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ARPA-E Grid Optimization (GO) Competition Challenge 1

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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.

Citation Formats

TY - DATA AB - 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. AU - Elbert, Stephen A2 - Holzer, Jesse A3 - Veeramany, Arun A4 - Hedman, Kory A5 - Mittelmann, Hans A6 - Coffrin, Carleton A7 - Overbye, Thomas A8 - Birchfield, Adam A9 - DeMarco, Christopher A10 - Duthu, Ray A11 - Kuchar, Olga A12 - Li, Hanyue A13 - Tbaileh, Ahmad A14 - Wert, Jessica DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.25984/2437761 KW - energy KW - power KW - ACOPF KW - Unit Commitment KW - GO Competition KW - security constrained KW - optimal powerflow KW - grid KW - grid optimization KW - ARPA-E KW - competition KW - computational science KW - energy model KW - optimization KW - synthetic grid data KW - model KW - data LA - English DA - 2024/08/05 PY - 2024 PB - Pacific Northwest National Laboratory T1 - ARPA-E Grid Optimization (GO) Competition Challenge 1 UR - https://doi.org/10.25984/2437761 ER -
Export Citation to RIS
Elbert, Stephen, et al. ARPA-E Grid Optimization (GO) Competition Challenge 1. Pacific Northwest National Laboratory, 5 August, 2024, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2437761.
Elbert, S., Holzer, J., Veeramany, A., Hedman, K., Mittelmann, H., Coffrin, C., Overbye, T., Birchfield, A., DeMarco, C., Duthu, R., Kuchar, O., Li, H., Tbaileh, A., & Wert, J. (2024). ARPA-E Grid Optimization (GO) Competition Challenge 1. [Data set]. Open Energy Data Initiative (OEDI). Pacific Northwest National Laboratory. https://doi.org/10.25984/2437761
Elbert, Stephen, Jesse Holzer, Arun Veeramany, Kory Hedman, Hans Mittelmann, Carleton Coffrin, Thomas Overbye, Adam Birchfield, Christopher DeMarco, Ray Duthu, Olga Kuchar, Hanyue Li, Ahmad Tbaileh, and Jessica Wert. ARPA-E Grid Optimization (GO) Competition Challenge 1. Pacific Northwest National Laboratory, August, 5, 2024. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2437761
@misc{OEDI_Dataset_6153, title = {ARPA-E Grid Optimization (GO) Competition Challenge 1}, author = {Elbert, Stephen and Holzer, Jesse and Veeramany, Arun and Hedman, Kory and Mittelmann, Hans and Coffrin, Carleton and Overbye, Thomas and Birchfield, Adam and DeMarco, Christopher and Duthu, Ray and Kuchar, Olga and Li, Hanyue and Tbaileh, Ahmad and Wert, Jessica}, abstractNote = {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.
}, url = {https://data.openei.org/submissions/6153}, year = {2024}, howpublished = {Open Energy Data Initiative (OEDI), Pacific Northwest National Laboratory, https://doi.org/10.25984/2437761}, note = {Accessed: 2025-04-23}, doi = {10.25984/2437761} }
https://dx.doi.org/10.25984/2437761

Details

Data from Aug 5, 2024

Last updated Sep 27, 2024

Submitted Aug 5, 2024

Organization

Pacific Northwest National Laboratory

Contact

Stephen Elbert

619.813.4210

Authors

Stephen Elbert

Pacific Northwest National Laboratory

Jesse Holzer

Pacific Northwest National Laboratory

Arun Veeramany

Pacific Northwest National Laboratory

Kory Hedman

Arizona State University

Hans Mittelmann

Arizona State University

Carleton Coffrin

Los Alamos National Laboratory

Thomas Overbye

Texas AM University

Adam Birchfield

Texas AM University

Christopher DeMarco

University of Wisconsin - Madison

Ray Duthu

U.S. Department of Energy Advanced Research Projects Agency-En...

Olga Kuchar

Oak Ridge National Laboratory

Hanyue Li

Texas AM University

Ahmad Tbaileh

Pacific Northwest National Laboratory

Jessica Wert

Texas AM University

DOE Project Details

Project Name ARPA-E Grid Optimization Competition

Project Number CJ0000903

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