PF∆: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
Ana Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya Donti
Large-scale integration of renewable energy and climate-induced extreme weather events increase uncertainty in power system operations, which calls for tools that can accurately and efficiently simulate a wide range of scenarios and operating conditions. Machine learning approaches offer potential speedup over traditional solvers, but their performance has not been systematically assessed on benchmarks that capture real-world variability. This paper introduces PF∆, a benchmark dataset designed to evaluate power flow methods under variations in load, generation, and topology. PF∆ contains 693,000 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 at https://anonymous.4open.science/r/pfdelta-A769.