OPFLearnData: Dataset for Learning AC Optimal Power Flow

Joswig-Jones, Trager; Zamzam, Ahmed; Baker, Kyri

doi:10.7799/1827404

Title: OPFLearnData: Dataset for Learning AC Optimal Power Flow

Dataset
Other Related Research

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.

Authors:

Joswig-Jones, Trager;

; Baker, Kyri

University of Washington; National Renewable Energy Laboratory
Power Systems Engineering
University of Colorado - Boulder

Publication Date:: Tue Oct 26 04:00:00 UTC 2021

Other Number(s):: AC36-08GO28308

DOE Contract Number:: AC36-08GO28308

Research Org.:: National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Org.:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; 25 ENERGY STORAGE; 29 ENERGY PLANNING, POLICY, AND ECONOMY; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; load profile; machine learning; nonlinear optimization; optimal power flow; power system

OSTI Identifier:: 1827404

DOI:: https://doi.org/10.7799/1827404

Citation Formats


                    Joswig-Jones, Trager, Zamzam, Ahmed, and Baker, Kyri. OPFLearnData: Dataset for Learning AC Optimal Power Flow.  United States: N. p., 2021. 
        Web.  doi:10.7799/1827404.

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

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                    Joswig-Jones, Trager, Zamzam, Ahmed, and Baker, Kyri. 2021.  
"OPFLearnData: Dataset for Learning AC Optimal Power Flow".  United States.  doi:https://doi.org/10.7799/1827404.  https://www.osti.gov/servlets/purl/1827404. Pub date:Tue Oct 26 04:00:00 UTC 2021

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@article{osti_1827404,

  title        = {OPFLearnData: Dataset for Learning AC Optimal Power Flow},

  author       = {Joswig-Jones, Trager and Zamzam, Ahmed and Baker, Kyri},

  abstractNote = {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.},

  doi          = {10.7799/1827404},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Oct 26 04:00:00 UTC 2021},

  month        = {Tue Oct 26 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.7799/1827404

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