The Dual Frontier between AI and the Power Grid
Talk Abstract
AI/ML technologies are rapidly reshaping the paradigm of operating electric power grids. Meanwhile, the hyperscale and dynamic energy demands of AI datacenters pose significant challenges to grid reliability. In this talk, I will explore the dual frontier between AI and the power system, with a focus on grid dynamic modeling and analysis. First, to effectively apply AI tools to grid dynamics, it is crucial to consider not only computational/memory efficiency but also the unique characteristics of dynamic systems. To address this, we introduce TRASE-NODEs—Trajectory Sensitivity-aware Neural ODEs—which leverage the classical dynamic sensitivity concept to significantly improve data efficiency and control performance in neural dynamic models. Second, we examine the impact of large-scale AI datacenters on wide-area power system oscillations. By developing a stochastic model to represent sustained, periodic power fluctuations, our numerical studies reveal that factors such as datacenter sizing and geographic distribution can influence oscillation levels. This quantitative analysis highlights the need for developing mitigation strategies at both the grid and hardware levels to support the continued growth of AI-driven energy demand.