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Technical Brief

Solar Variability Datalogger

[+] Author and Article Information
Matthew Lave

Sandia National Laboratories,
Livermore, CA 94550
e-mail: mlave@sandia.gov

Joshua Stein

Sandia National Laboratories,
Albuquerque, NM 87185

Ryan Smith

Pordis, LLC,
Austin, TX 78729

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 December 17, 2015; final manuscript received June 14, 2016; published online July 28, 2016. Assoc. Editor: Philippe Blanc.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Sol. Energy Eng 138(5), 054503 (Jul 28, 2016) (8 pages) Paper No: SOL-15-1432; doi: 10.1115/1.4034071 History: Received December 17, 2015; Revised June 14, 2016

To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. The low cost and ease of use of the SVD will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.

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References

Figures

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Fig. 1

Test setup with (from left to right) PV01, PV02, LICOR pyranometer, PD01, PD02, and PD03

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Fig. 2

Histograms of differences in irradiance measurements between the test sensors and the LICOR over 7 hrs on a highly variable day. Included in the top right of each plot are the mean bias difference (MBD) and root-mean squared difference (RMSD) between the test devices and the LICOR (negative MBD values indicate that the test device measured less irradiance than the LICOR).

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Fig. 3

A 30 s RR distribution for LICOR and test sensors over 7 hrs on a highly variable day

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Fig. 5

SVD (circled) installed at a weather station in Livermore, CA. The instrument to the top left of the solar panels is the PSP GHI measurement.

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Fig. 6

GHI time series measured in Albuquerque on three clear days by a CMP21 and an SVD. The SVD is shown without and with clear-sky correction.

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Fig. 7

(Left) A 30 s RR distribution collected over all days in the measurement period for pyranometers (solid lines) and SVDs (dashed lines), including VSs. The zoomed-in y-axis range of 0–10% probability is shown in larger plot, with the full 0–100% range shown in the inset plot. (Right) Scatter plot of daily VSs (one VS per day in measurement period) comparing SVD to pyranometers.

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Fig. 8

(Top) Calendar plot of SVD GHI measurements at different locations in November 2015. (Bottom) Comparison of 30 s VSs for each day.

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