Publication details

A Perspective on Foundation Models for the Electric Power Grid

Authors

HAMANN Hendrik F. BRUNSCHWILER Thomas GJORGIEV Blazhe MARTINS Leonardo S. A. PUECH Alban VARBELLA Anna WEISS Jonas BERNABE-MORENO Juan MASSÉ Alexandre Blondin CHOI Seong 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 BELY Aliaksandr BESSA Ricardo J. BHATTARI Bishnu Prasad SCHMUDE Johannes SOBOLEVSKY Stanislav

Year of publication 2024
Type Article in Periodical
Magazine / Source Joule
MU Faculty or unit

Faculty of Science

Citation
Keywords Foundation Models; Data-Driven Power Grid Modeling; Energy Transition; AI-based Power Flow Simulation
Description 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, FMs can find uses in electric power grids, challenged by the energy transition and climate change. In this paper, we call for the development of, and state why we believe in, the potential of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.

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