学术文献库

An increasing focus on the electricity use and carbon emissions associated with computing has lead to pledges by major cloud computing companies to lower their carbon footprint. Data centers have a unique ability to shift computing load between different geographical locations, giving rise to geographic load flexibility that can be employed to reduce carbon emissions. In this paper, we present a model where data centers shift load independently of the ISOs. We first consider the impact of load shifting guided by locational marginal carbon emissions, denoted by $λ_{\text{CO}_2}$, a sensitivity metric that measures the impact of incremental load shifts. Relative to previous models for data center load shifting, the presented model improves accuracy and include more realistic assumptions regarding the operation of both data centers and the electricity market. Further, we introduce a new benchmark model in which data centers have access to the full information about the power system and can identify optimal shifts for the current time period. We demonstrate the efficacy of our model on the IEEE RTS GMLC system using 5 minute load and generation data for an entire year. Our results show that the proposed accuracy improvements for the shifting model based on $λ_{\text{CO}_2}$ are highly effective, leading to results that outperform the benchmark model.