Tag Archive for: Modeling

This work contributes to agrivoltaic design methodology through a digital replica and genomic optimization framework which simulates light rays in a procedurally generated agrivoltaic system at an hourly timestep for a defined crop, location and growing season to model light absorption by the photovoltaic panels and the chosen tomato crop.

This article posits that in order to optimize agrivoltaic systems for crop growth, energy pathways must be characterized. While solar panels shade crops, they also emit longwave radiation and partially block the ground from downwelling longwave radiation. The authors suggest that a deeper understanding of the spatial variation in incoming energy would enable controlled allocation of energy in the design of agrivoltaic systems. This paper also presents a model to quantify the downwelling longwave energy at the ground surface in an agrivoltaic array and demonstrates that longwave energy should not be neglected when considering a full energy balance on the soil under solar panels.

When installing photovoltaic panels on agricultural land, one of the most important aspects to consider are the effects of the shadows of the panels on the ground. This study presents a valid methodology to estimate the distribution of solar irradiance in agrivoltaic installations as a function of the photovoltaic installation geometry and the levels of diffuse and direct solar irradiance incident on the crop land.

The researchers in this study aimed to simulate crop yields for paddy rice, barley, and soybeans grown under photovoltaic panels with an eye on reaching suitable agricultural productivity for the energy and food nexus coexistence. They also applied a geospatial crop simulation modeling system to stimulate the regional variations in crop yield according to solar radiation reduction scenarios.

In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to PV installations is quantified by introducing and calculating the “Social Disturbance” (SDIS) indicator, whereas optimum locations are determined for predefined values of two siting preferences (maximum allowable PV locations—grid station distance and minimum allowable total coverage area of PV installations). Thematic maps of appropriate selected exclusion criteria are produced, followed by a cumulative weighted viewshed analysis, where the SDIS indicator is calculated. Optimum solutions are then determined by developing and employing a Genetic Algorithms (GAs) optimization process. The methodology is applied for the municipality of La Palma Del Condado in Spain for 100 different combinations of the two siting preferences. The optimization results are also employed to create a flexible and easy-to-use web-GIS application, facilitating policy-makers to choose the set of solutions that better fulfils their preferences. The GAs algorithm offers the ability to determine distinguishable, but compact, regions of optimum locations in the region, whereas the results indicate the strong dependence of the optimum areas upon the two siting preferences.