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Statistical analysis of CSP plants by simulating extensive meteorological series

The feasibility analysis of any power plant project needs the estimation of the amount of energy it will be able to deliver to the grid during its lifetime. To achieve this, its feasibility study requires a precise knowledge of the solar resource over a long term period. In Concentrating Solar Power...

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Main Authors: Pavón, Manuel, Fernández, Carlos M., Silva, Manuel, Moreno, Sara, Guisado, María V., Bernardos, Ana
Format: Conference Proceeding
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Fernández, Carlos M.
Silva, Manuel
Moreno, Sara
Guisado, María V.
Bernardos, Ana
description The feasibility analysis of any power plant project needs the estimation of the amount of energy it will be able to deliver to the grid during its lifetime. To achieve this, its feasibility study requires a precise knowledge of the solar resource over a long term period. In Concentrating Solar Power projects (CSP), financing institutions typically requires several statistical probability of exceedance scenarios of the expected electric energy output. Currently, the industry assumes a correlation between probabilities of exceedance of annual Direct Normal Irradiance (DNI) and energy yield. In this work, this assumption is tested by the simulation of the energy yield of CSP plants using as input a 34-year series of measured meteorological parameters and solar irradiance. The results of this work show that, even if some correspondence between the probabilities of exceedance of annual DNI values and energy yields is found, the intra-annual distribution of DNI may significantly affect this correlation. This result highlights the need of standardized procedures for the elaboration of representative DNI time series representative of a given probability of exceedance of annual DNI.
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Feasibility studies
Irradiance
Meteorological parameters
Statistical analysis
title Statistical analysis of CSP plants by simulating extensive meteorological series
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