Optimal Power Scheduling Using Data-Driven Carbon Emission Flow Modelling for Carbon Intensity Control

The anthropogenic carbon emissions in the power system would continue to increase with growing energy consumption. In this regard, researchers have focused on managing the demand side with carbon tracing tools (e.g., carbon emission flow model) to achieve low-carbon transition, as it is the need of customers that drives the combustion of fossil fuels and creates substantial carbon emissions. However, such a low-carbon transition cannot be achieved effectively without the right incentive scheme. It is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, in this paper, a low-carbon optimal scheduling model with carbon intensity control by demand response (DR) mechanism is proposed. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Herein, energy consumption is linked to carbon emission via carbon emission flow (CEF) model. Also, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the CEF analysis to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered.