利用混合储能系统和超级电容器缓解数据中心需求阶跃和波动
Mitigation of Datacenter Demand Ramping and Fluctuation using Hybrid ESS and Supercapacitor
AI总结：
本文提出一种混合储能系统与超级电容器结合的控制方法，用于缓解数据中心的负载波动和阶跃问题，提升电网稳定性。
摘要：
本文提出了一种基于混合储能系统（HESS）的控制框架，该框架能够为具有大负载变化的超大规模人工智能数据中心提供全面的功率平滑。数据中心对电网频率和电压稳定性施加了严重的阶跃和波动引起的应力。为缓解此类干扰，所提出的HESS通过协调多时间尺度控制集成了电池储能系统（BESS）和超级电容器（SC）。一个高通滤波器（HPF）将数据中心需求分离为慢速和快速成分，分别通过泄漏积分控制器分配给BESS，通过带有前馈和阶跃跟踪补偿的相位超前比例微分控制器分配给SC。自适应加权和重复控制机制进一步提高了瞬态和周期响应。案例研究表明，所提出的方法有效抑制了阶跃和波动，稳定了系统频率，并在长时间、随机训练周期下维持了BESS和SC的可持续状态电量（SoC）轨迹。
英文摘要：
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.
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