{"ID":2850874,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22048","arxiv_id":"2510.22048","title":"PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations","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$Δ$, 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.","short_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 a...","url_abs":"https://arxiv.org/abs/2510.22048","url_pdf":"https://arxiv.org/pdf/2510.22048v3","authors":"[\"Ana K. Rivera\",\"Anvita Bhagavathula\",\"Alvaro Carbonero\",\"Priya Donti\"]","published":"2025-10-24T22:09:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false,"code_links":[{"ID":607845,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2850874,"paper_url":"https://arxiv.org/abs/2510.22048","paper_title":"PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations","repo_url":"https://github.com/MOSSLab-MIT/pfdelta","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}