2 datasets found
  1. ARPA-E Grid Optimization (GO) Competition Challenge 1

    • data.openei.org
    • osti.gov
    • +1more
    archive, data +2
    Updated Aug 5, 2024
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    Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert; Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert (2024). ARPA-E Grid Optimization (GO) Competition Challenge 1 [Dataset]. http://doi.org/10.25984/2437761
    Explore at:
    archive, image_document, data, websiteAvailable download formats
    Dataset updated
    Aug 5, 2024
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    Pacific Northwest National Laboratory
    Authors
    Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert; Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public.

    Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition.

    Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found.

    For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

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    ARPA-E Grid Optimization (GO) Competition Challenge 1 | gimi9.com

    • gimi9.com
    Updated Aug 23, 2024
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    (2024). ARPA-E Grid Optimization (GO) Competition Challenge 1 | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_arpa-e-grid-optimization-go-competition-challenge-1/
    Explore at:
    Dataset updated
    Aug 23, 2024
    Description

    The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public. Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition. Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found. For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

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Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert; Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert (2024). ARPA-E Grid Optimization (GO) Competition Challenge 1 [Dataset]. http://doi.org/10.25984/2437761
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ARPA-E Grid Optimization (GO) Competition Challenge 1

Explore at:
9 scholarly articles cite this dataset (View in Google Scholar)
archive, image_document, data, websiteAvailable download formats
Dataset updated
Aug 5, 2024
Dataset provided by
United States Department of Energyhttp://energy.gov/
Open Energy Data Initiative (OEDI)
Pacific Northwest National Laboratory
Authors
Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert; Stephen Elbert; Jesse Holzer; Arun Veeramany; Kory Hedman; Hans Mittelmann; Carleton Coffrin; Thomas Overbye; Adam Birchfield; Christopher DeMarco; Ray Duthu; Olga Kuchar; Hanyue Li; Ahmad Tbaileh; Jessica Wert
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public.

Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition.

Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found.

For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.

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