SOLAR RESOURCE ASSESSMENT AND FORECASTING USING SATELLITE DATA AND NUMERICAL WEATHER PREDICTION

Abstract

For a tropical country like India, which is potentially rich in solar energy resource, the study of the atmospheric parameters is of crucial importance from the perspective of solar energy. Furthermore, a complete utilization of solar energy depends on its proper integration with power grids. Because of its variable nature, incorporation of photovoltaic energy into electricity grid suffers technical challenges. Solar forecasting is one of the enabling technologies to sustain the growing penetration of weather-dependent solar-based power generation into the electric grid. Accurate forecasts are needed to deal with the operational costs associated with intra-day variability, utility costs associated with day ahead scheduling (thereby reducing overall operations and maintenance costs), imbalance charges incurred by plant operators due to inaccurate energy bids, as well as assisting the grid operators with balancing energy demand schedules. In this work, methods are developed to provide accurate solar forecasts for different forecast horizons based on the necessity of the grid operators. We have developed real-time, intra-day (< 6 h) and day ahead (>24 h) forecasting methods to directly predict the generation of operational solar power plants, without the need for intermediate solar irradiance forecasts and resource to-power modeling. Solar radiation ground data is available in poor spatial resolution, which provides an op portunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. Satellite data across various spectral bands may be employed to distinguish weather signatures, such as: dust, aerosols, fog and clouds. Solar radiation is subjected to reflection, scattering and absorption by air molecules, clouds and aerosols in the atmo sphere. Clouds can block most of the direct radiation. Modern solar energy forecasting systems relies on real-time Earth Observation from satellite for clouds and aerosols. For the real-time assessment of solar irradiance, an Indian Solar Irradiance Operational System (INSIOS) was developed using Copernicus Atmosphere Monitoring Service (CAMS) Monitoring Atmospheric Composition Climate (MACC). Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky condition for GHI and DNI. DNI was very sensitive to the presence of aerosols, and thus it affected the accuracy of simulations under realistic at mospheric conditions. The median BSRN and INSIOS difference was found to vary from-93 to-49 W/m2 for GHI and-103 to-76 W/m2 for DNI under high solar energy poten tial conditions. For various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. Next, we implemented the INSIOS model to estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of aerosols on the PV energy production based on Earth Observation (EO) related techniques and solar resource modeling. The proposed methodology employs CAMS 1-day forecasts of aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and f inancial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly and seasonal time horizons and the correlation was found to be 0.82 with most of the per centage difference evaluated with the BSRN measurements as the reference being within 25% for clear-sky conditions. The proposed methodology is operational ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations. Moving on to solar forecasting, an assessment of the impact of dust and forecasting solar irradiance and energy was made during an extreme dust event. We utilized the AERONET measurements and the MODIS and CALIPSO satellite observations and the MIDAS database in conjunction with Weather Research and Forecast (WRF) model, sim ulations based on Indian Solar Irradiance Operational System (INSIOS) and Copernicus Atmosphere Monitoring Service (CAMS) forecasts. The study region for this analysis is the northwestern semi-arid region of the Indian subcontinent, where three consecutive and deadly dust storms occurred in 2018. The same area also houses some of the largest solar power projects in India. The intensity of one of the dust storms that occurred at the beginning of May was found to be high, with the values of aerosol optical depth (AOD) and dust optical depth (DOD) reaching up to 2. The dust events like this lead to a significant reduction in solar irradiance and affect the capacity of energy exploitation through PV installations due to high intensity of aerosol loading. The dust plume re sulted in an average decrease of 76 W/m2 and 275 W/m2 for GHI and DNI, respectively. The maximum reduction as obtained from with and without aerosols inputs to the WRF model was found up to around 100 W/m2 (10%) and 400 W/m2 (40%) in GHI and DNI, respectively, was observed. The further analysis deals with the day-ahead predictions of solar energy and their valida tion against the ground-based measurements from the Baseline Surface Radiation Network (BSRN) for solar resource measurements in India. The operational day-ahead solar irra diance forecasts are provided by the mesoscale Weather Research and Forecasting (WRF) model. A novel approach was introduced of Copernicus Atmosphere Monitoring Service (CAMS) Aerosol Optical Depth (AOD) ingestion into the WRF model to better quantify the aerosol impact on the solar energy long-term forecasts. The analysis was carried out for India’s BSRN locations for forecast horizons varying from 24 h to 36 h for different sea sons of the year. Further, we varied the solar zenith angles, the prognostic energy potential of the area, and different cloud cover classifications based on the calculated clearness in dex (kt). The day-ahead forecasts are crucial for unit commitment, reserve requirement, maintenance, and scheduling purposes. The correlation coefficient was observed to be 0.95 for Global Horizontal Irradiance (GHI) and 0.82 for Direct Normal Irradiance (DNI) after the ingestion of CAMS AOD. The annual root mean square error (RMSE) was observed to vary from 10 to 130 W/m2 and 50 to 190 W/m2 for GHI and DNI, respectively, and the RMSE was found to be minimum in winter and maximum in monsoon seasons in general. The total monthly energy potential was found to be in the range of 40 kWh/m2 to 90 kWh/m2 for GHI and 25 kWh/m2 to 60 kWh/m2 for DNI. However, the mean monthly forecast error percentage normalised with respect to the BRSN ground measurements was found to be persistently within 20% for GHI and went up to 90% for DNI. The proposed methodology is hoped to provide more accurate forecasts for better solar plant energy planning and improve the day-to-day electricity exchange market supporting the solar energy producers and the distribution system operators.