Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power system operations from the integration of renewables and climate-induced extreme weather also calls for tools that can accurately and efficiently simulate a wide range of scenarios and operating conditions. Machine learning methods offer a potential speedup over traditional solvers, but their performance has not been systematically assessed on benchmarks that capture real-world variability. This paper introduces PF$Δ$, a benchmark dataset for power flow that captures diverse variations in load, generation, and topology. PF$Δ$ contains 859,800 solved power flow instances spanning six different bus system sizes, capturing three types of contingency scenarios (N , N -1, and N -2), and including close-to-infeasible cases near steady-state voltage stability limits. We evaluate traditional solvers and GNN-based methods, highlighting key areas where existing approaches struggle, and identifying open problems for future research. Our dataset is available at https://huggingface.co/datasets/pfdelta/pfdelta/tree/main and our code with data generation scripts and model implementations is at https://github.com/MOSSLab-MIT/pfdelta.

翻译：潮流计算是实时电网运行的核心，广泛应用于诸如事故分析（通过重复的潮流评估来评估停电情况下的电网安全性）和拓扑优化（涉及在组合巨大的动作空间上进行基于潮流的搜索）等工作流程。在运行时间尺度或大规模评估空间上执行这些计算仍然是一个主要的计算瓶颈。此外，可再生能源并网和气候引发的极端天气带来的电力系统运行不确定性日益增长，也要求能够准确高效模拟多种场景和运行条件的工具。机器学习方法相比传统求解器具有潜在的加速优势，但其性能尚未在捕捉现实世界变化的基准测试中得到系统评估。本文介绍了PF$Δ$，一个捕捉负荷、发电和拓扑多样变化的潮流基准数据集。PF$Δ$包含859,800个已求解的潮流实例，涵盖六种不同的母线系统规模，捕捉三种事故场景类型（N、N-1和N-2），并包含接近稳态电压稳定极限的近乎不可行案例。我们评估了传统求解器和基于GNN的方法，突出了现有方法面临挑战的关键领域，并指出了未来研究的开放性问题。我们的数据集可在https://huggingface.co/datasets/pfdelta/pfdelta/tree/main获取，包含数据生成脚本和模型实现的代码位于https://github.com/MOSSLab-MIT/pfdelta。