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

TL;DR

This paper introduces a receding-horizon scheduling model for data centers that captures grid signals and operational constraints, enabling load-shifting to reduce carbon emissions and peak demand, validated through case studies with real data.

Contribution

It presents a novel mixed-integer programming model that effectively captures the interface between data center operations and power grid signals, addressing computational challenges.

Findings

DCs can significantly reduce carbon emissions through load shifting.

The model demonstrates potential for lowering peak demand charges.

Case studies validate the effectiveness of the approach.

Abstract

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.