论文标题
使用回收者调度模型探索电网信号对数据中心操作的影响
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
论文作者
论文摘要
数据中心（DC）可以通过帮助吸收可再生能源（例如，风和太阳能）来帮助脱碳，因为它们能够在时空和时间上移动功率负载。但是，要利用这种负载转移的灵活性，有必要了解网格信号（碳信号和市场价格/负载分配）如何影响直流操作。这里出现的障碍是缺乏可计算上的DC操作模型，该模型可以捕获DCS和功率网络接口上出现的目标，约束和信息流。为了解决这一差距，我们提出了一个回收的Horizon资源管理模型（一种混合企业编程模型），该模型在考虑到逻辑约束，不同类型的目标以及传入的作业配置文件和可用计算能力的预测的同时，捕获DC调度程序和网格之间的资源管理层。我们使用模型根据Microsoft Azure和Miso的公共数据进行广泛的案例研究。我们的研究表明，DC可以提供明显的时间负荷转移柔韧性，从而导致碳排放量减少和峰值需求充电。模型和案例研究被共享为易于使用的朱莉娅代码。
Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.
