Abstract: The use of model predictive control with characteristics of rolling optimization and feedback correction is one of the key technologies to achieve multi-time-scale optimization scheduling of integrated energy systems. In view of the complexity of the centralized model predictive control to achieve the overall online optimization of the system, this paper proposes a multi-time-scale optimization scheduling method for the integrated energy system based on the distributed model predictive control, which realizes the flexible scheduling of the integrated energy system with the coordination of various subsystems. First, day-ahead and intra-day rolling optimization models are established with the objectives of optimal economy in daily operation of the system, the lowest system daily operation costs and unit on-off penalty costs. Then, at the real-time stage, an optimal scheduling strategy based on the distributed model predictive control is used to decompose the overall optimization problem. Each subsystem estimates the state according to the input sequence of the previous time of other subsystems and optimizes its performance index. Finally, through the coordinated control of various subsystems, the online optimization of the entire system is realized to meet its dynamic adjustment demands. The simulation results show that the proposed method can improve the economy of the system operation while improving the control performance of the system.