The efficiency of bifacial photovoltaic modules, which can capture light from both sides, is significantly influenced by the reflectivity of the surrounding environment, for example in agrivoltaic settings. We investigate the retroreflective properties of grass and their impact on the energy yield of bifacial solar panels. By combining spectro-angular reflection measurements with computational modeling, we quantify the contribution of grass reflection to overall solar electricity production and evaluate the inaccuracies associated with the assumption that grass behaves as a diffuse reflector. Our results show that this assumption can lead to an overestimation of energy yield by up to 10%, due to the actual retroreflective behavior of grass. This effect is particularly pronounced for long grass configurations. The research underscores the importance of considering the detailed reflectance properties of vegetation in optimizing the placement and performance of bifacial photovoltaics in agricultural environments, with implications for improving the efficiency and sustainability of solar energy generation.

Agrivoltaic systems offer innovative solutions to pressing global challenges such as climate change, renewable energy production, and food security. Specifically, horizontal single-axis tracker agrivoltaic systems can mitigate agricultural yield losses through optimized tracking strategies that balance light distribution between crops and solar panels. This paper presents a methodology for dynamically optimizing solar panel positioning to meet the varying light requirements of crops. Simulations are conducted for a case study of an agrivoltaic system in an apple orchard in southwestern Germany. Conventional shading strategies for apple orchards, based on agronomic experience and hail nets, are challenged, and specific irradiance targets for regional apple varieties are proposed, expressed in W/m2 or Wh/m2/day. Unlike estimated relative shading percentages, these absolute targets facilitate optimization and ensure consistent light availability, addressing issues related to weather variability. The analysis, focusing on tree light availability, is performed using the custom-developed tool APyV. Results indicate that 91% of the target irradiation for apples can be achieved in the simulated year with tailored PV control, resulting in a moderate 20% reduction in electrical yield. Periods when apple light requirements are not met are identified, highlighting the limitations of crop-based optimization. These findings provide valuable guidance for future optimizations that better balance electrical yield with agronomic effectiveness.