Simulation study uncovers energy yield variations in vertical bifacial PV systems, highlighting the significance of vegetation and shading impacts.
Vertical bifacial photovoltaic (PV) systems offer significant advantages in land‐use efficiency and energy yield, yet their performance is highly sensitive to environmental factors such as terrain topography, vegetation, module orientation, and ground albedo. Accurate prediction of energy yield is therefore critical for economic assessment and project bankability, but remains challenging due to complex rear‐side irradiance contributions and diffuse light capture. This study investigates a linear vertical bifacial PV installation in the Rhône valley, southeastern France, employing high‐resolution drone‐based LiDAR data to create a detailed 3D representation of terrain, vegetation, and PV modules. Within the H2020 SERENDI‐PV project, four partners—CEA, Cythelia, Lucisun, and Solargis—applied their proprietary PV modeling tools to simulate module‐level and plant‐scale energy yields, shading losses, and direct/diffuse irradiance profiles. A round‐robin framework enabled cross‐comparison and validation against high‐resolution monitoring data from microinverters, revealing that all tools reproduced the characteristic double‐peak diurnal energy profile of vertical bifacial arrays, while residual discrepancies were mainly associated with low‐angle shading transitions. Key insights include the dominance of shading and diffuse light in energy variability, the critical role of high‐fidelity site characterization, and the complementary strengths of GPU‐accelerated and harmonized modeling approaches. At the plant scale, annual energy prediction deviations were reduced to 2%–4%, demonstrating that LiDAR‐enhanced modeling combined with advanced simulation tools provides a robust, bankable framework for vertical bifacial PV performance assessment. These results highlight pathways for improved modeling, including spectrally‐resolved irradiance, dynamic albedo incorporation, and standardized LiDAR‐to‐simulation workflows.
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Tsanakas et al. (2025) studied this question.
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