Operational Risks in Grid Integration of Large Data Center Loads: Characteristics, Stability Assessments, and Sensitivity Studies

Abstract

This paper investigates the dynamic interactions between large-scale data centers and the power grid, focusing on reliability challenges arising from sudden fluctuations in demand. With the rapid growth of AI-driven workloads, such fluctuations, along with fast ramp patterns, are expected to exacerbate stressed grid conditions and system instabilities. We consider a few open-source AI data center consumption profiles from the MIT supercloud datasets, along with generating a few experimental HPC job-distribution-based inference profiles. Subsequently, we develop analytical methodologies for real-time assessment of grid stability, focusing on both transient and small-signal stability assessments. Energy-flow-like metrics for nonlinear transient stability, formulated by computing localized data center bus kinetic-like flows and coupling interactions with neighboring buses over varying time windows, help provide operators with real-time assessments of the regional grid stress in the data center hubs. On the other hand, small-signal stability metrics, constructed from analytical state matrices under variable operating conditions during a fast ramping period, enable snapshot-based assessments of data center load fluctuations and provide enhanced observability into evolving grid conditions. By quantifying the stability impacts of large data center clusters, studies conducted in the modified IEEE benchmark $68-$bus model support improved operator situational awareness to capture risks in reliable integration of large data center loads.