Using geographic load shifting to reduce carbon emissions

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 the unique ability to shift computing load between different geographical locations, giving rise to 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, lambda(CO2), 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 includes more realistic assumptions regarding the operation of data centers and electricity markets. Further, we introduce a benchmark model where 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 min load and generation data for an entire year. Our results show that the proposed improvements for the shifting model based on lambda(CO2) are highly effective, leading to results that outperform the benchmark model.