OPFLearnData: Dataset for Learning AC Optimal Power Flow

Published by National Renewable Energy Laboratory | Department of Energy | Metadata Last Checked: June 28, 2025 | Last Modified: 2025-01-21T22:19:35Z

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.

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Complete Metadata

bureauCode	[ "019:20" ]
dataQuality	true
identifier	https://data.openei.org/submissions/8232
issued	2021-10-26T16:06:12Z
landingPage	https://data.nrel.gov/submissions/177
programCode	[ "019:000" ]
projectNumber	AC36-08GO28308
projectTitle	The Science Undergraduate Laboratory Internships Program (SULI), and the Laboratory Directed Research and Development (LDRD) Program at NREL.