PF$Δ$: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
This provides a standardized benchmark for researchers and practitioners in power systems to assess machine learning methods for faster and more accurate simulations under real-world variability, though it is incremental as it focuses on dataset creation rather than a new method.
The paper tackles the computational bottleneck in power flow calculations for grid operations by introducing PFΔ, a benchmark dataset with 859,800 solved instances across various system sizes and contingency scenarios, and evaluates traditional solvers and GNN-based methods to identify performance gaps.
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.