ARPA-E Grid Optimization (GO) Competition Challenge 1
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 -
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
Research Areas
Keywords
energy, power, ACOPF, Unit Commitment, GO Competition, security constrained, optimal powerflow, grid, grid optimization, ARPA-E, competition, computational science, energy model, optimization, synthetic grid data, model, dataDOE Project Details
Project Name ARPA-E Grid Optimization Competition
Project Number CJ0000903