This report addresses the critical need for accurate monitoring of grid oscillations caused by the periodic load profiles of large AI data centers. It provides an essential analysis for anyone involved in grid performance and compliance.
Abstract:
Artificial intelligence (AI) training data centers with periodic load profiles can induce sustained grid oscillations across a wide frequency range, making accurate monitoring essential for reliable power system operation. This report evaluates the adequacy of existing measurement systems for monitoring such oscillations, focusing on phasor measurement units (PMUs) and point-on-wave (POW) measurement systems. The analysis shows that while PMUs are highly effective for monitoring low-frequency electromechanical oscillations, they have inherent limitations in accurately representing higher-frequency oscillations due to constraints imposed by reporting rates and the bandwidth of phasor estimation filters. Even when configured with higher reporting rates, the filtering inherent in the phasor estimation process can significantly attenuate oscillation magnitudes, potentially leading to underestimation of oscillatory behavior. This has important implications for compliance and performance monitoring of large loads. To address the limitations associated with PMU-based monitoring, the report examines the use of high-resolution POW measurements, which can capture oscillations across a broader frequency range. However, continuous POW monitoring introduces practical challenges related to large data volumes, communication bandwidth, and real-time data processing. For this reason, the report also discusses emerging approaches that could enable the use of POW measurements as a complementary capability alongside PMUs to improve observability of oscillations from large data center loads.
Authors:
Kaustav Chatterjee, PNNL
Jim Follum, PNNL
Antos Varghese, PNNL
Shuchismita Biswas, PNNL
Evangelos Farantatos, EPRI
Lin Zhu, EPRI