An edge-conditioned convolution model for carbon flow rate prediction under source-load uncertainty

Introduction The realization of China’s dual-carbon goals relies on accurate and fine-grained carbon emission modeling of the new-type power systems. Conventional carbon calculation methods are designed for traditional power systems and rely on deterministic power flow calculations. This makes it difficult to capture the impacts of source–load uncertainty on the carbon flow rate under high renewable penetration. Methods This paper proposes a carbon flow rate prediction model based on Edge Conditioned Convolution (ECC) to account for source–load uncertainty. First, a carbon flow rate calculation framework is established by incorporating the full lifecycle carbon emissions of renewable generators and a network loss allocation mechanism. This mechanism combines upstream and downstream power flow tracing to allocate network losses between the generation and consumption sides using an allocation coefficient. Second, an ECC-based carbon flow rate prediction model is developed to incorporate branch attributes into information aggregation through edge-conditioned convolution weights. A physics-informed loss function is adopted to encourage the conservation of the carbon flow rate. Furthermore, by using Sobol sequences and Inverse Transform Sampling (ITS) algorithms to generate multi-scenario data sets, the application scenarios of the prediction model are extended to power systems with high renewable-energy penetration. This also enhances the model’s generalization ability under uncertainty. Results Case studies show that the proposed model achieves a mean absolute error of approximately 0.144 tCO 2 /h, confirming practical prediction accuracy. Further analysis reveals that under different renewable penetration levels, the method more accurately characterizes carbon flow rate distributions and adapts better to source–load uncertainty than conventional carbon flow tracing methods. Discussion These results support the use of ECC to predict carbon flow rates under source–load uncertainty and the specified network-loss allocation scheme, providing a potential basis for low-carbon power system dispatch.