OPFLearnData: Dataset for Learning AC Optimal Power Flow
- University of Washington; National Renewable Energy Laboratory
- Power Systems Engineering
- University of Colorado - Boulder
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.
- Research Organization:
- National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1827404
- Report Number(s):
- AC36-08GO28308
- Availability:
- datacatalog@nrel.gov
- Country of Publication:
- United States
- Language:
- English
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