Results of Analysis of the Applicability of Satellite Observations and Reanalysis Data for Simulations of Autonomous Solar Power Systems
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An accuracy of predicting energy performance of autonomous photovoltaic systems using various climatic databases (NASA POWER, SARAH-E, CLARA-A, ERA5, Meteonorm, etc.) has been analyzed for some geographic points in Russia by comparison with calculations based on data from the World Radiation Data Center. It has been showed that the databases considered provide a spread of predictions for required rated solar battery power at a level of 10–20% only when solar fraction is less than 70%. For a larger solar fraction prediction error of required rated solar battery power can reach several hundred percent.
This work was supported by the Joint Institute for High Temperatures of the Russian Academy of Sciences (State assignment).
- 1.V. E. Fortov and O. S. Popel, Renewable Energetics in the Modern World (MEI, Moscow, 2015) [in Russian].Google Scholar
- 2.WRDC. Accessible at http://wrdc.mgo.rssi.ru/wwwrootnew/wrdc_ru_new.htm. The link was active on June 18, 2019.Google Scholar
- 3.NASA Prediction of Worldwide Energy Resources. Accessible at https://power.larc.nasa.gov/. The link was active on June 18, 2019.Google Scholar
- 4.CM SAF—Product navigator. Accessible at https://wui.cmsaf.eu/safira/action/viewProduktSearch. The link was active on June 18, 2019.Google Scholar
- 5.Copernicus. ERA5 hourly data on single levels from 1979 to present. Accessible at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. The link was active on June 18, 2019.Google Scholar
- 8.O. S. Popel, S. E. Frid, Yu. G. Kolomiets, et al., An Atlas of Solar Energy Resources on the Territory of Russia (MFTI, Moscow, 2010) [in Russian].Google Scholar
- 10.Meteonorm. Irradiation Data for Every Place on Earth. Accessible at https://meteonorm.com/en/. The link was active on June 18, 2019.Google Scholar