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 crop.

This study focused on the photosynthetic photon flux density and employed an all-climate solar spectrum model to calculate the photosynthetic photon flux density accurately on farmland partially shaded by solar panels and supporting tubes. This study also described an algorithm for estimating the photosynthetic photon flux density values under solar panels.

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 provides a bibliometric analysis of agrivoltaic topics based on 121 publications indexed in SCOPUS, in which either economic assessments of agrivoltaics, agrivoltaic systems for crops and livestock animals, photovoltaic greenhouse and agrivoltaics with open field are discussed, or its ideas are used to analyze certain locations.

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.

Agrivoltaic systems have an increasing interest. Realizing this upcoming technology raises still many challenges at design, policy and economic level. This study addresses a geospatial methodology to quantify the important design and policy questions across Europe. An elevated agrivoltaic system on arable land is evaluated: three crop light requirements (shade-loving, shade-tolerant and shade-intolerant) are simulated at a spatial resolution of 25 km across the European Union (EU). As a result, this study gives insight into the needed optimal ground coverage ratio (GCR) of the agrivoltaic system for a specific place. Additionally, estimations of the energy production, levelized cost of energy (LCOE) and land equivalent ratio (LER) are performed in comparison with a separated system. The results of the study show that the location-dependent solar insolation and crop shade tolerance have a major influence on the financial competitiveness and usefulness of these systems, where a proper European policy system and implementation strategy is required. Finally, a technical study shows an increase in PV power of 1290 GWp (almost × 10 of the current EU’s PV capacity) if potato cultivation alone (1% of the total arable agricultural area) is converted into agrivoltaic systems.

The increasing pressure on land resources for food and energy production along with efforts to maintain natural systems necessitates the development of compatible land uses that maximize the co-benefits of multiple ecosystem services. One such land sharing opportunity is the restoration and management of native grassland vegetation beneath ground-mounted solar energy facilities, which can both protect biodiversity and restore related ecosystem services. In this paper, the researchers applied the InVEST modeling framework to investigate the potential response of four ecosystem services (carbon storage, pollinator supply, sediment retention, and water retention) to native grassland habitat restoration at 30 solar facilities across the Midwest United States. Compared to pre-solar agricultural land uses, solar-native grassland habitat produced a 3-fold increase in pollinator supply and a 65% increase in carbon storage potential. The researchers also observed increases in sediment and water retention of over 95% and 19%, respectively. They applied these results to project the potential benefits of adoption of native grassland management practices in current and future solar energy buildout scenarios. Their study demonstrates how multifunctional land uses in agriculture-dominated landscapes may improve the provision of a variety of ecosystem services and improve the landscape compatibility of renewable energy and food production. These findings may be used to build cooperative relationships between the solar industry and surrounding communities to better integrate solar energy into agricultural landscapes.