Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption

Greenhouse systems consume lots of energy, water and carbon dioxide (CO2) to provide a suitable growth environment for crops. Due to the problems of operation mode, some greenhouse systems are inefficient and need to be optimized. In this paper, four optimization strategies for improving the operation efficiency of greenhouse systems are studied. Strategy 1 minimizes the energy consumed for greenhouse heating, cooling, ventilation and irrigation. Strategy 2 minimizes the water consumed for irrigation. Strategy 3 minimizes the CO2 consumed for greenhouse CO2 enrichment. Strategy 4 minimizes the total cost of energy, water and CO2 consumed. These optimization strategies are based on a multi-input multi-output (MIMO) climate model and a modified evapotranspiration model. Moreover, a sensitivity analysis is conducted to study the influence of electricity price, water price, CO2 price and the range of system constraints on the optimization results. Finally, a model predictive controller (MPC) is designed to reject system disturbances and address model plant mismatch. The MPC controller is compared with a commonly used open loop controller. A performance index relative average deviation (RAD) is introduced to evaluate the tracking performance of the proposed MPC and the compared open loop control. Simulation results show that Strategy 4 reduce the total cost by 66.60 %, 92.68 % and 68.83% compared with Strategy 1, Strategy 2 and Strategy 3 respectively. Changes in electricity price have a greater impact on optimization results than changes in water price and CO2 price. Both temperature constraints and relative humidity constraints have a great influence on the optimization results. The controller designed is verified to be effective.

Automated summary

This study examines four optimization strategies to improve the efficiency of greenhouse systems, with Strategy 4 showing the most significant reduction in total costs. Changes in electricity prices have a greater impact on optimization results, and temperature and humidity constraints also play an important role. The designed controller is proven to be effective in rejecting disturbances and addressing model plant mismatch.