• Energy Research

Saving energy in greenhouses is an important issue for growers. Here, we present a method to minimize the total energy that is required to heat and cool a greenhouse. Using this method, the grower can define bounds for temperature, humidity, CO2 concentration, and the maximum amount of CO2 available. Given these settings, optimal control techniques can be used to minimize energy input. To do this, an existing greenhouse climate model for temperature and humidity was expanded to include a CO2 balance. Heating, cooling, the amount of natural ventilation, and the injection of industrial CO2 were used as control variables.Standard optimization settings were defined in order to compare the grower's strategy with the optimal solution. This optimization resulted in a theoretical 47% reduction in heating, 15% reduction in cooling, and 10% reduction in CO2 injection for the year 2012. The optimal control does not need to maintain a minimum pipe temperature, in contrast to current practice. When the minimum pipe temperature strategy of the grower was implemented, heating and CO2 were reduced by 28% and 10% respectively.We also analyzed the effect of different bounds on optimal energy input. We found that as more freedom is given to the climate variables, the higher the potential energy savings. However, in practice the grower is in charge of defining the bounds. Thus, the potential energy savings critically depend on the choice of these bounds. This effect was analyzed by varying the bounds. However, because the effect can be demonstrated to the grower, the outcome has value to the grower with respect to decision making, an option that is not currently available in practice today.

In the daily operation of a greenhouse, decisions must be made about the best deployment of equipment for generating heat and electricity. The purpose of this paper is twofold: (1)To demonstrate the feasibility and flexibility of an optimal control framework for allocating heat and electricity demand to available equipment, by application to two different configurations used in practice. (2)To show that for a given energy and electricity demand benefit can be obtained by minimizing costs during resource allocation. The allocation problem is formulated as an optimal control problem, with a pre-defined heat and electricity demand pattern as constraints. Two simplified, yet realistic, configurations are presented, one with a boiler and heat buffer, and a second one with an additional combined heat and power generator (CHP)and a second heat buffer. A direct comparison with the grower is possible on those days where the other equipment that was at the grower's disposal was not used (63 days in the available 2012 data set). On those days overall costs savings of 20% were obtained. This shows that a given heat demand does not come with a fixed price to pay. Rather, benefits can be obtained by determining the utilization of the equipment by dynamic optimization. It also appears that prior knowledge of gas and electricity prices in combination with dynamic optimization has a high potential for cost savings in horticultural practice. To determine the factors influencing the outcome, different sensitivities to the optimization result were analyzed.

Based on differences in dynamic response times in the crop production process, a hierarchical decomposition of greenhouse climate management is proposed. To a large extent the proposed decomposition builds on the time-scale decomposition of singularly perturbed systems commonly found in the literature. Main difference with these existing theoretical concepts is that the proposed decomposition is able to deal with rapidly fluctuating deterministic external inputs or disturbances acting on the fast sub-processes. For an example of economic optimal greenhouse climate management during one lettuce production cycle, the decomposition was successfully evaluated in simulations. Using these favourable results, a hierarchical concept for economic optimal greenhouse climate management is derived and discussed in view of application in horticultural practice.

Need for reduction of energy use in greenhouse production has increased. First objective was to develop and validate a dynamic air temperature model. Second objective was to minimize total energy input to the greenhouse, for pre-set temperature boundaries. Optimal control techniques were used to minimize total energy input (cooling and heating). Results confirm that air temperature is on the upper boundary when cooling is applied and on the lower boundary when heating is applied. This work is a first step toward optimal deployment of a large array of possible options for the grower to optimally satisfy heat or cooling.