面向AI训练负载平滑的BESS通信延迟鲁棒控制
Communication Delay Robust Control of BESS for AI Training Load Smoothing
- Pacific Northwest National Laboratory（太平洋西北国家实验室）
- Washington State University（华盛顿州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种结合下垂构网控制与负载电流补偿的混合BESS控制策略，并引入基于预测器的延迟补偿，以平滑AI训练负载波动，仿真验证了其在不同电网强度和时变延迟下的有效性。
AI中文摘要：
AI训练负载可能表现出快速的功率波动，因为其在计算阶段和通信阶段的功率需求差异显著，从而在数据中心公共耦合点（PCC）产生具有挑战性的爬坡速率。在数据中心集成电池储能系统（BESS）是一种有前景的缓解方案。本文提出了一种混合BESS控制策略，该策略结合了基于下垂的构网型（GFM）控制与基于瞬时负载电流的补偿，以抑制高频负载波动。GFM控制回路调节BESS的长期功率交换，而负载跟随控制则为短期AI工作负载波动提供快速补偿。显式建模了负载电流测量与BESS控制器之间的通信延迟，并分析了其对平滑性能的影响。为减轻延迟引起的性能退化，在BESS控制结构中引入了一种基于预测器的补偿方法。进行了高保真电磁暂态仿真以验证所提方法。结果表明，在不同电网强度条件和时变通信延迟下，该方法能有效平滑AI训练负载波动。
英文摘要：
AI training loads can exhibit rapid power fluctuations because their power demand differs significantly between computational and communication phases, creating challenging ramp rates at the data center point of common coupling (PCC). Integrating battery energy storage systems (BESS) in data centers is a promising mitigation option. This paper proposes a hybrid BESS control strategy that combines droop-based grid-forming (GFM) control with instantaneous load current-based compensation to suppress high frequency load fluctuations. The GFM control loop regulates the long-term power exchange of the BESS, while the load-following control provides fast compensation for short-term AI workload fluctuations. Communication delay between the load current measurements and the BESS controller is explicitly modeled, and its impact on smoothing performance is analyzed. To mitigate delay-induced degradation, a predictor-based compensation method is incorporated into the BESS control structure. High-fidelity electromagnetic transient simulations are conducted to validate the proposed approach. Results demonstrate effective smoothing of AI training load fluctuations across different grid strength conditions and under time varying communication delays.
