Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC

The uncertainty of renewable energy makes the optimal scheduling of integrated energy systems (IES) challenging and complex. The paper suggests a novel two-stage optimized scheduling model based on distributionally robust adaptive model predictive control (DRAMPC), which effectively improves scheduling accuracy and efficiency while taking robustness and economy into account. In the day-ahead stage, the multi-objective distributionally robust optimization (MODRO) model is composed based on the imprecise dirichlet model (IDM), which incorporates robustness and operating cost metrics into the optimization objective to achieve synergistic optimization of robustness and economy. The adaptive step double-loop model predictive control (ASDL-MPC) utilizes the dual closed-loop feedback of renewable energy output prediction errors and operating cost prediction errors to adaptively adjust the rolling time step, correcting the day-ahead scheduling bias while improving scheduling efficiency. The model is resolved using the cross-entropy-radar scanning differential evolution (CERSDE) algorithm. The results show that the DRAMPC model can balance economy and robustness, improving the economy of IES by 2.27% while ensuring robustness. The ASDL-MPC intra-day rolling optimization further improves scheduling accuracy and also increases computational efficiency by 6.58%.