2024/06/11 by Sean Lovett, Miha Zgubic, Lovett, Sean +15 · 5 citations
Computer Science · Engineering · Materials Science · #Computational Physics and Python Applications #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Magnetic Properties and Applications
paper · pdf · doi:10.48550/arxiv.2406.07234
openalex publication_date 2024/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology.