The optimal energy management of a multi-energy system is a complicated optimization task. This work explores the optimal scheduling problem of power systems under the conditions of distributed power sources and energy storage devices. Firstly, model each unit in the system, explain its operating mechanism, provide constraints during the operation process, and consider the objective function of minimizing scheduling costs. A Model Predictive Control (MPC) based solution is developed that includes distributed power sources and energy storage devices. Rolling optimization was carried out based on predicted data, and feedback correction was continuously performed using measured data during the optimization process to form a complete closed-loop control. In addition, this work compares and analyzes the optimization control methods. The results show that the algorithm proposed in this article can more effectively utilize the output of distributed power sources, fully utilize the charging and discharging characteristics of energy storage devices, improve their economic benefits, and reduce the impact and burden on the public power grid.