Mitigation of Datacenter Demand Ramping and Fluctuation using Hybrid ESS and Supercapacitor
Min-Seung Ko, Jae Woong Shim, Hao Zhu
TL;DR
Datacenters introduce large ramping and fluctuating loads that threaten grid stability. The paper presents a co-located hybrid energy storage system (HESS) combining a battery energy storage system (BESS) and a supercapacitor (SC) with HPF-based demand decomposition, Kalman-filter ramp estimation, and multi-timescale control (including leaky-integral, RC, FF, PD, and RK) plus an SoC manager. Simulations on a synthetic 1 GW grid demonstrate suppression of ramps and oscillations, improved frequency stability, and sustainable SoC trajectories under prolonged AI training workloads. This approach offers a practical path to enhance grid reliability for hyperscale AI datacenters.
Abstract
This paper proposes a hybrid energy storage system (HESS)-based control framework that enables comprehensive power smoothing for hyperscale AI datacenters with large load variations. Datacenters impose severe ramping and fluctuation-induced stresses on the grid frequency and voltage stability. To mitigate such disturbances, the proposed HESS integrates a battery energy storage system (BESS) and a supercapacitor (SC) through coordinated multi-timescale control. A high-pass filter (HPF) separates the datacenter demand into slow and fast components, allocating them respectively to the ESS via a leaky-integral controller and to the SC via a phase-lead proportional-derivative controller enhanced with feedforward and ramp-tracking compensation. Adaptive weighting and repetitive control mechanisms further improve transient and periodic responses. Case studies verify that the proposed method effectively suppresses both ramping and fluctuations, stabilizes the system frequency, and maintains sustainable state-of-charge (SoC) trajectories for both ESS and SC under prolonged, stochastic training cycles.