The issue of reflector soiling becomes more important as concentrating solar thermal power plants (CSP) are being implemented at sites subject to high dust loads. In an operational power plant, a trade-off between reducing cleaning costs and cleaning related collector availability on the one hand and keeping the solar field cleanliness (ξfield) high to minimize soiling induced losses on the other hand must be found. The common yield analysis software packages system advisor model (SAM) and greenius only allow the input of a constant mean ξfield and constant cleaning costs. This oversimplifies real conditions because soiling is a highly time-dependent parameter and operators might adjust cleaning activities depending on factors such as soiling rate and irradiance. In this study, time-dependent soiling and cleaning data are used for modeling the yield of two parabolic trough plant configurations at two sites in Spain and Morocco. We apply a one-year soiling rate dataset in daily resolution measured with the tracking cleanliness sensor (TraCS). We use this as a basis to model the daily evolution of the cleanliness of each collector of a solar field resulting from the application of various cleaning strategies (CS). The thus obtained daily average ξfield is used to modify the inputs to the yield analysis software greenius. The cleaning costs for each CS are subtracted from the project's financial output parameters to accurately predict the yield of a CSP project over its lifetime. The profits obtained with different CSs are compared in a parameter variation analysis for two sites and the economically best CS is identified. The profit can be increased by more than 2.6% by the application of the best strategy relative to a reference strategy that uses a constant cleaning frequency. The error in profit calculated with constant soiling and cleaning parameters compared to the simulation with variable soiling and cleaning can be as high as 9.4%. With the presented method, temporally variable soiling rates and CS can be fully integrated to CSP yield analysis software, significantly increasing its accuracy. It can be used to determine optimum cleaning parameters.

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 ( $T_{1km}$ ) time series is compared with a reference data set of on-site absorption- and broadband corrected $T_{1km}$ 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 $T_{1km}$ 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 $T_{1km}$ with a high accuracy and provides an estimation of the $T_{1km}$ 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.

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model 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 was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature ($T_{air}$), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.