Preparing for the ARPA-E Grid Optimization Competition
Phased competition intended to accelerate grid optimization tools for industry use
The first-ever Grid Optimization—or GO—Competition will challenge researchers and industry to develop and test power system optimization and control tools to accelerate new solutions for the nation’s power grid.
Competition development is being led by a research team from Pacific Northwest National Laboratory, Arizona State University and the University of Wisconsin–Madison, with support from the Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) Program1.
Several emerging trends—including integration of renewables such as solar or wind power into the grid and the advent of technologies like energy storage—are increasing the complexity of the grid. Energy experts recognize the need for new and improved optimization and control tools that ease the complexity and help utilities and others operate power systems more cost-effectively and reliably. But historically, the ability for the electricity industry to evaluate and test new tools has been challenging. This is primarily due to variations in modeling assumptions and vast ranges of computational systems used for experiments, which complicate comparisons among tools.
The GO Competition is helping to overcome these obstacles. Results of this three-year competition are expected to accelerate the comprehensive evaluation of new power system optimization and control tools and, in turn, hasten industry use and adoption of the tools.
GO with the Flow
The GO competition will be open to anyone with solutions to grid optimization challenges, with the exception of federal entities.
The competition is currently in Phase 0 / Beta Testing Phase—designed to allow competitors to become familiar with the competition procedure, including code requirements and the submission process. Following completion of Phase 0 / Beta Testing Phase in January 20181, the two ensuing main phases— Phases 1 and 2—will be rolled out to focus on a different variant of an optimal power flow problem. Each phase will use four unique data sets consisting of a collection of power system network models of different sizes and associated operating scenarios. Competitors can download the data sets to test algorithms within their own development and testing environments. In each phase, two “dry run” trial rounds will be held in advance of a final event at the end of each phase. The final event will involve evaluation and scoring of the tools—as shown via an online leaderboard—with competition winners announced for the final prizes.
More information about the competition:
- How to get started (click on Submission tab)
- How to register (click on Registration tab)
- Problem description
- The evaluation process
- Data sets
Competition Taps Team Strengths
Conceptualizing the GO Competition in 2014, ARPA-E asked the national laboratory in 2015 to develop a prototype of a fully automated competition platform, including the optimal power flow problem design, website, evaluation platform, and scoring technique. The University of Wisconsin-Madison was tapped for their expertise in power flow optimization and power system data as well as Arizona State University for its leading expertise in algorithm evaluation and scoring.
Following a major ARPA-E review in March 2017, the team was given the “green light” to begin preparing the competition for launch.
“PNNL is a leader in energy research, supporting government energy policy decisions and providing industry-advanced technologies,” said PNNL engineer Feng Pan, who helped lead the effort. “Most importantly, PNNL is known for its ability to provide the full spectrum of science, engineering, and IT technologies, which are important for developing an international competition like ARPA-E’s GO Competition.”
Looking forward, Pan adds, “Such a competition has been proven in many fields to be an effective way to close the capability gap and transition research to industry in a short amount of time. The hosting and automated evaluation capabilities we developed could be applied to other government programs and industry directly.”