On the Stability of Power Systems with AI Data Centers: A Multi-Equilibrium Switched DAE Framework
Abstract
The rapid growth of Artificial Intelligence (AI) data centers is introducing dynamic loads into power transmission systems as GPU clusters switch between computational phases with distinct power demands. These dynamics are naturally modeled by switched affine differential-algebraic equations (DAEs), where each mode induces a different equilibrium and consistency space. We study their stability using control and hybrid systems tools. Differential projectors derived from the quasi-Weierstrass form yield an equivalent hybrid representation, from which we characterize the ω-limit set and establish Lyapunov conditions for practical stability with an explicit 𝒦ℒ bound accounting for switching-induced resets. For stochastic inference workloads, we use Foster--Lyapunov techniques to establish probabilistic uniform global recurrence under mode-dependent dwell times and transition probabilities. The framework is validated on the IEEE 39-bus transmission network with co-located AI data centers using high-fidelity electromagnetic transient simulations of deterministic training and stochastic inference workloads.
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BibTeX
@misc{shenoy2026stability,
title = {{On the Stability of Power Systems with AI Data Centers: A Multi-Equilibrium Switched DAE Framework}},
author = {Vishal Shenoy and Damola Ajeyemi and Mahmoud Abdelgalil and Guido Cavraro and Emiliano Dall'Anese and Jorge I. Poveda},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.16768},
eprint = {2610.16768},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.16768},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - On the Stability of Power Systems with AI Data Centers: A Multi-Equilibrium Switched DAE Framework
AU - Vishal Shenoy
AU - Damola Ajeyemi
AU - Mahmoud Abdelgalil
AU - Guido Cavraro
AU - Emiliano Dall'Anese
AU - Jorge I. Poveda
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.16768
UR - https://arxiv.org/abs/2610.16768
AB - The rapid growth of Artificial Intelligence (AI) data centers is introducing dynamic loads into power transmission systems as GPU clusters switch between computational phases with distinct power demands. These dynamics are naturally modeled by switched affine differential-algebraic equations (DAEs), where each mode induces a different equilibrium and consistency space. We study their stability using control and hybrid systems tools. Differential projectors derived from the quasi-Weierstrass form yield an equivalent hybrid representation, from which we characterize the ω-limit set and establish Lyapunov conditions for practical stability with an explicit 𝒦ℒ bound accounting for switching-induced resets. For stochastic inference workloads, we use Foster--Lyapunov techniques to establish probabilistic uniform global recurrence under mode-dependent dwell times and transition probabilities. The framework is validated on the IEEE 39-bus transmission network with co-located AI data centers using high-fidelity electromagnetic transient simulations of deterministic training and stochastic inference workloads.
ER -