Analysis of the cloud cover variability using geostationary satellite data

• Ano de defesa: 2018
• Autor(a) principal: Eduardo Weide Luiz
• Orientador(a): Enio Bueno Pereira, Fernando Ramos Martins
• Banca de defesa: Simone Marilene Sievert da Costa Coelho, Arcilan Trevenzoli Assireu, Samuel Luna de Abreu
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Nacional de Pesquisas Espaciais (INPE)
• Programa de Pós-Graduação: Programa de Pós-Graduação do INPE em Ciência do Sistema Terrestre
• País: BR
• Link de acesso: http://urlib.net/sid.inpe.br/mtc-m21c/2018/03.22.18.21

One of the main barriers to increase the solar energy share is its intermittency. Solar energy has a large variability in different time-scales driven by the solar astronomical cycles and by weather. Ground-based measurements are important to evaluate the variability at high resolutions, but they are only representative of small areas close to the measurement sites. Satellite observations come as a solution for the analysis over large areas, however they have coarse temporal and spatial resolutions. The main objective of this thesis is to develop a methodology for the characterization of the variability of the solar resource, focusing on the cloud effects. This simple methodology will allow to evaluate the variability of the solar power generation over large areas, using only data of geostationary satellite images, with no need of ground data. First, we compared the cloud cover fraction obtained through a satellite-based methodology with sitespecific data from all-sky cameras. This comparison presented a Pearson correlation of 0.9. In addition, we evaluated the similarity between the cumulative distributions functions of both datasets using the Kolmogorov-Smirnov test and the results pointed out for statistically significant similarity between them, even though their time resolutions were different. Then, we examined the variability of the global horizontal irradiance ramp rates from ground-based radiometers and compared it with the satellite cloud cover variability in 3 different Brazilian climate regimes. The results showed that the driest periods have lower solar irradiance variability. However, this result is not necessarily valid for different climate regimes. For instance, Petrolina, the driest place, exhibited the higher variability for shorter timescales, probably due to the rapid passage of small clouds shadowing the sun. When comparing the variability of the satellite cloud cover with that of the solar irradiance, the Pearson correlation reached up to 0.93, depending on the site, for the same time resolution (30 minutes). However, considering smaller time steps for solar irradiance ramps, the correlation decreased to values lower than 0.66 in all sites. The proposed methodology has broad application in the planning and management of solar power generation in countries with large territorial extension, such as Brazil.