Assessment of a Global-to-Direct empirical model for the long-term characterization of Direct Normal Insolation

The statistical characterization of the solar resource (direct normal solar radiation) is a key point in the initial phases of a solar thermal electricity (STE) plant project. Ideally, this characterization should be based on long time series (at least 8 years) of on-site measured data of Direct Normal Insolation (DNI) and other meteorological parameters. Unfortunately, there are very few places around the world where such time series are available, so alternative methods have to be used. Most of them rely on the application of global-to-direct conversion models to long time series of Global Horizontal Insolation (GHI), measured or derived from satellite images, to estimate the long-term resource. Usually, a meteorological station including sensors for the measurement of DNI is installed at the selected project site at the beginning of the project. The data collected during the measurement campaign, which normally extends between a few months and 2 years, are used to adjust the conversion models and to correct the estimates. In this paper, a simple empirical model that relates monthly clearness index and monthly direct normal fraction is used to estimate monthly and annual long-term DNI from statistically representative monthly values of GHI. This model is adjusted with GHI and DNI data collected during measurement campaigns of different durations. We show that the accuracy of the proposed model is under +-5% and that this accuracy improves sharply with the duration of the test campaign. For this purpose, we have used 13 years of high quality DNI and GHI data from the radiometric station of the Group of Thermodynamics and Renewable Energies (GTER) of the University of Seville, Spain. The results suggest that, this simple empirical model is a good alternative to the present methodologies when short DNI measurement campaign but long-term GHI values are available