Foundation models (FMs), pre-trained on large datasets and adapted to a broad set of applications, are revolutionizing the field of artificial intelligence (AI). Powerful FMs for language and weather have recently emerged, proving that such models can understand complex physical systems. In this presentation we review a moonshot concept, which are FMs for the electric grid (GridFMs). In an...
Under the pressure of the energy transition and the digital transformation, there has been an intensification of global research efforts on AI for power systems in recent years. This led to the creation of a new practice and solutions applicable to a wide set of problems for the generation, transmission and distribution of electricity to customers. This presentation will give an overview of...
The grid of today is one that is complex, unpredictable, and dynamic. These traits will be exponentially exacerbated, and new challenges will arise for the grid of tomorrow. The processes and technologies internal to electric utilities must adapt and evolve to prepare for these challenges and even enable utilities to improve their KPIs compared to today. This presentation will highlight...
As the electric grid evolves, managing its complexity becomes increasingly challenging. Digital twins offer a powerful solution by providing a virtual, real-time model of the grid, enabling the simulation, monitoring, and optimization of the system. This presentation will explore the transformative value of digital twins in today's dynamic grid environment, with a particular focus on...
NextEra Analytics (NEA) serves the R&D function for NextEra Energy which build and operates renewable energy plants across US and Canada. The design of the solutions for our customers requires NEA to simulate processes and optimize decisions across the various interacting systems of the electrical grid, electricity and gas markets, regulatory and government, customer clean energy goals and...
Large blackouts are important because they have a high impact on our society, and, although rare, are not rare enough to be low risk. Indeed, the heavy tails in their statistics make them high risk. The observed data for large blackouts is limited, which poses challenges to analysis and prevents the training of AI models. However, large blackouts combine different processes at multiple time...
Before the advent of synthetic electric grids, public test cases for electric transmission grids were limited to the IEEE test cases and similar datasets. While these have served the community well, they do not match the size, complexity, or structure of today’s bulk electric grids. Industry grid models, however, are not publicly sharable because of critical energy infrastructure information...
The Argonne Low-carbon Electricity Analysis Framework (A-LEAF) is a comprehensive power system simulation platform. A-LEAF integrates advanced tools for long-term generation and transmission expansion planning, production cost simulation, and probabilistic reliability assessment. This presentation will introduce the key capabilities of A-LEAF including the climate-informed decision-making,...
Digital Twin is emerging as a key technology supporting and improving grid planning and operation. While different interpretation for the definition of a digital twin can be found in literature, there is an urgent need to come to common understanding and interoperable implementations. The presentation focuses on the current status and ongoing development in the European context. While it is...
AuroraGPT is a foundation model for science, trained on the Aurora supercomputer at the Argonne Leadership Computing Facility (ALCF). This model is designed to process and generate human-like scientific text, with the goal of supporting scientific discovery. Trained on a large corpus of scientific literature, this model has the potential to assist researchers in exploring, summarizing, and...
Globus is a hybrid cloud platform designed to support advanced machine learning (ML) and artificial intelligence (AI) development, deployment, and research across distributed cyberinfrastructure (CI). With Globus services, users can manage remote computation, data management, indexing, search, and automated workflows without regard for the distributed resources used, from scientific...
With the rapid advancement of digitization, energy systems have experienced significant improvements through innovations. As we move toward a more dynamic and complex network, real-time analysis, AI, and decentralized systems are essential for enhancing efficiency and reliability. Addressing this issue, many studies focus on centralizing data in a cloud system to develop innovative monitoring,...
Recent successes with large language models (LLMs) for natural language processing have prompted exploration into their potential for time series prediction using numerical data. Chronos is a recent framework that pre-trains LLM models for predictions on time series data from various domains. The authors show that the framework can lead to successful zero-shot predictions for unseen time...
There has been a growing interest in solving multi-period AC OPF problems, as the increasingly fluctuating electricity market requires operation planning over multiple periods. These problems, formerly deemed intractable, are now becoming technologically feasible to solve thanks to the advent of high-memory GPU hardware and accelerated NLP tools. This study evaluates the capability of the...
Developing a general-purpose AI model for infrastructure resiliency assessments requires integration across a variety disciplines sectors and data heterogeneity and inference for a range of needs. When we talk about resilience to hydrological and meteorological hazards, hazard forecasting at time scales extending from few minutes to 100’s of years is necessary for addressing resiliency of...
This presentation examines two primary dimensions of the evolving interplay between AI and power systems. First, AI’s increasing electricity demand poses both short-run operational and long-run planning challenges, making it necessary to reform and modernize the existing electric grid. Second, new AI capabilities present an unprecedented opportunity to enhance the efficiency, reliability, and...
The increasing penetration of stochastic and uncertain inverter-based resources (IBRs), such as wind and solar PVs, has a considerable influence on the power system dynamics, operation, and optimization, causing reliability and resiliency concerns. On the other hand, the power industry is transforming itself from a hierarchical, passive, and sparsely sensed engineering system into a flat,...
This interactive session will bring the workshop to a productive close by collecting insights and planning the path forward. Breakout leads will present their proposed roadmaps to address challenges and opportunities in building foundation models for the electric grid. A focused presentation will outline governance strategies to ensure sustained collaboration. The session will conclude with a...
