可持续数据中心的实时控制：考虑工作负载灵活性与余热回收的两层模型预测控制框架
Real-Time Control of Sustainable Data Centers: A Two-Layer Model Predictive Control Framework with Workload Flexibility and Heat Recovery
AI总结：
针对集成多能源系统的数据中心，提出含随机优化上层与自适应MPC下层的两层控制框架，可降低调度偏差与不平衡成本，适配季节及碳感知信号，实现经济可持续的电网支持运行。
AI中文摘要：
本文提出一种两层模型预测控制（MPC）框架，用于集成了现场光伏发电、电池储能、余热回收及区域供热的数据中心的实时运行。上层采用基于场景的随机优化方法，在不确定性条件下联合优化日内市场参与、工作负载调度与能源管理；下层采用自适应管状MPC策略，在跟踪上层给定调度参考值的同时补偿短期扰动。该框架还集成了多 horizon 预测以支持实时决策。在典型晴天与阴天运行条件下开展的基于微服务的仿真研究表明，尽管光伏与工作负载波动快速，所提框架仍能准确跟踪调度计划。与单层控制策略相比，自适应下层控制器大幅降低了实时调度偏差及相关不平衡成本。此外，所提框架可自然适应季节运行条件并响应碳感知运行信号，为未来数据中心实现经济高效、可持续且支持电网的运行提供了实用方法。
英文摘要：
This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower layer adopts an adaptive tube-based MPC strategy that compensates short-term disturbances while tracking the dispatch references given by the upper layer. The framework further integrates multi-horizon forecasting to support real-time decision making. Microservice-based simulation studies under representative clear-sky and overcast operating conditions demonstrate that the proposed framework accurately tracks dispatch plans despite fast photovoltaic and workload fluctuations. Compared with single-layer control strategies, the adaptive lower-layer controller substantially reduces real-time dispatch deviations and the associated imbalance costs. In addition, the proposed framework naturally adapts to seasonal operating conditions and responds to carbon-aware operating signals, offering a practical approach for economically efficient, sustainable, and grid-supportive operation of future data centers.
