Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling

Poor resolution of solar irradiance ground data demonstrates the necessity and provides an opportunity for satellite data-based solar irradiance modeling. The study is focused on India due to its tropical climate that provides varied as well as extreme conditions for solar energy research. For solar energy monitoring in near real-time, the Indian Solar Irradiance Operational System (INSIOS) was developed using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate, respectively. It had high accuracy under clear-sky conditions for global horizontal irradiance (GHI) and direct normal irradiance (DNI) that were evaluated for a year at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The presented methodology could effectively support the penetration of photovoltaic installation as estimations were reliable during high solar energy potential conditions with median BSRN and INSIOS difference varying from 93 to 49 W/m² for GHI.

Further, an operational day-ahead solar irradiance forecasting system (WRF-CAMS) is presented that ingests CAMS aerosol optical depth (AOD) into the WRF model to better quantify the aerosol impact on solar energy long-term forecasts, and validation was done against ground-based measurements from BSRN stations. The analysis was carried out for forecast horizons varying from 24 h to 36 h for different seasons, varying solar zenith angles, and different cloud cover classifications based on calculated clearness index. The correlation coefficient was improved from 0.93 to 0.95 for GHI and 0.75 to 0.82 for DNI after the ingestion of CAMS AOD as compared to WRF default aerosol scheme. The annual root mean square error was observed to vary from 10 to 130 W/m² and 50 to 190 W/m² for GHI and DNI, respectively. This system is hoped to provide more accurate forecasts for better solar plant energy planning and improve day-to-day electricity exchange market supporting solar energy producers and distribution system operators.

In the final analysis, INSIOS and WRF-CAMS models were used for forecasting dust impact on solar irradiance during an extreme dust event using Aeronet measurements, satellite observations (MODIS and CALIPSO), and ModIs Dust AeroSol (MIDAS) dust database. WRF-CAMS model was used to examine dust impact on solar irradiance for a high-intensity dust storm with AOD and dust optical depth values reaching up to 2. The observed average decrease in GHI and DNI due to the dust plume was 76 W/m² and 275 W/m², respectively, and a maximum reduction of 100 W/m² (10%) and 400 W/m² (40%), respectively. The proposed methodology can support solar energy producers, for optimum energy production forecasting, management, and maintenance as well as transmission and distribution system operators, as dust events of this extent significantly reduce solar irradiance and affect energy exploitation capacity due to solar aerosol-related extinction.