Supervisory optimization of the MGT-CCHP system using model predictive control

Abstract

This paper proposes a supervisory optimal control structure for the micro-gas-turbine based combined cooling heating and power (MGT-CCHP) system. In the upper layer of the structure, a dynamic optimal reference governor is developed to calculate the optimal operating points according to the given economic performance indexes, and in the lower layer of the structure, model predictive control is utilized to track the given operating points, so that a dynamic optimality can be achieved under the input-output constraints during the optimization. Moreover, a disturbance term is introduced in the model to lump the effect of unmeasured disturbances and plant behavior variations, thus, their influences on the supervisory optimization can be removed. The advantages and effectiveness of the proposed method are demonstrated through the simulations on an 80kW MGT-CCHP simulator.