发表机构
Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出电网模式感知模型预测控制框架，通过协调混合储能系统平滑AI数据中心电网侧功率，经仿真验证其可有效减轻电网振荡，兼具灵活性与计算效率。
AI 中文摘要
为实现高波动AI数据中心负荷的电网友好型接入，本文提出一种电网模式感知模型预测控制（G-MPC）框架，用于管理混合储能系统（HESS）以平滑电网侧功率需求。该框架通过滚动时域方式求解多步优化问题，实现对电池储能系统（BESS）与超级电容器（SC）的最优协调。特别地，带通滤波器动力学被直接嵌入G-MPC公式中，以提取并抑制与电网脆弱振荡模式相关的电网侧功率分量，从而减轻负荷引发的电网振荡。所得G-MPC优化同时最小化电网侧功率包络、爬坡率及模态功率要求的违反情况，以及BESS和SC的衰减与功率爬坡成本，同时满足功率限值、荷电状态限值及其他运行约束。为实现实时部署，开发了一种“固定并重优化”算法，以高效求解每个G-MPC问题，同时避免同时充放电。大量仿真验证了所提框架的有效性、灵活性和计算效率，结果还强调了明确抑制与电网脆弱模式相关的功率分量（而非仅减少整体负荷波动）对于有效减轻电网振荡的重要性。
英文摘要
To facilitate the grid-friendly integration of highly variable AI data center loads, this paper proposes a grid-mode-aware model predictive control (G-MPC) framework for managing a hybrid energy storage system (HESS) to smooth grid-side power demand. The framework optimally coordinates a battery energy storage system (BESS) and a supercapacitor (SC) by solving a multi-step optimization problem in a receding-horizon manner. In particular, band-pass filter dynamics are directly embedded in the G-MPC formulation to extract and suppress grid-side power components associated with vulnerable grid oscillatory modes, thus mitigating load-induced grid oscillations. The resulting G-MPC optimization jointly minimizes violations of grid-side power-envelope, ramp-rate, and modal-power requirements and the degradation and power-ramping costs of the BESS and SC, while satisfying power limits, state-of-charge limits, and other operational constraints. To enable real-time implementation, a fix-and-re-optimize algorithm is developed to solve each G-MPC problem efficiently while preventing simultaneous charging and discharging. Extensive simulations demonstrate the effectiveness, flexibility, and computational efficiency of the proposed framework. The results also highlight the importance of explicitly suppressing power components associated with vulnerable grid modes, rather than merely reducing overall load variations, to effectively mitigate grid oscillations.
