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

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.