Optimal management of energy resources in greenhouse crop production systems.

  • van Beveren, Peter (PI)
  • Bontsema, Jan (CoI)
  • van Henten, Eldert (CoI)
  • van Straten, Gerrit (CoI)
  • van Beveren, Peter (PhD candidate)
  • van Henten, Eldert (Promotor)
  • van Straten, Gerrit (Promotor)
  • Bontsema, Jan (Co-promotor)

Project: PhD

Project Details


Costs of gas and electricity, societal acceptance of greenhouse crop production, and agreements between the horticultural sector and the government in The Netherlands increased the quest for energy saving in modern greenhouse horticulture. This has led to investments of growers in a wide variety of equipment for controlling the greenhouse climate. Given the broad range of available climate conditioning equipment and energy sources, their optimal deployment in view of energy conservation has become a complex matter. The main objective of this thesis was to develop an optimization framework for minimizing the total energy consumption and energy costs of greenhouse horticulture in the Netherlands. A two-stage approach was proposed in this thesis in order to minimize the energy consumption and costs of modern greenhouses. In the first stage, the grower defines desired trajectories for the greenhouse climate, i.e. the climate recipe. Then, optimal control techniques using models of the greenhouse climate physics, are used to calculate the demand for heating, cooling, and CO2. In the second stage, this energy demand serves as a reference, an optimal energy distribution of this demand over the various types of equipment was calculated using models of the technical infrastructure. In this thesis, the proposed model-based two-stage approach was demonstrated for one test greenhouse. The potential energy and cost savings of dynamic optimization were successfully shown for this test greenhouse. The potential energy and cost savings of dynamic optimization were shown for this test greenhouse. The energy optimization in the first stage resulted in a theoretical 47% reduction in heating, 15% reduction in cooling, and 10% reduction in CO2 injection for the year 2012 (Chapter 3). The costs optimization in the second stage resulted in a potential cost savings of 29% given the prices for gas and electricity and the known weather (Chapter 5). The results showed that optimization of the greenhouse energy system is feasible and beneficial. The two-stage method is in close connection with the grower's daily practice. It is to be expected that with online application in practice the energy and cost savings will be lower, but still substantial. The application of the two-stage approach in practice seems promising. A number of challenges still need to be solved for this, such as tuning the models online, availability of consistent data, including weather forecasts, and price information.
Effective start/end date15/10/1131/01/23


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