Atmospheric Transmittance Model Validation for CSP Tower Plants

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT’s Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km (T1km) time series is compared with a reference data set of on-site absorptionand broadband corrected T1km derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM’s T1km using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high T1km with a high accuracy and provides an estimation of the T1km for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design. Remote Sens. 2019, 11, 1083; doi:10.3390/rs11091083 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1083 2 of 18

[1]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[2]  Jesús Polo,et al.  Sensitivity study for modelling atmospheric attenuation of solar radiation with radiative transfer models and the impact in solar tower plant production , 2016 .

[3]  Didier Ramon,et al.  Sensitivity of the solar resource in solar tower plants to aerosols and water vapor , 2019, SOLARPACES 2018: International Conference on Concentrating Solar Power and Chemical Energy Systems.

[4]  Robert Pitz-Paal,et al.  Atmospheric extinction in solar Tower plants - A review , 2017 .

[5]  Detlev Heinemann,et al.  Vertical aerosol concentrations in the lowest 300m of the troposphere for solar tower plants assessment from CALIPSO satellite and ECMWF-MACC data , 2019, SOLARPACES 2018: International Conference on Concentrating Solar Power and Chemical Energy Systems.

[6]  Jesús Fernández-Reche,et al.  Solar extinction measurement system based on digital cameras. Application to solar tower plants , 2018, Renewable Energy.

[7]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[8]  Ahmed Al-Salaymeh,et al.  The enerMENA meteorological network – Solar radiation measurements in the MENA region , 2016 .

[9]  Christian A. Gueymard,et al.  Evaluation of Solar Energy Losses for the Heliostat-To-Receiver Path of a Tower Solar Plant for Different Aerosol Models , 2017 .

[10]  Jesús Fernández-Reche,et al.  Analysis of solar tower plant performance influenced by atmospheric attenuation at different temporal resolutions related to aerosol optical depth , 2017 .

[11]  Natalie Hanrieder,et al.  Determination of Atmospheric Extinction for Solar Tower Plants , 2016 .

[12]  Inmaculada Pulido-Calvo,et al.  Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks , 2018, Solar Energy.

[13]  Robert Pitz-Paal,et al.  Atmospheric extinction in solar tower plants: absorption and broadband correction for MOR measurements , 2015 .

[14]  Marcelino Sánchez,et al.  High-accuracy real-time monitoring of solar radiation attenuation in commercial solar towers , 2019, SOLARPACES 2018: International Conference on Concentrating Solar Power and Chemical Energy Systems.

[15]  A. Beljaars,et al.  Climatology of the planetary boundary layer over the continental United States and Europe , 2012 .

[16]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[17]  Laurent Dubus,et al.  Instrumental set-up to estimate the atmospheric attenuation along the slant path of concentrated solar plants , 2018 .

[18]  I. Troen,et al.  A simple model of the atmospheric boundary layer; sensitivity to surface evaporation , 1986 .

[19]  Jesús Fernández-Reche,et al.  Atmospheric extinction levels of solar radiation at Plataforma Solar de Almería. Application to solar thermal electric plants , 2018 .

[20]  Laurent Dubus,et al.  Solar energy incident at the receiver of a solar tower plant, derived from remote sensing: Computation of both DNI and slant path transmittance , 2017 .

[21]  Jesús Fernández-Reche,et al.  Diagnosis of a Lambertian target in solar context , 2018 .

[22]  Manajit Sengupta,et al.  Estimating Atmospheric Attenuation in Central Receiver Systems , 2012 .

[23]  Arve Kylling,et al.  The libRadtran software package for radiative transfer calculations (version 2.0.1) , 2015 .

[24]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[25]  Manajit Sengupta,et al.  Impact of Aerosols on Atmospheric Attenuation Loss in Central Receiver Systems , 2011 .

[26]  Albert Ansmann,et al.  LIVAS: a 3-D multi-wavelength aerosol/cloud climatology based on CALIPSO and EARLINET , 2015 .

[27]  Jesús Fernández-Reche,et al.  Evolution of the aerosol extinction coefficient at 100 m above ground during an episode of Saharan dust intrusion as derived from data registered by a ceilometer in Almería (SE Spain) , 2018 .

[28]  Christian A. Gueymard,et al.  Atmospheric transmission loss in mirror-to-tower slant ranges due to water vapor , 2017 .

[29]  Jesús Fernández-Reche,et al.  Modelling atmospheric attenuation at different AOD time-scales in yield performance of solar tower plants , 2018 .

[30]  M. Sengupta,et al.  Atmospheric Attenuation in Central Receiver Systems from DNI Measurements , 2012 .

[31]  Florian Wiesinger,et al.  Atmospheric extinction in CSP tower plants in Morocco and Spain , 2017 .

[32]  Robert Pitz-Paal,et al.  Modeling beam attenuation in solar tower plants using common DNI measurements , 2016 .

[33]  Bernhard Mayer,et al.  Atmospheric Chemistry and Physics Technical Note: the Libradtran Software Package for Radiative Transfer Calculations – Description and Examples of Use , 2022 .