A carbon emission factors prediction method for user-side nodes in power grids considering uncertainty of combined wind and solar outputs

To address the problem that existing carbon emission factors calculation methods for nodes in power grids struggle to handle the output uncertainty caused by high-penetration wind and solar energy integration, a prediction method considering the uncertainty of combined wind-solar output is proposed. First, a joint probability distribution of wind-solar output is constructed, followed by scenario generation and reduction to obtain typical sequences of combined wind-solar output. On this basis, the carbon emission factors of each node are accurately calculated by combining power grid power flow and carbon emission flow theories, and a training sample set is established. Furthermore, a variant graph convolutional network is adopted to model the power grid topology and the electricity-carbon coupling relationship between nodes, enabling the rapid prediction of user-side node carbon emission factors in future periods solely based on source-load forecast data. Simulation results show that the proposed method maintains high accuracy even under high-penetration renewable energy scenarios, with a mean absolute error (MAE) of 0.029 t CO₂/(MW·h) and a mean absolute percentage error (MAPE) of 3.9% for node carbon emission factor prediction, thus verifying its effectiveness.