Foundation Models for the Electric Power Grid

Hamann, Hendrik F.; Gjorgiev, Blazhe; Brunschwiler, Thomas; Martins, Leonardo S. A.; Puech, Alban; Varbella, Anna; Weiss, Jonas; Bernabe-Moreno, Juan; Masse, Alexandre Blondin; Choi, Seong Lok; Foster, Ian; Hodge, Bri-Mathias; Jain, Rishabh; Kim, Kibaek; Mai, Vincent; Miralles, Francois; De Montigny, Martin; Ramos-Leanos, Octavio; Supreme, Hussein; Xie, Le; Youssef, El-Nasser S.; Zinflou, Arnaud; Belyi, Alexander; Bessa, Ricardo J.; Bhattarai, Bishnu Prasad; Schmude, Johannes; Sobolevsky, Stanislav

doi:10.1016/j.joule.2024.11.002

Foundation Models for the Electric Power Grid

Journal Article · Tue Dec 03 00:00:00 EST 2024 · Joule

DOI:https://doi.org/10.1016/j.joule.2024.11.002· OSTI ID:2496246

Hamann, Hendrik F.; Gjorgiev, Blazhe; Brunschwiler, Thomas; Martins, Leonardo S. A.; Puech, Alban; Varbella, Anna; Weiss, Jonas; Bernabe-Moreno, Juan; Masse, Alexandre Blondin; Choi, Seong Lok; Foster, Ian; ; Jain, Rishabh; Kim, Kibaek; Mai, Vincent; Miralles, Francois; De Montigny, Martin; Ramos-Leanos, Octavio; Supreme, Hussein; Xie, Le more »

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2496246

Report Number(s):: NREL/JA-5D00-91846; MainId:93624; UUID:3de58a81-de1c-4ef8-81ff-a2fb50e5142c; MainAdminId:75632

Journal Information:: Joule, Vol. 8, Issue 12

Country of Publication:: United States

Language:: English

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