This paper presents a novel 3D agrovoltaic modelling tool developed in python which enables technical and economical evaluation of potential agrovoltaic designs. It has been designed and applied for fruit crops which typically have a crucial flowering period. To illustrate the potential of this tool, a case study for pear trees in Bierbeek, Belgium is shown. While many geometrical parameters of agrovoltaic systems are fixed in practice, however, there is also the need to model the impact of PV modules on the tree light interception. The results of the modelling show that the amount of solar radiation depends on the modules used, with semi-transparent modules offering better light distribution and reduced crop loss. Based on the modelling, a prototype agrovoltaic set-up with pear trees and semitransparent modules has been built in Bierbeek, Belgium.
Tag Archive for: Simulation
Global energy consumption and costs have increased exponentially in recent years, accelerating the search for viable, profitable, and sustainable alternatives. Renewable energy is currently one of the most suitable alternatives. The high variability of meteorological conditions (irradiance, ambient temperature, and wind speed) requires the development of complex and accurate management models for the optimal performance of photovoltaic systems. The simplification of photovoltaic models can be useful in the sizing of photovoltaic systems, but not for their management in real time. To solve this problem, we developed the I-Solar model, which considers all the elements that comprise the photovoltaic system, the meteorological conditions, and the energy demand. We have validated it on a solar pumping system, but it can be applied to any other system. The I-Solar model was compared with a simplified model and a machine learning model calibrated in a high-power and complex photovoltaic pumping system located in Albacete, Spain. The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%. However, models based on machine learning are specific to the system evaluated, while the I-Solar model can be applied to any system.
Achieving optimal yield and quality at harvest depends on the grower’s ability to avoid abiotic stresses (water, light, and temperature). This task has usually been satisfied through the implementation of adequate horticultural practices. In the context of clean energy transition and global climate change, growers nowadays have the possibility to grow their crops under solar panels, which modify the micro-environment of the crops. Being able to anticipate the behavior of plants under these new micro-environmental conditions would help growers adapt their horticultural practices. For electricity producers, in the context of dynamic agrivoltaic systems, anticipating the crop status is useful to choose a solar panels steering policy that maximizes electricity production while ensuring favorable environmental conditions for the crop to grow. To help electricity producers and growers estimate a crop status under panels, we developed a decision support system (DSS) called crop_sim. As of now, it can be used to monitor two types of perennial crops: grapevines and apple trees. crop_sim produces three indicators of the crop status: predawn water potential, canopy temperature and carbon production. Besides providing information on the crop status, the DSS incorporates an expert system which indicates the best time and the amount of irrigation to maintain a desired water status under the new micro-environmental conditions. This paper first focuses on the description of crop_sim and the usefulness of the three indicators. Then, a case study is presented. Our results show that, in a mature vineyard, with a typical panel steering policy conservative on crop yield, growers could save 13% of water compared to an open-field reference. Experimental data pertaining to apple trees, grapevines, tomatoes, and maize are being collected. They will be used to adapt the model to tomato and maize, evaluate it and make it robust enough to bring to market. Further improvements of the crop_sim model may be required to finely reproduce observations in the field. A full validation of the model is expected when all data from the experiments will be available. The DSS will evolve depending on the requirements of the agrivoltaics community and may incorporate additional plant indicators and new expert system rules.
A system combining soil grown crops with photovoltaic panels (PV) installed several meters above the ground is referred to as agrivoltaic systems. In this work a patented agrivoltaic solar tracking system named Agrovoltaico®, was examined in combination with a maize crop in a simulation study. To this purpose a software platform was developed coupling a radiation and shading model to the generic crop growth simulator GECROS. The simulation was conducted using a 40-year climate dataset from a location in North Italy, rainfed maize and different Agrovoltaico configurations (that differ according to panel density and sun-tracking set up). Control simulations for an irrigated maize crop under full light were added to results. Reduction of global radiation under the Agrovoltaico system was more affected by panel density (29.5% and 13.4% respectively for double density and single density), than by panel management (23.2% and 20.0% for suntrack and static panels, respectively). Radiation reduction, under Agrovoltaico, affected mean soil temperature, evapotranspiration and soil water balance, on average providing more favorable conditions for plant growth than in full light. As a consequence, in rainfed conditions, average grain yield was higher and more stable under agrivoltaic than under full light. The advantage of growing maize in the shade of Agrovoltaico increased proportionally to drought stress, which indicates that agrivoltaic systems could increase crop resilience to climate change. The benefit of producing renewable energy with Agrovoltaico was assessed using the Land Equivalent Ratio, comparing the electric energy produced by Agrovoltaico cultivated with biogas maize to that produced by a combination of conventional ground mounted PV systems and biogas maize in monoculture. Land Equivalent Ratio was always above 1, it increased with panel density and it was higher with sun tracking than with static panels. The best Agrivoltaico scenario produced twice as much energy, per unit area, as the combination of ground mounted PV systems and biogas maize in monoculture. For this Agrivoltaico can be considered a valuable system to produce renewable energy on farm without negatively affecting land productivity.