The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide electricity users in carbon reduction and promote power industry low-carbon transformation. Fundamentally, calculating indirect carbon emissions involves allocating direct carbon emission data from the power source side, indicating that accurate indirect emission results rely on the precise measurement of power source emissions. However, existing research on indirect carbon emissions in large-scale power systems rarely accounts for variations in carbon emission characteristics under different operating conditions of power sources, such as rated/non-rated operating conditions and ramping up/down conditions, making it difficult to reflect source-side and load-side carbon emission information variation during providing ancillary services. Quadratic and exponential functions are proposed to characterize the energy consumption profiles of coal-fired and gas-fired power generation, respectively, to construct a refined carbon emission model for power sources. By leveraging the theory of power system carbon flow, we analyze how variable operating conditions of power sources impact indirect carbon emissions. Case studies demonstrate that changes in power source emissions under variable conditions have a significant effect on the indirect carbon emissions of power grids.

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%.