The near real-time solar irradiance mapping in California based on satellite data and economic and emission benefits analysis
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The near real-time solar irradiance mapping in California based on satellite data and economic and emission benefits analysis

  • Author(s): Liu, Honglei
  • Advisor(s): Guo, Qinghua
  • et al.
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Abstract

As the most abundant, sustainable, and green energy source on the earth, solar energy has the potential to resolve environmental problems such as climate change and air pollution caused by fossil energy. Real-time solar irradiance mapping, which gives the real-time data on local solar energy distribution, would provide valuable information and lead to more efficient use of solar energy. State of California (CA) is abundant in solar energy. However, the data of real-time direct normal irradiance (DNI) in CA is still currently not available. This thesis focuses on developing-models that would use satellite data to map near real-time solar irradiance in CA and analyzing the potential economic and emission benefits of solar installation based on the modeled DNI data. The transmittance data was derived from GOES-WEST for the development of ON I model. The study showed that the major atmospheric factors influencing the solar irradiance are cloud coverage and aerosol concentration in non-clear and clear sky condition, respectively. This suggests that the transmittances of cloud and aerosol should be as accurate as possible when calculating DNI using satellite data. Next, the modeled DNI was compared with actual DNI data, and the results indicated a good agreement between them, implying that the modeled DNI data is a good estimate of the actual solar irradiance. Finally, the economic and emission benefits of solar use were analyzed based upon process-modeling and empirical regression approach. The results indicated that 0.2% coverage of solar PV in CA could reduce the C02, NOx and S02 emissions on average by 9.08%, 4.51 % and 2.73% during 2007 summertime, respectively. The cost savings are amount to 48.7%, 32.0% and 16.9% in summer under three levels of PV installation rate. In summary, we developed a model to map near real-time DNI spatial and temporal distribution based on satellite data. The outcomes from solar mapping are used to predict potential economic and emission benefits using empirical regression and process-based approach.

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