Análise multivariada do impacto das variáveis meteorológicas locais no desempenho da geração de energia elétrica de um sistema fotovoltaico conectado à rede

Abstract

This experiment aimed to analyze the behavior of atmospheric variables and the temperature of photovoltaic modules, along with the efficiency and energy generation of a photovoltaic system installed at the State University of Western Paraná - UNIOESTE, Cascavel Campus, Paraná. The system is composed of four types of photovoltaic modules, 18 modules of each model, totaling 72 modules with 24.3 kWp of power. To obtain the atmospheric variables, different sensors from a weather station installed near the analyzed photovoltaic system were used. The equipment provides data on wind speed through an anemometer, relative humidity and ambient temperature through a hygrometer, solar irradiation through a pyranometer inclined on the same plane as the photovoltaic modules, and finally, rainfall through a rain gauge. For reading and storing the sensors' data, a CR310 datalogger from Campbell Scientific was used. The module temperature values were obtained through thermocouples installed on the photovoltaic modules and connected to a CR1000 datalogger from Campbell Scientific. The energy generation data were obtained from the manufacturer's website. The variables were analyzed in four periods, corresponding to the seasons of the year: winter, spring, summer, and autumn, with 31 days for each interval and 124 days in total. The season that produces the most energy is summer; however, the season that shows the highest energy efficiency is spring. Solar irradiation, energy generation, and module temperatures are the variables that most affect the efficiency of a photovoltaic system. Thus, energy efficiency presents a negative correlation with these variables, a characteristic that was repeated for all the studied modules. The other variables do not show significant correlation with efficiency, except for humidity, which shows a slight positive correlation. Module temperature shows a strong positive correlation with energy generation, solar irradiation, and ambient temperature for all modules. Energy generation shows a strong positive correlation with solar irradiation, the highest correlation value among the variables. The higher the module temperature, the lower the energy efficiency of the system. On the other hand, module temperature is strongly correlated with energy generation, indicating that higher temperatures are associated with higher energy generation, but not with higher efficiency. Efficiency could be better utilized if some instrument or technique were used to reduce the temperature of the modules at peak energy generation times. The Jinko monocristalline modules stood out with the highest average efficiency for the periods, followed by the Jinko polycrystalline modules, BYD monocristalline, and BYD polycrystalline modules.