Integration of Soiling-Rate Measurements and Cleaning Strategies in Yield Analysis of Parabolic Trough Plants

Abstract

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.