Variability and Sensitivity of Models Used to Estimate Photovoltaic Production

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ( P p v ). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the P p v estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence P p v , the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in P p v between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m 2 . The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences.