Towards precision in bifacial photovoltaic system simulation: a model selection approach with validation - Grommes, Eva-Maria; Koch, Maximilian; Hadji-Minaglou, Jean-Régis; Voos, Holger; Blieske, Ulf (Frontiers in Energy Research, 2025)
As global reliance on sustainable energy solutions intensifies, there is a growing need to optimise and accurately predict renewable energy outputs. Bifacial photovoltaic systems, which are capable of capturing irradiance on both their front and rear sides, represent a significant advancement over traditional monofacial systems, yielding higher energy per area. The accuracy of simulation models for these systems has a direct impact on their financial viability, necessitating the use of comprehensive and reliable simulation frameworks. This research validates BifacialSimu, an open-source simulation tool designed to enhance the prediction of bifacial PV system energy outputs by incorporating multiple simulation models. The practical validation of BifacialSimu is based on empirical data from three diverse geographic locations. The locations of Golden, United States; Heggelbach, Germany; and Florianópolis, Brazil, provide insights into the performance of bifacial PV systems across a range of environmental conditions and installation configurations. These findings underscore the practical applicability of BifacialSimu, with recommendations for simulation model selection and methodological advancements, paving the way for more precise and efficient bifacial PV system simulations across diverse scenarios. This study employs a number of validation metrics, including relative error, coefficient of determination and Normalized Root Mean Square Error, to assess the accuracy of the simulations. The findings indicate that the Ray tracing method is the most accurate of the irradiance simulation modes for most scenarios. The validation results highlight that the Ray Tracing method achieves superior accuracy in irradiance simulations, particularly under varied environmental conditions, while Variable Albedo models further enhance predictive precision by accounting for dynamic factors such as snow cover.