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Monitoring of Soiling with the AVUS Instrument - Technical and Economic Assessment

: Heimsath, A.; Schmidt, T.; Rohani, S.; Haack, L.; Meyer, R.; Steinmetz, J.; Nitz, P.

Volltext urn:nbn:de:0011-n-5652174 (2.6 MByte PDF)
MD5 Fingerprint: 08d152e66ea62a7e1f413b791e2d6afc
Copyright AIP
Erstellt am: 30.11.2019

Richter, Christoph (Ed.):
SolarPACES 2018, 24th International Conference on Concentrating Solar Power and Chemical Energy Systems : 2-5 October 2018, Casablanca, Morocco
New York, N.Y.: AIP Press, 2019 (AIP Conference Proceedings 2126)
ISBN: 978-0-7354-1866-0
Art. 190007, 10 S.
International Conference on Concentrating Solar Power and Chemical Energy Systems (SolarPACES) <24, 2018, Casablanca>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer ISE ()
Thermische Systeme und Gebäudetechnik; CSP; optical characterization; site assessment; soiling; system monitoring; Solarthermische Kraftwerke und Industrieprozesse; konzentrierende Kollektoren

Soiling impacts energy harvesting of CSP Plants as well as operation and maintenance cost. The identification of the proneness of a potential solar site to soiling would reduce risk and improve the performance and cost prediction. To allow for medium and long term monitoring of soiling rates, precise, replicable and fully automated measuring methods are needed, similar to today’s solar resource assessment. Furthermore, the analysis of dust and its adhesion to solar reflectors are of interest. Tackling both requests, we developed a new instrument for automated online monitoring of soiling. The AVUS measurement set-up directly measures reflectance. Furthermore, the soiled samples are available for retrospective assessment of dust and its adhesion to solar reflectors. We present measurement results from a one-year monitoring campaign at a solar thermal power plant located in the south of Spain. Additionally we show a comparison of the AVUS sensor results and results from PV reference cells. We assess the advantage of soiling monitoring for financial risk reduction and for performance prediction. Detailed cost and performance models are applied to a case study of a parabolic trough plant.