Hierarchical model predictive control of Venlo-type greenhouse climate for improving energy efficiency and reducing operating cost

In this paper, a hierarchical control strategy for Venlo-type greenhouse climate control under South Africa climate is proposed to improve energy efficiency and reduce operating cost. The proposed hierarchical control architecture includes two layers. The upper layer is to generate set points by solving different optimization problems. Three different strategies with different optimization objectives are studied. The meteorological data of a typical winter day is used. Strategy 1 is to minimize the energy consumption. Strategy 2 is to minimize the energy cost under the time-of-use (TOU) tariff. Strategy 3 is to minimize the total cost of energy consumption, ventilation and carbon dioxide (CO2) supply. The lower layer is to track the trajectories obtained from the upper layer. A closed-loop model predictive control (MPC) strategy is introduced to address model plant mismatch and reject system disturbances. Two performance indices, relative average deviation (RAD) and maximum relative deviation (MRD), are introduced to compare the tracking performance of the proposed MPC and an open loop control under three different levels of system disturbances (2%, 5%, 10%). Simulation results show that the proposed strategy can effectively reduce the operating cost while keeping the temperature, relative humidity and CO2 concentration within required ranges. Compared with Strategy 1 and Strategy 2, the total cost of Strategy 3 is reduced by 72.07% and 71.41% respectively. Moreover, the proposed MPC has better tracking performance than the open loop control. Therefore, the proposed hierarchical MPC strategy could be an effective way to improve greenhouse energy efficiency and achieve sustainable cleaner production.