article · Energy Conversion and Management
Agrivoltaic systems combine farming and solar energy generation on the same land, but panel shading often reduces agricultural output. To resolve this trade-off, an optimisation model was created to identify panel setups that balance crop yields and electricity generation. Implemented in MATLAB using genetic algorithms, the model incorporates variables such as panel elevation, tilt, row spacing, table spacing, and panel dimensions alongside local climatic data. The approach was tested through a case study of maize cultivation in Kamboinsin, Burkina Faso. Findings show that sunlight distribution under the panels is strongly affected by elevation and spacing, with crop yields being particularly sensitive to the distance between panel rows. Additionally, smaller solar tables supported higher crop yields, reaching up to 92.8 percent of full-sun harvest, though this configuration reduced overall solar capacity compared to larger multi-panel setups.
As the demand for both food and clean energy expands, land competition between farming and solar developments grows. Agrivoltaics allows both activities on the same plot, but poorly planned solar shading harms food production. This research provides a mathematical method to configure solar panel layouts, helping planners protect agricultural productivity while still generating renewable energy.
This model could be used by solar developers, agricultural planners, and engineering consultancies designing dual-use land projects. It assists in customising panel layouts for specific crops and climates. Currently, the work represents applied computational modelling and simulation validated on a regional case study, meaning further engineering and field-level integration would be required for deployment as a commercial planning tool.
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The global population is experiencing rapid growth, leading to increased demand for energy and food resources, necessitating the expansion of cultivated land. The construction of photovoltaic power plants to meet energy needs may result in competition for land between the agriculture and energy sectors. To address this issue, agrivoltaics systems are perceived as a solution, allowing for the coexistence of agricultural and energy production in the same area. However, the shading caused by solar panels can potentially. Therefore, a model has been developed to determine the best configuration for maximising both crop yields and energy production from the photovoltaic field. The purpose of this paper is to develop a model that optimizes energy production and crop yield within an agrivoltaics system. The model integrates factors such as elevation, spacing, tilt, panel technology and size to enhance the radiation under the photovoltaic panels, as well as to increase crop yield and the efficiency of photovoltaic array. It is constructed based on the climatic condition and the relationship between the shaded area and the sunlight distribution below the photovoltaic panels. Furthermore, the model relies on the correlation between the configuration used and the energy power delivered by the photovoltaic array. A set of equations that link configuration, sunlight, crop yield, and photovoltaic panel power was developed, and the model was implemented in MATLAB, using genetic algorithm optimisation techniques. The initial step involves the determination of radiation values under the panels, followed by the identification of the best scenarios for subsequent simulations aimed at evaluating crop yield and power generation from the photovoltaic array. A case study was conducted in Kamboinsin village (12°27′ N, 1°33′ W), in Burkina Faso, focusing onusing corn cultivation to validate the model. The results show that the model effectively identifies the optimal configuration for maximizing both crop yield and photovoltaic field output. The simulation results reveals that the distribution of radiation under the panels is significantly influenced by factors such as panel elevation height, spacing between table, and spacing between rows of table. Notably, the yield is more sensitive to the spacing between rows of panels. When comparing the effects of the different panel sizes, it is evident that utilizing smaller tables leads to higher crop yields. However, this approach results in a decrease in energy production from the photovoltaic field. For instance, on 1 ha of land, a table consisting of a single 100 Wp panel generated 92.8 % of the crop yield achieved in full sun with a nominal power of 96.9 kWp, whereas a table comprising 2 panels of 260 Wp produced 80.1 % of the yield with a nominal power of 378.56 kWp.
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DOI: 10.1016/j.enconman.2024.118436
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