Composite Control of Grid-Following Inverters for Stabilizing AI-Induced Fast Power Disturbances

Abstract

AI data center loads create query-driven power transients on millisecond timescales. Such loads can violate the timescale separation assumptions underlying internal inverter control of grid-following resources collocated with data centers as supplementary generation. This paper develops a singular perturbation-based modeling and control for stabilizing fast power imbalances. We show that physically-implementable droop control is derived and valid by requiring reduced-system stability rather than being imposed a priori, and that AI workloads satisfy a bounded-rate disturbance class due to physical filtering in power delivery hardware. The analysis yields explicit gain bounds linking inverter parameters to disturbance rejection performance, a modulation admissibility condition ensuring physical realizability of the feedback linearizing control, and a feasibility condition identifying the maximum tolerable load ramp rate. Numerical simulations validate the theoretical predictions under stochastic AI transients.