Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty

The fluctuating uncertainty of load demand as an influencing factor for day-ahead scheduling of an integrated energy system with photovoltaic (PV) power generation may cause an imbalance between supply and demand, and to solve this problem, this paper proposes a day-ahead optimal scheduling model considering uncertain loads and electric heating appliance (EH)–PV energy storage. The model fuses the multi-interval uncertainty set with the CNN-MI-BILSTM neural network prediction technique, which significantly improves the accuracy and reliability of load prediction and overcomes the limitations of traditional methods in dealing with load volatility. By integrating the EH–photothermal storage module, the model achieves efficient coupled power generation and thermal storage operation, aiming to optimize economic targets while enhancing the grid’s peak-shaving and valley-filling capabilities and utilization of renewable energy. The validity of the proposed model is verified by algorithm prediction simulation and day-ahead scheduling experiments under different configurations.