WASHINGTON, D.C.—The U.S. Department of Energy’s (DOE’s) Office of Electricity (OE) today announced the three Phase 2 winning teams of the American-Made Digitizing Utilities Prize. This $1.1 million prize connected utilities with interdisciplinary teams of software developers and data scientists to transform the energy sector through innovation in obtaining actionable insight from grid use.

“Ongoing advancements in information and communications technologies have enabled utilities to collect more data than ever before,” said Dr. Roshi Nateghi, DOE Program Manager. “The solutions brought forth by these winning teams will provide new, out-of-the-box ways of managing the influx of large datasets and deriving actionable information for the electric industry to continue to improve the reliability and resilience of the nation’s grid.”

This two-phase prize, launched on October 10, 2022, empowered competitors to challenge traditional methods of data acquisition, processing, storage, and analysis while collaborating in real time with industry partners. In Phase 1, teams proposed software solutions for one of three predetermined tracks: load modeling, data analysis automation, or a competitor-identified challenge with an existing utility partner.

From the solutions proposed, judges selected three teams from each track—nine in total—to receive a $75,000 cash prize and advance to Phase 2. The second phase of this prize allowed teams to work directly with utility partners to implement their proposals and demonstrate how other utilities could use their software solution in the future.

One team from each track will receive $100,000 in cash prizes. From these finalists, one grand prize winner will receive an additional $125,000 prize for the winning solution. Watch the video announcement by clicking here.

Track 1 – Load Modeling

Competitors worked with Bonneville Power Administration to use load modeling to help correctly forecast future power demands, specifically addressing the dynamic nature of weather-sensitive loads for residential and commercial buildings. The Track 1 winner is:

• Cadmus: The Data Wave of the Future (Arlington, VA) – Cadmus used machine-learning modeling techniques to provide insights into the distribution network and characterize weather-sensitive energy loads.

Track 2 – Data Analysis Automation

Competitors worked with Dominion Energy to propose methods for the automation of data analysis to help engineers scrutinize and evaluate a data-driven approach to resolving nuanced complexities across the system. The Track 2 winner is:

• ScaLaDE: Scalable Labeling for Data Enrichment (Princeton, NJ) – ScaLaDE used machine learning to enrich large datasets with event labels by incorporating domain knowledge from power system experts.

Track 3 – Competitor-Identified Challenge

Competitors with an existing utility partner identified their own digitization or data challenge and proposed solutions. The Track 3 winner is:

• Electrify USA: Expedite Integration of EV in Distribution System (Riverside, CA) – Electrify USA developed an integrated and data-driven planning platform that predicts feeder-level electric vehicle adoption, forecasts charging profiles, and analyzes impacts on the distribution network.

Grand Prize Winner

Each of these winning proposals demonstrated innovative solutions to challenges in the energy industry, but one team stood out among the rest. For the strength of their proposed solution and progress made during Phase 2, DOE selected Electrify USA as the Digitizing Utilities Grand Prize Winner.

Find Additional Prizes and Challenges

This prize, and others like it, supports DOE’s commitment to accelerating research, development, and demonstration of new technologies and tools within the electricity sector to advance reliability, resilience, and affordable operation of the power system. Stay tuned for more information by following the Digitizing Utilities Prize on HeroX.

Visit AmericanMadeChallenges.org for more information on additional funding opportunities and upcoming prizes.

Learn more about OE’s work here.