Mitigation of Datacenter Demand Ramping and Fluctuation using Hybrid ESS and Supercapacitor

TL;DR

This paper introduces a hybrid energy storage control framework combining batteries and supercapacitors to effectively smooth power demand fluctuations in hyperscale AI datacenters, enhancing grid stability and system resilience.

Contribution

It presents a novel multi-timescale control strategy integrating BESS and supercapacitors with adaptive mechanisms for improved power smoothing in datacenters.

Findings

Effective suppression of demand ramping and fluctuations.

Enhanced grid frequency and voltage stability.

Stable state-of-charge trajectories during stochastic loads.

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.