Quantifying Soiling Loss Directly From PV Yield

Deceglie, Michael G.; Micheli, Leonardo; Muller, Matthew

doi:10.1109/JPHOTOV.2017.2784682

Title: Quantifying Soiling Loss Directly From PV Yield

Abstract

Soiling of photovoltaic (PV) panels is typically quantified through the use of specialized sensors. Here, we describe and validate a method for estimating soiling loss experienced by PV systems directly from system yield without the need for precipitation data. The method, termed the stochastic rate and recovery (SRR) method, automatically detects soiling intervals in a dataset, then stochastically generates a sample of possible soiling profiles based on the observed characteristics of each interval. In this paper, we describe the method, validate it against soiling station measurements, and compare it with other PV-yield-based soiling estimation methods. The broader application of the SRR method will enable the fleet scale assessment of soiling loss to facilitate mitigation planning and risk assessment.

Authors:

^[1];

^[1]; Muller, Matthew ^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States)

Publication Date:: 2018-01-23

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1419409

Report Number(s):: NREL/JA-5J00-70668
Journal ID: ISSN 2156-3381

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Journal of Photovoltaics

Additional Journal Information:: Journal Volume: 8; Journal Issue: 2; Journal ID: ISSN 2156-3381

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; field performance; Monte Carlo methods; photovoltaic cells; photovoltaic systems; soiling; solar energy; solar panels; time series analysis

Citation Formats


                    Deceglie, Michael G., Micheli, Leonardo, and Muller, Matthew. Quantifying Soiling Loss Directly From PV Yield.  United States: N. p., 2018. 
Web.  doi:10.1109/JPHOTOV.2017.2784682.

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                    Deceglie, Michael G., Micheli, Leonardo, & Muller, Matthew. Quantifying Soiling Loss Directly From PV Yield.  United States.  https://doi.org/10.1109/JPHOTOV.2017.2784682

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                    Deceglie, Michael G., Micheli, Leonardo, and Muller, Matthew. Tue .  
"Quantifying Soiling Loss Directly From PV Yield".  United States.  https://doi.org/10.1109/JPHOTOV.2017.2784682.  https://www.osti.gov/servlets/purl/1419409.

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@article{osti_1419409,

  title        = {Quantifying Soiling Loss Directly From PV Yield},

  author       = {Deceglie, Michael G. and Micheli, Leonardo and Muller, Matthew},

  abstractNote = {Soiling of photovoltaic (PV) panels is typically quantified through the use of specialized sensors. Here, we describe and validate a method for estimating soiling loss experienced by PV systems directly from system yield without the need for precipitation data. The method, termed the stochastic rate and recovery (SRR) method, automatically detects soiling intervals in a dataset, then stochastically generates a sample of possible soiling profiles based on the observed characteristics of each interval. In this paper, we describe the method, validate it against soiling station measurements, and compare it with other PV-yield-based soiling estimation methods. The broader application of the SRR method will enable the fleet scale assessment of soiling loss to facilitate mitigation planning and risk assessment.},

  doi          = {10.1109/JPHOTOV.2017.2784682},

  journal      = {IEEE Journal of Photovoltaics},

  number       = 2,

  volume       = 8,

  place        = {United States},

  year         = {2018},

  month        = {1}

}

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https://doi.org/10.1109/JPHOTOV.2017.2784682

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Figures / Tables:

Fig. 1: Steps of the yield-based SRR soiling estimation method. Blue circles are the daily performance metric, PM. (a) Time series of PM is divided into soiling intervals by detecting positive shifts in the moving median (black line), which correspond to cleaning events (dashed lines). (b) Slope of each intervalmore »

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Works referencing / citing this record:

Predicting photovoltaic soiling losses using environmental parameters: An update
journal, October 2018

Micheli, Leonardo; Deceglie, Michael G.; Muller, Matthew
Progress in Photovoltaics: Research and Applications, Vol. 27, Issue 3
DOI: 10.1002/pip.3079

Modelling photovoltaic soiling losses through optical characterization
journal, January 2020

Smestad, Greg P.; Germer, Thomas A.; Alrashidi, Hameed
Scientific Reports, Vol. 10, Issue 1
DOI: 10.1038/s41598-019-56868-z

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Title: Quantifying Soiling Loss Directly From PV Yield

Abstract

Citation Formats

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