Mixed-Integer Bi-Level Approach for Low-Carbon Economic Optimal Dispatching Based on Data-Driven Carbon Emission Flow Modelling

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R2) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems.