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

Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya L. Donti

Keywords: Machine learning for power flow, Graph neural networks, Benchmarking

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∆, a benchmark dataset for power flow that captures diverse variations in load, generation, and topology. PF∆ 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

Dataset URL: https://huggingface.co/datasets/pfdelta/pfdelta/tree/main

Code URL: https://github.com/MOSSLab-MIT/pfdelta

Primary Area: Dataset and Benchmark for Optimization (e.g., convex and non-convex, stochastic, robust, metrics for optimization, scaling of datasets, benchmarks)

Submission Number: 2004