Impact of Different Solar Radiation Databases on Techno-economics of Concentrating Solar Power (CSP) Projects in Northwestern India

Bankable solar radiation data is one of the key technical barriers towards large-scale development and deployment of solar power in India. Depending on the technology (solar photovoltaic or solar thermal) the requirement of solar irradiance varies viz. the stationary solar PV works on global horizontal irradiance (GHI) and concentrating solar power (CSP) systems comprises direct normal irradiance (DNI). Till date the cumulative operational CSP projects are of around 225 MW capacities in the country out of more than 5400 MW operational grid connected solar power projects. Availability of long-term DNI data is a key challenge towards designing of a CSP project as there are no existing ground (measured) databases for long-term time series DNI in context of India. The project developers are therefore considering the satellite or interpolated databases for designing of the project. In the present study, the impact of DNI through different databases has been estimated on annual capacity utilization factor (CUF) and levelized cost of electricity (LCOE) of a parabolic trough collector (PTC) based CSP projects for two representative locations, i.e., Jodhpur (Rajasthan) and Bhuj (Gujarat) of northwestern part of India from its hot and dry climatic zone. The DNI values have been referred from ground measurements (IMD, NIWE), satellite source (NASA, NREL, and SWERA) and time series databases viz. Meteonorm (Mn) and SolarGIS for the selected locations. The conversion of all selected databases (static and dynamic) has been made in Typical Meteorological Year (TMY) in order to use in System Advisor Model (SAM) software. It has been estimated that the annual DNI variation for the selected locations has been observed from 3 to 30% (or more) with respect to estimated DNI from ground measurements of GHI and diffuse irradiance by Indian Meteorological Department (IMD). It has been estimated that the mutual variation of annual CUF in within the range of 1.0-32.0%; however in the same line the mutual deviation in LCOE (based on the benchmark cost of CERC) has been observed from 1.0 to 44.0% over the selected representative locations. The study establishes the criticality of the selection of DNI database at the design stage of the project and hence for the comprehensive project evaluation.