ARPA-E Grid Optimization (GO) Competition Challenge 3
Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public.
The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead.
In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023).
For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.
Challenge 1 and Challenge 2 information can be found in the resources linked below.
Citation Formats
TY - DATA
AB - Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public.
The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead.
In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023).
For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.
Challenge 1 and Challenge 2 information can be found in the resources linked below.
AU - Elbert, Stephen
A2 - Holzer, Jesse
A3 - Veeramany, Arun
A4 - O'Neill, Richard
A5 - Mittelmann, Hans
A6 - Coffrin, Carleton
A7 - Garcia, Manuel
A8 - Parker, Robert
A9 - Elgindy, Tarek
A10 - Hale, Elaine
A11 - Palmintier, Bryan
A12 - Mak, Terrence
A13 - Overbye, Thomas
A14 - Safdarian, Farnaz
A15 - DeMarco, Christopher
A16 - Greene, Scott
A17 - Lesieutre, Bernard
A18 - Eldridge, Brent
A19 - Oh, Hyungseon
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.25984/2426334
KW - energy
KW - power
KW - ACOPF
KW - Unit Commitment
KW - multiperiod
KW - GO Competition
KW - security-constrained optimal power flow
KW - grid
KW - grid optimization
KW - ARPA-E
KW - competition
KW - computational science
KW - model
KW - multiperiod dynamic markets
KW - energy model
KW - optimization
KW - challenge 3
LA - English
DA - 2024/05/02
PY - 2024
PB - Pacific Northwest National Laboratory
T1 - ARPA-E Grid Optimization (GO) Competition Challenge 3
UR - https://doi.org/10.25984/2426334
ER -
Elbert, Stephen, et al. ARPA-E Grid Optimization (GO) Competition Challenge 3. Pacific Northwest National Laboratory, 2 May, 2024, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2426334.
Elbert, S., Holzer, J., Veeramany, A., O'Neill, R., Mittelmann, H., Coffrin, C., Garcia, M., Parker, R., Elgindy, T., Hale, E., Palmintier, B., Mak, T., Overbye, T., Safdarian, F., DeMarco, C., Greene, S., Lesieutre, B., Eldridge, B., & Oh, H. (2024). ARPA-E Grid Optimization (GO) Competition Challenge 3. [Data set]. Open Energy Data Initiative (OEDI). Pacific Northwest National Laboratory. https://doi.org/10.25984/2426334
Elbert, Stephen, Jesse Holzer, Arun Veeramany, Richard O'Neill, Hans Mittelmann, Carleton Coffrin, Manuel Garcia, Robert Parker, Tarek Elgindy, Elaine Hale, Bryan Palmintier, Terrence Mak, Thomas Overbye, Farnaz Safdarian, Christopher DeMarco, Scott Greene, Bernard Lesieutre, Brent Eldridge, and Hyungseon Oh. ARPA-E Grid Optimization (GO) Competition Challenge 3. Pacific Northwest National Laboratory, May, 2, 2024. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2426334
@misc{OEDI_Dataset_5997,
title = {ARPA-E Grid Optimization (GO) Competition Challenge 3},
author = {Elbert, Stephen and Holzer, Jesse and Veeramany, Arun and O'Neill, Richard and Mittelmann, Hans and Coffrin, Carleton and Garcia, Manuel and Parker, Robert and Elgindy, Tarek and Hale, Elaine and Palmintier, Bryan and Mak, Terrence and Overbye, Thomas and Safdarian, Farnaz and DeMarco, Christopher and Greene, Scott and Lesieutre, Bernard and Eldridge, Brent and Oh, Hyungseon},
abstractNote = {Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public.
The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead.
In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023).
For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below.
Challenge 1 and Challenge 2 information can be found in the resources linked below. },
url = {https://data.openei.org/submissions/5997},
year = {2024},
howpublished = {Open Energy Data Initiative (OEDI), Pacific Northwest National Laboratory, https://doi.org/10.25984/2426334},
note = {Accessed: 2025-04-25},
doi = {10.25984/2426334}
}
https://dx.doi.org/10.25984/2426334
Details
Data from May 2, 2024
Last updated Sep 27, 2024
Submitted May 2, 2024
Organization
Pacific Northwest National Laboratory
Contact
Stephen Elbert
619.813.4210
Authors
Research Areas
Keywords
energy, power, ACOPF, Unit Commitment, multiperiod, GO Competition, security-constrained optimal power flow, grid, grid optimization, ARPA-E, competition, computational science, model, multiperiod dynamic markets, energy model, optimization, challenge 3DOE Project Details
Project Name ARPA-E Grid Optimization Competition
Project Number CJ0000903
