Los Alamos and Georgia Tech scientists to research AI for next-gen power grid

Los Alamos National Laboratory and the Georgia Institute of Technology are partnering on research and education on AI tools for the next-generation power grid.

The partnership between the US DOE’s national laboratory and Georgia Tech’s Artificial Intelligence Institute for Advances in Optimisation (AI4OPT) is focused on driving research in applied artificial intelligence and engaging students and other professionals in the future of the burgeoning field.

The agreement, which extends over three years, specifically spells out a research and educational partnership that will focus on AI tools for advancing a next-generation power grid.

Building and maintaining the future energy grid requires extensive computation, which AI-informed approaches, including modelling and optimisation, should help to address.

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“This collaboration will help develop new AI technologies for the next generation of scientific discovery and the design of complex systems and the control of engineered systems,” said Russell Bent, scientist at Los Alamos.

“At Los Alamos, we have a lot of interest in doing work related to the optimisation of complex systems and we see an opportunity with AI to make systems more resilient and efficient in the face of factors like climate change, extreme events and third party actors.”

The partnership envisages using the expertise and resources at Georgia Tech to develop ‘trustworthy foundation models’ incorporating AI, which can reduce the computational needs to solve such complex problems.

Pascal Van Hentenryck, AI4OPT director, says its use-inspired research seeks to address fundamental societal and technological challenges.

“The energy grid is of special importance as a complex system that is central to our everyday life. Working with Los Alamos, we have an opportunity to advance a research mission and educational vision that makes an impact for science and our society.”

The research agreement will develop ‘optimisation proxies’ that build on current optimisation methods by using broader parameters, including generator limits, line ratings, generator commitments and grid topologies.

While AI approaches have helped train optimisation proxies for energy applications, doing so using broader parameters has so far been a significant research challenge.

The collaboration will also concentrate on solving problems posed by the wide range of missions and applications at the Los Alamos Laboratory.