Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.03436

Grid-Mode-Aware AI Data Center Power Smoothing via Bayesian-Optimized Hybrid Energy Storage System

Sina Mohammadi·Quang Manh Hoang·Marcus Chen I Wada·Van-Hai Bui·Wencong Su
View original source ↗Read source document ↗Export metadata ↓

Abstract

AI data centers exhibit highly stochastic, volatile power demands from intensive workloads and synchronized GPU training cycles, which can excite grid natural frequencies and threaten system stability. This paper proposes a real-time, forecasting-independent control framework utilizing a Hybrid Energy Storage System (HESS) that coordinates a Battery Energy Storage System (BESS) and a supercapacitor (SC). Employing a parameterized frequency-decoupling and active modal-damping policy, the framework mitigates critical oscillatory load components while smoothing overall power fluctuations. To bypass the heavy computational burden of online receding-horizon optimization, this work introduces an offline Bayesian Optimization (BO) approach to optimally tune real-time control parameters, minimizing a multi-objective cost function that balances grid compliance penalties and storage degradation. Numerical simulations demonstrate that the proposed method ensures grid stability and strong target envelope adherence. Specifically, it reshapes extreme 75-160 MW raw demand fluctuations to largely fit within a tight 10 MW target band, with only brief transient excursions, while attenuating the critical 20 MW oscillatory component below 1 MW to suppress resonant excitation.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sina Mohammadi, Quang Manh Hoang, Marcus Chen I Wada, Van-Hai Bui, Wencong Su. 2026-11-02. Grid-Mode-Aware AI Data Center Power Smoothing via Bayesian-Optimized Hybrid Energy Storage System. https://arxiv.org/abs/2611.03436

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Reinforcement Learning based Traffic Signal Control for Queue Minimization: From Lane-based to Laneless Traffic

Efficient traffic signal control (TSC) is essential for reducing congestion and delays. Traditional TSC methods struggle to capture the dynamics of laneless, heterogeneous traffic. We propose a reinforcement learning (RL) based adaptive TSC framework whose reward depends only on approach-level queue length, motivated by the difficulty of obtaining per-vehicle measurements in laneless traffic, and evaluate it using SUMO on a conventional lane-based, homogeneous (LBHO) intersection and on a non-orthogonal laneless, heterogeneous (LLHE) intersection. On the LBHO site, the lowest mean configuration has about 23\% lower mean queue length than a Dynamic Webster baseline, and the unselected Full-State configuration about 15\% lower. On the LLHE site, all RL configurations have 66-73\% lower mean queue length than a Dynamic Webster baseline under nominal demand, and 63--74\% lower under two higher-demand scenarios evaluated without retraining. Our algorithm also outperforms actuated control and a RESCO-DQN baseline on mean queue length. We additionally release a laneless, heterogeneous-vehicle SUMO framework integrated with RL based signal control, to our knowledge the first such open implementation on a current SUMO version.

eess.SY↗
arXiv2026

A Deep State-Space Model Compression Method using Upper Bound on Output Error

We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error guarantee. We first derive an upper bound on the output error between two Deep SSMs and show that the bound can be expressed in terms of the $h^2$-error norms between the layerwise LQO systems. In particular, we show that reducing the $h^2$ approximation errors of the LQO systems placed in shallow layers is effective in reducing the derived upper bound on the output error. Next, we formulate an optimization problem for the derived upper bound and develop a gradient-based MOR method. In the numerical experiments, using the IMDb task from the LRA benchmark, we demonstrate the effectiveness of the proposed upper-bound-based compression method. In particular, we show that the number of trainable parameters can be reduced by approximately 60\% without retraining while maintaining the performance of the original model.

eess.SY↗
arXiv2026

Safe Navigation in Cluttered Environments Via Spline-Based Harmonic Potential Fields

We provide a motion-planning framework for target reaching and obstacle avoidance in cluttered environments. Given a polyhedral decomposition of the feasible space, the agent is guided through a prescribed sequence of cells by a suitable control policy. For each cell, we construct a potential induced by a harmonic function on the unit disk whose Dirichlet boundary condition is represented by a cardinal B-spline curve. A detailed analysis of the curve periodicity, support, and control-point selection provides an explicit analytical construction of these potentials and the associated vector fields. We further characterize inter-cell control continuity and forward invariance of non-exit facets, and introduce a smoothed control law ensuring continuous switching between independently constructed neighboring cells. Numerical results illustrate that the resulting framework safely guides the agent along the prescribed cell sequence from the initial point to the target.

eess.SY↗