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.

翻译：基础模型（FMs）当前占据着新闻头条。它们采用先进的深度学习架构，通过自监督方式从海量数据集中自主提取结构信息。由此生成的复杂系统与动态过程的丰富表征可应用于众多下游任务。因此，在面临能源转型与气候变化挑战的电力系统中，基础模型具有广阔的应用前景。本文呼吁开发电力系统基础模型，并阐述我们对其潜力的信念。我们着重分析了在电网变革挑战下基础模型的优势与局限。我们认为，通过从多样化的电网数据与拓扑结构中学习的基础模型，有望释放变革性能力，开创利用人工智能重新定义电网复杂性与不确定性管理的新范式。最后，我们探讨了一种基于图神经网络的电网基础模型概念——GridFM，并展示其在不同下游任务中的应用价值。