Multi-level and Multi-model Predictive Control of Combined Heat and Power System Based on Improved Weighting Method

Combined heat and power (CHP) system is a comprehensive energy utilization technology that simultaneously generates the electricity and heat. This study proposes a multi-level and multi-model predictive control approach, using gap metric theory to carry out a non-linear analysis of the CHP system. Considering the non-linear characteristics and the multi-condition operation modes in a CHP system, one multi-model predictive control strategy is employed to control the dynamic behavior of the system using weighted linear model controllers. To achieve the offset-free tracking of set-point, the gap metric and the prediction error are adopted to design an improved controller weighting strategy. Besides, a multi-level and multi-model structure of the system is established based on the hierarchical scheduling method. In the experimental part, the proposed control method is implemented to a CHP system with gas turbine and waste heat boiler. Compared with the existing weighting method, the result shows that the proposed method has better performance in the accuracy and rapidity of control.