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Foundation Models for the Electric Power Grid

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

TL;DR

It is argued that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how the authors manage complexity and uncertainty in the electric grid.

Abstract

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.

Foundation Models for the Electric Power Grid

TL;DR

It is argued that an FM learning from diverse grid data and topologies could unlock transformative capabilities, pioneering a new approach in leveraging AI to redefine how the authors manage complexity and uncertainty in the electric grid.

Abstract

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.
Paper Structure (41 sections, 6 figures)

This paper contains 41 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Looming challenges
  3. Distributed and renewable energy sources
  4. Everything inverter
  5. Changes to demand and weather patterns
  6. Short-term models
  7. Long-term models
  8. Aging infrastructure
  9. Cybersecurity threats
  10. Foundation models
  11. The foundation model approach
  12. Salient advantages
  13. Predicting "tokens"
  14. Homogenization and scaling
  15. Accelerating simulations
  16. ...and 26 more sections

Figures (6)

  • Figure 1: The energy transition, aging infrastructure, cybersecurity challenges, and climate change greatly increase complexity and uncertainty in operating, controlling, and planning the power grid. This is creating a widening gap between existing computational capabilities and the evolving needs of the electric power industry.
  • Figure 2: Evolution in AI and ML methods, leading to the emergence of FMs
  • Figure 3: FM life-cycle phases i) pre-training, ii) fine-tuning, iii) inference.
  • Figure 4: Applications: Examples of power system problems to be solved with GridFMs.
  • Figure 5: GridFM implementation road map with four near-term phases to develop GridFM– v0, a GridFM for power flow related applications. The icons refer to academia, power system industry, and AI community. The long-term goal is creating a family of GridFMs for a broader range of grid challenges.
  • ...and 1 more figures