A methodology for calculating percentile values of annual direct normal solar irradiation series

A detailed knowledge of the solar resource is a critical point in the performance of an economic feasibility analysis of solar thermal electricity plants. In particular, the Direct Normal solar Irradiance (DNI) is the most determining variable in its final energy yield. Inter-annual variations of DNI can be large and seriously compromise the viability of solar energy projects. In this work, a methodology for evaluating the statistical properties of annual DNI series is presented for generating inputs to risk assessments in an economic feasibility analysis of a solar power plant. The methodology relies on the construction of a cumulative distribution function of annual DNI values, which allows for the evaluation of both mean and extreme climate characterization at a particular location in the long term.

[1]  N. Singpurwalla,et al.  Kolmogorov Statistics for Tests of Fit for the Extreme-Value and Weibull Distributions , 1981 .

[2]  C. F. Peruchena,et al.  A New Methodology to Generate Long Time Series of Solar Radiation Based on Stochastic Analysis , 2014 .

[3]  Tomas Cebecauer,et al.  Typical Meteorological Year Data: SolarGIS Approach☆ , 2015 .

[4]  A. Bernardos,et al.  Generation of Series of High Frequency DNI Years Consistent with Annual and Monthly Long-term Averages using Measured DNI Data☆ , 2014 .

[5]  Carlos M. Fernández-Peruchena,et al.  MUS: A multiscale stochastic model for generating plausible meteorological years designed for multiyear solar energy yield simulations , 2015 .

[6]  Stefan Wilbert,et al.  Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications , 2015 .

[7]  G. Marsaglia,et al.  Evaluating Kolmogorov's distribution , 2003 .

[8]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[9]  D. Darling.  The Kolmogorov-Smirnov, Cramer-von Mises Tests , 1957 .

[10]  Gregory J. Kolb,et al.  Incorporating Uncertainty into Probabilistic Performance Models of Concentrating Solar Power Plants , 2010 .

[11]  Carlos M. Fernández-Peruchena,et al.  Increasing the temporal resolution of direct normal solar irradiance series in different climatic zones , 2015 .

[12]  Aron Dobos,et al.  P50/P90 Analysis for Solar Energy Systems Using the System Advisor Model: Preprint , 2012 .

[13]  R. Meyer,et al.  Calculation of CSP Yields with Probabilistic Meteorological Data Sets: A Case Study in Brazil☆ , 2015 .

[14]  José A. Villaseñor-Alva,et al.  A Goodness-of-Fit Test for the Gumbel Distribution Based on Kullback–Leibler Information , 2009 .

[15]  Frank Vignola,et al.  Chapter 5 – Bankable Solar-Radiation Datasets , 2013 .

[16]  Christoph Schillings,et al.  Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data , 2006 .

[17]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[18]  Min Liao,et al.  A new goodness-of-fit test for type-I extreme-value and 2-parameter weibull distributions with estimated parameters , 1999 .