Optimization of Multi-source Complementary Heating System Based on Model Predictive Control

This paper focuses on the research of multi-time scale optimal operation scheduling of multi-source heating system under distributed model predictive control (DMPC). Firstly, a multi-time scale optimization modulation method for multi-source heating system based on distributed model predictive control is proposed to realize flexible scheduling of integrated energy system through coordination of subsystems. Then, the day-ahead and day-day rolling optimization model is established, which aims at the optimal daily operation economy of the system, the minimum daily operation cost of the system and the minimum start-stop penalty cost of the unit. In the real-time stage, the optimization scheduling strategy based on distributed model predictive control (DMPC) is adopted to decompose the overall optimization problem. Each subsystem makes state estimation and optimizes its own performance index according to the input sequence of other subsystems at the previous time. Finally, through the coordinated control of each subsystem, the whole system can be optimized online to meet the dynamic adjustment requirements of the whole system.