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DOE OSTI · 1827404

OPFLearnData: Dataset for Learning AC Optimal Power Flow

Joswig-Jones, Trager·Zamzam, Ahmed·Baker, Kyri
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Abstract

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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24 POWER TRANSMISSION AND DISTRIBUTION25 ENERGY STORAGE29 ENERGY PLANNING, POLICY, AND ECONOMY32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATIONload profilemachine learningnonlinear optimizationoptimal power flowpower system

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Joswig-Jones, Trager, Zamzam, Ahmed, Baker, Kyri. 2021-10-26. OPFLearnData: Dataset for Learning AC Optimal Power Flow. https://doi.org/10.7799/1827404

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