Research Papers

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

[+] Author and Article Information
Fabian Wolfertstetter

German Aerospace Center (DLR),
Plataforma Solar de Almería,
Crtra Senés s/n,
Tabernas 04200, Spain
e-mail: Fabian.Wolfertstetter@dlr.de

Stefan Wilbert

German Aerospace Center (DLR),
Plataforma Solar de Almería,
Ctra Senés s/n,
Tabernas 04200, Spain
e-mail: Stefan.Wilbert@dlr.de

Jürgen Dersch

German Aerospace Center (DLR),
Linder Hoehe,
Cologne 51147, Germany
e-mail: Juergen.Dersch@dlr.de

Simon Dieckmann

German Aerospace Center (DLR),
Linder Hoehe,
Cologne 51147, Germany
e-mail: Simon.Dieckmann@dlr.de

Robert Pitz-Paal

German Aerospace Center (DLR),
Linder Hoehe,
51147 Cologne, Germany
e-mail: Robert.Pitz-Paal@dlr.de

Abdellatif Ghennioui

Institut de Recherche en Energie Solaire et
Energies Nouvelles (IRESEN),
Green Energy Park,
Km 2 Route Régionale R206,
Benguerir 43150, Morocco
e-mail: ghennioui@iresen.org

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received March 20, 2017; final manuscript received November 16, 2017; published online April 9, 2018. Assoc. Editor: Marc Röger.

J. Sol. Energy Eng 140(4), 041008 (Apr 09, 2018) (11 pages) Paper No: SOL-17-1095; doi: 10.1115/1.4039631 History: Received March 20, 2017; Revised November 16, 2017

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.

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Grahic Jump Location
Fig. 1

Soiling rate SR in daily time resolution measured with TraCS at PSA from May 2013 until May 2014. The mirror was cleaned roughly every 2 weeks. Negative values represent a decrease in cleanliness from one day to the next, and positive values are caused by rain events. One measurement value from the Feb. 16, 2014 with SR = −0.089/d is not shown for scaling reasons.

Grahic Jump Location
Fig. 3

RPI plotted against NCU for all configurations of the CS Constant. RPI compares the candidate CS to the reference strategy (Constant dn with one vehicle) for the same countries and power plants. Left for the power plant AS, right for the plant IP. dn means that cleaning is performed in one daily and one nightly shift, n means only one nightly cleaning shift.

Grahic Jump Location
Fig. 2

Overview of selected output parameters from greenius and cleaning cost calculation for plant type AS in ES and two example cleaning strategies: Constant dn (reference CS) and Threshold n with 3 CUs. Units are given in the column titles, and the dashed line separates affiliation to the two Y-scales. The scale of the three right parameters is shown on the right and that of the rest on the left. Values marked with * refer to cleaning specific costs, the calculation of those marked with ^ do not include cleaning activities.

Grahic Jump Location
Fig. 7

Simulated RPIs shown in color for the CS threshold in ES and AS, where the x-axis shows the number of CU employed and ξlim is shown on the y-axis. The soiling-rate dataset used was set to the average soiling rate of −0.0052/d of the dataset acquired at PSA for every day of the year. The depicted scenario is the same as shown in the upper left graph of Fig. 5 with the only difference that here the SR has been assumed as constant in time with the mean value of the measured SR from PSA here.

Grahic Jump Location
Fig. 4

Ratio of dumped potential thermal power Qdump and total potential thermal power Qtot plotted against NCU for both power plant types and sites for CS Constant n and dn

Grahic Jump Location
Fig. 5

Comparison of RPI for CS Threshold where color represents the RPI in %. The x-axes values represent NCU, the y-axes represent ξlim. Power plant AS is shown in the upper graphs and IP in the lower graphs. In the left column, financial values from ES have been applied and in the right column those from MOR are used. The color scale is the same in all graphs. Each color tile represents one simulation run with the exact parameter combination at the center of the tile. The step width for the parameter variation is 1 for NCU and 1/300 for ξlim. The contour lines are shown as an orientation. The dashed contour line represents RPI = 0.

Grahic Jump Location
Fig. 8

Influence of the financial and some technical input parameters on the RPI of the CS threshold with 3 CUs and a ξlim of 0.977. Original values are those given in Tables 14 above. The SR original value equals 0.965.

Grahic Jump Location
Fig. 6

Comparison of the CS threshold and assisted for AS in Spain and Morocco. The simulations were performed with the SR data set that has been extended by five randomly created intense soiling events. The RPI shown in color was calculated in comparison to the reference CS applied to the same soiling rate scenario. The color scale is the same for all graphs. Strategy, power plant type, and site are shown in the headers.




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