Estimating the carbon emission reduction potential of using carbon-oriented demand response for data centers: A case study in China

Abstract

The rapid advancement of artificial intelligence (AI) has significantly increased the computational load on data centers. AI-related computational activities consume considerable electricity and result in substantial carbon emissions. To mitigate these emissions, future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands. This paper aims to investigate how much carbon emission reduction can be achieved by using a carbon-oriented demand response to guide the optimal planning and operation of data centers. A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load. In the planning model, future operation simulations comprehensively coordinate the temporal-spatial flexibility of computational loads and the quality of service (QoS). An empirical study based on the proposed models is conducted on real-world data from China. The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province, Ningxia Hui Autonomous Region, Sichuan Province, Inner Mongolia Autonomous Region, and Qinghai Province, accounting for 57% of the total national increase in server capacity. 33% of the computational load from Eastern China should be transferred to the West, which could reduce the overall load carbon emissions by 26%.