Robust PV Degradation Methodology and Application

Jordan, Dirk C.; Deline, Chris; Kurtz, Sarah R.; Kimball, Gregory M.; Anderson, Mike

doi:10.1109/JPHOTOV.2017.2779779

Title: Robust PV Degradation Methodology and Application

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Abstract

The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case of sensor drift, data shifts, and soiling. Compared with alternate methods, we demonstrate that the proposed method delivers the lowest uncertainty in degradation rate estimates for a fleet of 486 PV systems.

Authors:

;

Publication Date:: Thu Mar 01 00:00:00 EST 2018

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:: 1422265

Alternate Identifier(s):: OSTI ID: 1417794; OSTI ID: 1422266

Report Number(s):: NREL/JA-5J00-67945
Journal ID: ISSN 2156-3381; 8233204

Grant/Contract Number:: AC36-08-GO28308; AC36-08GO28308

Resource Type:: Published Article

Journal Name:: IEEE Journal of Photovoltaics

Additional Journal Information:: Journal Name: IEEE Journal of Photovoltaics Journal Volume: 8 Journal Issue: 2; Journal ID: ISSN 2156-3381

Publisher:: Institute of Electrical and Electronics Engineers

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; 36 MATERIALS SCIENCE; degradation; temperature measurement; standards; atmospheric modeling; photovoltaic systems; inverters

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                    Jordan, Dirk C., Deline, Chris, Kurtz, Sarah R., Kimball, Gregory M., and Anderson, Mike. Robust PV Degradation Methodology and Application.  United States: N. p., 2018. 
Web.  doi:10.1109/JPHOTOV.2017.2779779.

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                    Jordan, Dirk C., Deline, Chris, Kurtz, Sarah R., Kimball, Gregory M., & Anderson, Mike. Robust PV Degradation Methodology and Application.  United States.  https://doi.org/10.1109/JPHOTOV.2017.2779779

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                    Jordan, Dirk C., Deline, Chris, Kurtz, Sarah R., Kimball, Gregory M., and Anderson, Mike. Thu .  
"Robust PV Degradation Methodology and Application".  United States.  https://doi.org/10.1109/JPHOTOV.2017.2779779.

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@article{osti_1422265,

  title        = {Robust PV Degradation Methodology and Application},

  author       = {Jordan, Dirk C. and Deline, Chris and Kurtz, Sarah R. and Kimball, Gregory M. and Anderson, Mike},

  abstractNote = {The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case of sensor drift, data shifts, and soiling. Compared with alternate methods, we demonstrate that the proposed method delivers the lowest uncertainty in degradation rate estimates for a fleet of 486 PV systems.},

  doi          = {10.1109/JPHOTOV.2017.2779779},

  journal      = {IEEE Journal of Photovoltaics},

  number       = 2,

  volume       = 8,

  place        = {United States},

  year         = {Thu Mar 01 00:00:00 EST 2018},

  month        = {Thu Mar 01 00:00:00 EST 2018}

}

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