Probabilistic assessment of concentrated solar power plants yield: The EVA methodology

Probabilistic assessment of concentrated solar power plants yield: The EVA methodology Understanding the long-term temporal variability of solar resource is fundamental in any assessment of solar energy potential. The variability of the solar resource (as shown by historical solar data) plays a significant role in the statistical description of the future performance of a solar power plant, thus influencing its financing conditions. In particular, solar-power financing is mainly based on a statistical quantification of the solar resource. In this work, a methodology for generating meteorological years representative of a given annual probability of exceedance of solar irradiation is presented, which can be used as input in risk assessment for securing competitive financing for Concentrating Solar Thermal Power (CSTP) projects. This methodology, which has been named EVA, is based on the variability and seasonality of monthly Direct Normal solar Irradiation (DNI) values and uses as boundary condition the annual DNI value representative for a given probability of exceedance. The results are validated against a 34-year series of net energy yield calculated at hourly intervals from measured solar irradiance data and meteorological, and they are also supplemented with the analysis of uncertainty associated to the probabilities of exceedance estimates. Relations between DNI and CSTP energy yields at different time scales are also analyzed and discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy Elsevier

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
1386-1425
D.O.I.
10.1016/j.rser.2018.03.018
Publisher site
See Article on Publisher Site

Abstract

Understanding the long-term temporal variability of solar resource is fundamental in any assessment of solar energy potential. The variability of the solar resource (as shown by historical solar data) plays a significant role in the statistical description of the future performance of a solar power plant, thus influencing its financing conditions. In particular, solar-power financing is mainly based on a statistical quantification of the solar resource. In this work, a methodology for generating meteorological years representative of a given annual probability of exceedance of solar irradiation is presented, which can be used as input in risk assessment for securing competitive financing for Concentrating Solar Thermal Power (CSTP) projects. This methodology, which has been named EVA, is based on the variability and seasonality of monthly Direct Normal solar Irradiation (DNI) values and uses as boundary condition the annual DNI value representative for a given probability of exceedance. The results are validated against a 34-year series of net energy yield calculated at hourly intervals from measured solar irradiance data and meteorological, and they are also supplemented with the analysis of uncertainty associated to the probabilities of exceedance estimates. Relations between DNI and CSTP energy yields at different time scales are also analyzed and discussed.

Journal

Spectrochimica Acta Part A: Molecular and Biomolecular SpectroscopyElsevier

Published: Sep 5, 2018

References

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