4.6 Article

Energy yield prediction of a bifacial PV system with a miniaturized test array

Journal

SOLAR ENERGY
Volume 179, Issue -, Pages 316-325

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2018.12.042

Keywords

Bifacial; Energy yield prediction; Test system; Tilt angle

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Because of the sensitivity on multiple additional factors, compared to monofacial standard installations, the simulation and prediction of a bifacial PV arrays yield is by far more complicated and less reliable. Accordingly, the determination of optimized installation conditions is considerably less straightforward for bifacial installations. Due to the pronounced dependencies also the assignability of otherwise applied installation conditions to similar systems is limited. Because of the low accuracy of the traditional approaches to predict the bifacial system output the use of a miniaturized test rig might be an interesting option. Provided that the results can be assigned to measurements at real systems it can be used as a cheap and flexible testing device. Miniaturized devices could be used in long-term trials for yield measurements at specific locations, to identify optimum installation conditions or to validate simulation algorithms. Running several test rigs with different configurations in parallel would enable a direct comparison. The small size of a miniaturized rig also allows a fast change of the set-up, which is an interesting feature to test the respective impact at otherwise almost unchanged conditions in short-term tests. In this paper we report on a test device which is a miniaturized (1:12) replica of an existing PV array with commercial bifacial modules. The measurement data of both systems are compared in order to investigate if there is a clear assignability. A unique feature of the large test field, a permanent and automated variation of the tilt angle, is also transferred to the miniaturized version. Accordingly, the whole tilt angle range is tested and potential tilt angle dependent effects are revealed. Based on the obtained data we give an estimation of the error in the prediction accuracy and discuss options for possible improvements.

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