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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.18392 |
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| _version_ | 1866917423054061568 |
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| author | Kosanic, Miroslav Ilic, Marija |
| author_facet | Kosanic, Miroslav Ilic, Marija |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18392 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Composite Control of Grid-Following Inverters for Stabilizing AI-Induced Fast Power Disturbances Kosanic, Miroslav Ilic, Marija Systems and Control 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. |
| title | Composite Control of Grid-Following Inverters for Stabilizing AI-Induced Fast Power Disturbances |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.18392 |