AI Foundations for Power Grids: From Models to Deployment at Scale

The power grid presents a compelling yet underexplored domain for machine learning, combining hard physical constraints, real-time operation, and large-scale societal impact. Despite increasing interest in applying learning-based methods to problems such as optimal power flow, contingency analysis, and system control, evaluation practices have not kept pace with methodological advances. Many existing studies rely on small, static benchmarks and in-distribution metrics, which fail to capture the challenges of real-world deployment in evolving and safety-critical environments. This workshop aims to position power systems as a first-class methodological challenge for machine learning, with a central focus on evaluation under realistic operating conditions. We will bring together researchers from machine learning and power systems to discuss datasets, benchmarks, model classes, and validation protocols needed to support robust and trustworthy deployment. The program includes invited talks from academia and industry, contributed papers, panel discussions, and interactive sessions designed to foster collaboration across communities. By emphasizing rigorous evaluation, distributional robustness, and system-level behavior, the workshop seeks to shape the development of machine learning methods for infrastructure systems and to establish shared standards for future research.