Impact of temporal and spatial variability of solar resource on technical sizing of isolated solar installations in Senegal using satellite data

The use of visible images from geostationary satellites such as Meteosat makes it possible to observe practically the entire surface of the earth almost instantaneously. This study aims to analyze for the first time the spatial and temporal variability of solar resource at different scales (daily, interannual and seasonal) in Senegal. Senegal is a climatologically interesting area because of its heterogeneity in terms of climate, from desert type (Sahelian) in the north to tropical type in the south, its sea–land interface and also its north–south gradient in precipitation. It is therefore interesting to map the solar resource to identify the different zone (zoning) in terms of solar potential. We will then suggest a model to size a 100% solar microgrid for electricity production. A case study has been applied with this model for a village that has a requirement of 134 kWh/d. In this paper, GHI (Global Horizontal Irradiance) estimates obtained from satellite imagery allowed us to evaluate the solar resource and its daily, interannual and seasonal variability. The Helioclim-3 V5 (HC-3) database is used over a 14-year period (2006–2019). This database allows to estimate the solar resource components at any point of the globe and for longer time periods. HC-3 has a very good spatial resolution (about 3 km) and a high temporal resolution (15 min). This method takes into account the relief and integrates clouds, water vapors and aerosols. The results to fully cover the needs of this 100% solar village mini-grid, there is a variation of the peak power of 20.20% between the maximum and minimum values over the year and 46.56% over the seasons. Coefficient of variation (COV) maps expressed as a percentage also show a low dispersion of the solar resource over the study period. The COV shows a narrow distribution with a minimum value of 6% and a maximum value of 9% varying from area to area and season to season. To see how a data compares to others, we will see the concept of percentiles. The P5, P10, P50 and P90 percentiles are calculated and compared to the GHI average from 2006 to 2019. The results show that P10 and P90 are closer to the GHI average than P5 and P50.