A GNN-based Day Ahead Carbon Intensity Forecasting Model for Cross-Border Power Grids

Carbon intensity forecasting of power grids is critical to the optimization of demand-side consumers. Recently, cross-border power grids have emerged, i.e., those allowing electricity to be transmitted across different national transmission systems. Cross-border power grids substantially increase the sharing of highly variable renewable energy sources (VRE), leading to greater economic benefits and increased reliability. In Europe, the total volume of cross-border electricity that is exchanged comprises 13% of the annual net electricity that is generated. Current studies on carbon intensity forecasting, however, apply to individual regional power grids. In cross-border grids, the carbon intensity of a regional grid depends not only on that of its own electricity but also on the carbon intensity from the electricity exchanged with cross-border grids. Thus, if the cross-border electricity exchange is not captured appropriately, significant forecasting errors can occur.