fast point predictions but cannot capture this conditional distribution. Diffusion-based generative models can sample diverse solutions in principle, yet existing methods operating in the raw state space exhibit degraded solution quality and fail to scale beyond medium-sized systems. We identify the root cause as the conflation of two distinct tasks within a single model. Compressing the high-dimensional OPF solution manifold is one task, and learning the conditional mapping from loads to that manifold is another. This paper presents FMOPF, a framework that resolves this conflation by decoupling compression from generation through latent flow matching and by explicitly modeling load-state coupling through a Constraint-Aware Interaction Prior Network. Experiments on four IEEE test systems demonstrate that FMOPF provides the most effective Newton-Raphson warm starts, achieves the lowest tail risk among generative methods, and is the first such method to scale to systems with several hundred buses while preserving full feasibility. Ablation studies confirm that the latent generation pipeline is a necessary condition for physical feasibility and that the interaction prior functions as a late-stage tail-risk controller.
