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Data centers (DCs) can help decarbonize the power grid by helping absorbrenewable power (e.g., wind and solar) due to their ability to shift powerloads across space and time. However, to harness such load-shiftingflexibility, it is necessary to understand how grid signals (carbon signals andmarket price/load allocations) affect DC operations. An obstacle that ariseshere is the lack of computationally-tractable DC operation models that cancapture objectives, constraints, and information flows that arise at theinterface of DCs and the power grid. To address this gap, we present areceding-horizon resource management model (a mixed-integer programming model)that captures the resource management layer between the DC scheduler and thegrid while accounting for logical constraints, different types of objectives,and forecasts of incoming job profiles and of available computing capacity. Weuse our model to conduct extensive case studies based on public data fromMicrosoft Azure and MISO. Our studies show that DCs can provide significanttemporal load-shifting flexibility that results in reduced carbon emissions andpeak demand charges. Models and case studies are shared as easy-to-use Juliacode.

Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model