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Flexible Training Workloads in Large-Scale AI Data Centers for Transient-Stability Support in Transmission-Constrained Power Systems
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- OpenAlex
- DOI
- 10.48550/arxiv.2608.30901
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Abstract
The rapid expansion of large-scale artificial intelligence (AI) data centers is adding substantial, concentrated, and rapidly varying loads to transmission-constrained power systems. Although such load variations are generally regarded as operational challenges, this paper presents an alternative perspective in which the upward load flexibility of AI data centers could be coordinated for transient-stability support. To this end, this paper proposes training-induced load surge (TILS), a fast demand-side strategy that initiates or resumes flexible AI training workloads after fault clearing to increase active-power demand at electrically effective locations. The resulting load increase allows accelerating generators to supply additional electrical power, thereby reducing the accelerating-power imbalance and limiting the first-swing rotor-angle excursion. The underlying mechanism is first clarified in a single-machine infinite-bus (SMIB) system and then evaluated in the IEEE 39-bus system and a large-scale Korean power system. Results across all three systems demonstrate that TILS can increase the transient-stability-constrained generation limit. Larger responses, earlier activation, and siting at buses with a stronger electrical influence on the critical generators provide greater generation-limit increases. These results suggest that the upward load-response capability of AI data centers can provide complementary transient-stability support when sufficient electrical headroom, flexible workloads, and reliable grid-triggered activation are available.
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