Hierarchical Scheduling Method of Integrated Energy System(IES) Based on Compensation of Disturbance Predictive Control

To achieve the goal of carbon neutrality, the IES has received significant attention due to its advantages in multi-energy complementarity and the utilization of renewable energy sources. However, the intermittency and volatility of renewable energy sources, as well as the characteristics such as complex system structure, strong coupling of equipment, and large differences in dynamic responses, pose significant challenges to the stable operation of the IES. Optimal scheduling is the core to ensure the economic and safe operation of the IES. Nevertheless, traditional scheduling strategies have two major limitations: Firstly, the use of static models and long-cycle scheduling makes it difficult to cope with the random fluctuations of renewable energy sources and loads. Secondly, based on the open-loop optimization mode, it is unable to effectively handle model errors as well as the prediction deviations of new energy output and loads. Aiming at the above problems, this paper proposes a hierarchical scheduling method based on the prediction control compensation of the disturbance observer. The dynamic scheduling layer conducts rolling optimization based on the dynamic models of equipment and the prediction data of wind power, photovoltaic power, and loads, and generates scheduling instructions for multi-energy equipment. The feedback correction layer, on the other hand, uses the anti-disturbance predictive control algorithm to correct the scheduling variables in real-time according to the control deviations of process parameters, transforming the traditional open-loop scheduling into closed-loop scheduling. Finally, simulations verify that this method can effectively solve the problem of inaccurate scheduling caused by the large inertia of cooling/heating equipment, multi-source disturbances, and modeling errors, and significantly improve the operational stability of the system.