A multi-model benchmarking of direct and global clear-sky solar irradiance predictions at arid sites using a reference physical radiative transfer model

被引:23
|
作者
Ruiz-Arias, Jose A. [1 ,2 ]
Gueymar, Christian A. [3 ]
机构
[1] Univ Malaga, Dept Appl Phys 1, Malaga 29071, Spain
[2] Solargis SrO, Mytna 48, Bratislava 81107, Slovakia
[3] Solar Consulting Serv, POB 392, Colebrook, NH 03576 USA
关键词
Clear sky; Solar irradiance; Reanalysis; Radiative transfer; LIBRADTRAN SOFTWARE PACKAGE; PERFORMANCE ASSESSMENT; GROUND-LEVEL; SURFACE; VALIDATION; TURBIDITY; DATASET; BEAM; TRANSMITTANCE; CLOSURE;
D O I
10.1016/j.solener.2018.06.048
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This study provides a comparison of the predictions of fifteen clear-sky irradiance models against those from the RRTMG radiative transfer. RRTMG is selected as the benchmarking reference here because its code is open source and features a good compromise between accuracy, ease of use, and speed of execution, which should guarantee reproducible results by a majority of solar analysts. The model comparisons are undertaken at ten worldwide sites of importance for concentrating solar power (CSP) projects, and at a highly turbid desert site. The models' inputs are directly or indirectly obtained from worldwide reanalyses having a spatial resolution of 1.125 degrees x 1.125 degrees. The simulations of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are done hourly over a whole year (2012). The present results confirm a previous validation study to the effect that significant inter-model disparities exist at arid sites. Large aerosol optical depth (AOD) and high site elevation appear to be the main drivers behind the disagreements. The comparison between four different Linke turbidity coefficient parameterizations shows that none of them provide good results for both GHI and DNI at all sites. Linke-based irradiance models are thus not recommended for accurate solar resource assessments. Considering all ten CSP sites combined, GHI predictions are found within +/- 5% of the benchmark for nine models. Only five models have the same success in the case of DNI. Finally, only two models, REST2v5 and SMARTS, are close to the benchmark for both GHI and DNI.
引用
收藏
页码:447 / 465
页数:19
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