PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations

• URL: http://arxiv.org/abs/2510.22048v1
• Date: Fri, 24 Oct 2025 22:09:09 GMT
• Title: PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
• Authors: Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya Donti,
• Abstract summary: Power flow calculations are the backbone of real-time grid operations.<n> PF$Delta$ is a benchmark dataset for power flow that captures diverse variations in load, generation, and topology.
• Score: 0.055997926295092294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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$\Delta$, a benchmark dataset for power flow that captures diverse variations in load, generation, and topology. PF$\Delta$ 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.

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