Optimal Energy Management of Data Center Micro-Grid Considering Computing Workloads Shift

Abstract

Data centers have been experiencing increasingly significant challenges in electricity consumption and carbon emissions with the fast-development of artificial intelligence (AI). In the context of carbon neutrality, the integration of data centers with renewable green energy has become a prevailing trend. To effectively integrate renewable energy, it is imperative to thoroughly explore the data center’s operation flexibility. Delay-tolerant computational workloads have been considered as one of the most promising flexible resources for power regulation within data center micro-grids (DCMs). This paper first analyzes the working characteristics of three kinds of typical delay-tolerant computing workloads, i.e. short-running deferrable workloads, long-running continuous workload, long-running interruptible workload, and then clarifies the time-shifting mechanisms for each. Next, the corresponding time-shifting models of the delay-tolerant workloads are established. Finally, considering the time attributes of workloads and system settings, the day-ahead optimization scheduling framework of DCM incorporating the time-shifting models of multiple workloads are formulated, with the aim of minimizing the operation cost of DCM and renewable power curtailment. Application of the power management scheme in a data center case study confirms its effectiveness in improving the operational economy of data center and increasing green energy utilization.