Análise da influência dos dados ambientais aplicáveis nas previsões de geração de energia solar fotovoltaica para Manaus

Abstract

The present dissertation addresses the performance analysis of gridconnected photovoltaic systems by calculating the expected energy from historical environmental data, the expected energy from local environmental data of the year 2021, the measured energy in the system itself in the year 2021 and respective merit indices. The performance indexes, or merit indexes, are parameters that characterize systems and express their performance in energy conversion. The study was applied to the photovoltaic system in operation at the Instituto Federal de Educação, Ciência e Tecnologia do Amazonas (IFAM), in Manaus. The problematic prioritized the evaluation of the performance of the GridConnected Photovoltaic System (SFVCR) of IFAM Campus Manaus Centro (CMC) estimated from the predicted, expected, and measured energy in the year 2021 and the influence of environmental variables applied in the predictions. The objective was to evaluate the performance of a photovoltaic system installed in Manaus from the generated and expected energy for the year 2021, contrasting the influence of environmental variables. In the methodological procedures the IEC617243 was used as reference. The real data of energy generation for the year 2021 in the SFVCR was collected and simulated, by a computationally implemented model, calculating the predicted and expected energy generation of the system. The simulated and actual energy values from the simulations based on the collected and filtered meteorological data resulted in comparative graphs and spreadsheets. The measured and simulated energy data resulted in the energy performance index of 100%, energy unavailability of 3.98%, performance ratio of 0.762, energy availability of 96%, the generated energy of 51.186 MWh and the expected energy of 51.289 MWh in 2021. The measured and expected annual productivities were 1099.31 and 1099.71 (kWh/kWp), respectively. The hourly environmental data collected, filtered, and applied to the models resulted in 0.2% inaccuracy between the generated and expected energies, indicating the accuracy of the method as a basic assessment of system performance.