A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting

TL;DR

This paper introduces a new metric, locational marginal carbon emission, to optimize data center load shifting geographically for reducing carbon emissions effectively by considering power grid physics.

Contribution

It proposes the $ ext{lambda}_{ ext{CO}_2}$ metric for better geographic load shifting to minimize carbon emissions, outperforming existing metrics.

Findings

$ ext{lambda}_{ ext{CO}_2}$ reduces carbon emissions more effectively.

The metric also lowers electricity generation costs.

It accounts for power grid congestion and physics.

Abstract

Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both geographically and temporally. This paper focuses on how data centers can use their geographic load flexibility to reduce carbon emissions through clever interactions with electricity markets. Because electricity market clearing accounts for congestion and power flow physics in the electric grid, the carbon emissions associated with electricity use varies between (potentially geographically close) locations. Using our knowledge about this process, we propose a new and improved metric to guide geographic load shifting, which we refer to as the locational marginal carbon emission $\lambda_{\text{CO}_2}$. We compare this and three other shifting metrics on their ability to reduce carbon emissions and generation costs throughout the course of a year. Our analysis demonstrates that $\lambda_{\text{CO}_2}$ is more effective in reducing carbon emissions than more commonly proposed metrics that do not account for the specifics of the power grid.