Nov 28, 2018
Published in: Center for Study of Science, Technology & Policy (2019)
Posted on RAND.org on January 09, 2019
India plans to add 100 GW of solar electric power generation by 2022 to an existing system (with an installed capacity of close to 330 GW, as on January 2018, from all sources). Selection of sites for such a large infrastructure investment is definitely an important decision. Here, we examine the performance of four models of annual solar photovoltaic production that are appropriate for site selection against actual generation data from utility-scale plants in the Indian state of Gujarat. We find that a simple Bird clear sky index predicts the annual PV plant production with an error of 14±5%, while the satellite data (at 10 km x 10 km resolution) in National Renewable Energy Laboratory's National Solar Radiation Database (NSRDB) has a prediction error of 9 ±6%. Data from solar monitors at India's Solar Radiation Resource Assessment (SRRA) stations, which are 46-95 km away from the power plants, has a prediction error of 7 ±3%. However, a power plant model using SRRA weather data to predict output correction for solar cell temperature performs best with an error of 6 ±6%. The inter-annual variability of the annual mean irradiation for 2000–2014 in Gujarat is ±3.6% for direct normal irradiance (DNI). Thus, we find that for site selection based on annual PV production, the inter-annual variability of irradiance is larger than the differences between models; in the absence of coincident data, satellite data (NSRDB) could be a preferred choice.