Optimal operation of electro-thermal integrated energy systems based on source-load scenario generation and tiered carbon trading

In integrated energy systems (IES), the deep coupling of heterogeneous energy flows results in complex interdependencies between energy sources and loads. However, current optimization strategies for IES scheduling often overlook the dynamic coupling relationships between sources and loads, which poses significant challenges to system scheduling and planning. To address this issue, this study proposes a scenario generation method based on a CNN-GRU-Attention architecture. Convolutional Neural Networks (CNN) are employed to extract local features, Gated Recurrent Units (GRU) are used to model the temporal dynamics of time series data, and an Attention mechanism is incorporated to enhance the weighting of critical information—thereby capturing the source-load correlations more effectively. Furthermore, a reward-penalty tiered carbon emission mechanism is introduced, which incentivizes substantial emission reductions and penalizes excessive emissions through segmented carbon pricing. Based on this, a low-carbon economic scheduling model for an integrated electricity-heat energy system is constructed with the objective of minimizing both operational and carbon emission costs. Simulation studies based on a representative IES in Northwest China demonstrate that the proposed CNN-GRU-Attention model effectively learns features and patterns from historical data and captures source-load correlations with high accuracy. The implementation of the reward-penalty tiered carbon trading mechanism results in an 18.32% reduction in carbon emissions, albeit with a 7.67% increase in carbon costs. When source-load correlation is comprehensively considered, the total system cost decreases by 7.34%, and carbon emissions are further reduced by 18.83%. Seasonal analysis reveals reductions in operational costs of 15.64%, 8.36%, and 11.72% in winter, summer, and transition seasons, respectively, indicating strong adaptability across seasons.