Assessment of solar radiation components in Brazil using the BRL model

Quality data regarding direct and diffuse solar irradiation is crucial for the proper design and simulation of solar systems. This information, however, is not available for the entire Brazilian territory. However, hourly measurements of global irradiation for more than seven hundred stations over the territory are available. Several mathematical models have been developed over the past few decades aiming to deliver estimations of solar irradiation components when only measurement of global irradiation is available. In order to provide reliable estimates of diffuse and direct radiation in Brazil, the recently presented Boland–Ridley–Laurent (BRL) model is adjusted to the particular features of Brazilian climate data, developing adjusted BRL models on minute and hourly bases. The model is adjusted using global, diffuse and direct solar irradiation measurements at nine stations, which are maintained by INPE in the frame of the SONDA project. The methodology for processing and analyzing the quality of the data-sets and the procedures to build the adjusted BRL model is thoroughly described. The error indicators show that the adjusted BRL model performs better or similarly to the original one, for both diffuse and DNI estimates calculated for each analyzed Brazilian station. For instance, the original BRL model diffuse fraction estimates have MeAPE errors ranging from 16% to 51%, while the adjusted BRL model gives errors from 9% to 26%. Regarding the comparison between the minute and hourly adjusted models, it can be concluded that both performed similarly, indicating that the logistic behavior of the original BRL model is well suited to make estimates in sub-hourly data sets. Based on the results, the proposed adjusted model can be used to provide reliable estimates of the distribution of direct and diffuse irradiation, and therefore, can help to properly design and reduce the risks associated to solar energy systems.

[1]  Alexander Berk,et al.  MODTRAN6: a major upgrade of the MODTRAN radiative transfer code , 2014, Defense + Security Symposium.

[2]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[3]  Ricardo Rüther,et al.  Atlas Brasileiro de Energia Solar , 2017 .

[4]  Tariq Muneer,et al.  Quality control of solar radiation data: Present status and proposed new approaches , 2005 .

[5]  J. Orgill,et al.  Correlation equation for hourly diffuse radiation on a horizontal surface , 1976 .

[6]  Miroslav Kocifaj,et al.  Angular distribution of scattered radiation under broken cloud arrays: An approximation of successive orders of scattering , 2012 .

[7]  P. Ineichen,et al.  A new operational model for satellite-derived irradiances: description and validation , 2002 .

[8]  J. Olseth,et al.  An hourly diffuse fraction model with correction for variability and surface albedo , 1998 .

[9]  Michel Journée,et al.  Quality control of solar radiation data within the RMIB solar measurements network , 2010 .

[10]  W. Beckman,et al.  Diffuse fraction correlations , 1990 .

[11]  J. A. Ruiz-Arias,et al.  Performance of Separation Models to Predict Direct Irradiance at High Frequency: Validation over Arid Areas , 2015 .

[12]  P. Ineichen A broadband simplified version of the Solis clear sky model , 2008 .

[13]  P. Ineichen Validation of models that estimate the clear sky global and beam solar irradiance , 2016 .

[14]  Michael Geiger,et al.  A Web service for controlling the quality of measurements of global solar irradiation , 2002 .

[15]  Nicholas A. Engerer Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia , 2015 .

[16]  Christoph Schillings,et al.  Solar and Wind Energy Resource Assessment (SWERA) , 2004 .

[17]  L. Ramírez,et al.  Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations , 2009 .

[18]  Benjamin Y. H. Liu,et al.  The interrelationship and characteristic distribution of direct, diffuse and total solar radiation , 1960 .

[19]  Miroslav Kocifaj,et al.  Unified model of radiance patterns under arbitrary sky conditions , 2015 .

[20]  Miroslav Kocifaj,et al.  Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation , 2016 .

[21]  J. Boland,et al.  Decomposing global solar radiation into its direct and diffuse components , 2013 .

[22]  J. Duffie,et al.  Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation , 1982 .

[23]  J. A. Ruiz-Arias,et al.  Extensive worldwide validation and climate sensitivity analysis of direct irradiance predictions from 1-min global irradiance , 2016 .

[24]  C. Gueymard Parameterized transmittance model for direct beam and circumsolar spectral irradiance , 2001 .

[25]  John Boland,et al.  Models of diffuse solar radiation , 2008 .

[26]  R. Inman,et al.  Cloud enhancement of global horizontal irradiance in California and Hawaii , 2016 .

[27]  E. Maxwell A quasi-physical model for converting hourly global horizontal to direct normal insolation , 1987 .

[28]  S. L. Abreu,et al.  Solar energy scenarios in Brazil, Part one: Resource assessment , 2008 .

[29]  John Boland,et al.  Modelling of diffuse solar fraction with multiple predictors , 2010 .

[30]  J. Boland,et al.  Models of Diffuse Solar Fraction , 2008 .

[31]  Cn Long,et al.  The QCRad Value Added Product: Surface Radiation Measurement Quality Control Testing, Including Climatology Configurable Limits , 2006 .