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Considering the Variability of Soiling in Long-Term PV Performance Forecasting
Abstract
This study presents the development of a methodology for evaluating the variability associated with soiling on long-term photovoltaic (PV) forecasting. Independent engineering firms typically build forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions are constructed through a Monte Carlo analysis that includes various factors, such as satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within long-term performance uncertainty. In this work soiling information from 16 sites in the U.S. Southwest are combined with 24 years of rainfall data to generate 24 years of energy production with soiling losses and then subsequently generate probability of exceedance values (e.g., P50, P90, P95…). The results show that the size of the 90% confidence interval (P5–P95) can increase from –0.7% to 10.1% when interannual soiling variability and soiling rate uncertainty is included.
- Authors:
-
[1];
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Leidos, Denver, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (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:
- 1999659
- Report Number(s):
- NREL/JA-5K00-87429
Journal ID: ISSN 2156-3381; MainId:88204;UUID:19279bfb-2f5e-4658-95b4-c8ba03158519;MainAdminID:70533
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Journal of Photovoltaics
- Additional Journal Information:
- Journal Volume: 13; Journal Issue: 6; Journal ID: ISSN 2156-3381
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; interannual variability; p-values; performance forecasting; photovoltaic soiling; uncertainty
Citation Formats
Muller, Matthew, and Rashed, Faisal. Considering the Variability of Soiling in Long-Term PV Performance Forecasting. United States: N. p., 2023.
Web. doi:10.1109/JPHOTOV.2023.3300369.
Muller, Matthew, & Rashed, Faisal. Considering the Variability of Soiling in Long-Term PV Performance Forecasting. United States. https://doi.org/10.1109/JPHOTOV.2023.3300369
Muller, Matthew, and Rashed, Faisal. Mon .
"Considering the Variability of Soiling in Long-Term PV Performance Forecasting". United States. https://doi.org/10.1109/JPHOTOV.2023.3300369.
@article{osti_1999659,
title = {Considering the Variability of Soiling in Long-Term PV Performance Forecasting},
author = {Muller, Matthew and Rashed, Faisal},
abstractNote = {This study presents the development of a methodology for evaluating the variability associated with soiling on long-term photovoltaic (PV) forecasting. Independent engineering firms typically build forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions are constructed through a Monte Carlo analysis that includes various factors, such as satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within long-term performance uncertainty. In this work soiling information from 16 sites in the U.S. Southwest are combined with 24 years of rainfall data to generate 24 years of energy production with soiling losses and then subsequently generate probability of exceedance values (e.g., P50, P90, P95…). The results show that the size of the 90% confidence interval (P5–P95) can increase from –0.7% to 10.1% when interannual soiling variability and soiling rate uncertainty is included.},
doi = {10.1109/JPHOTOV.2023.3300369},
journal = {IEEE Journal of Photovoltaics},
number = 6,
volume = 13,
place = {United States},
year = {Mon Aug 28 00:00:00 EDT 2023},
month = {Mon Aug 28 00:00:00 EDT 2023}
}
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Works referenced in this record:
Dust and soiling issues and impacts relating to solar energy systems: Literature review update for 2012–2015
journal, September 2016
- Costa, Suellen C. S.; Diniz, Antonia Sonia A. C.; Kazmerski, Lawrence L.
- Renewable and Sustainable Energy Reviews, Vol. 63
pvlib python: a python package for modeling solar energy systems
journal, September 2018
- F. Holmgren, William; W. Hansen, Clifford; A. Mikofski, Mark
- Journal of Open Source Software, Vol. 3, Issue 29
Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization
journal, January 2020
- Micheli, Leonardo; Fernandez, Eduardo F.; Muller, Matthew
- IEEE Journal of Photovoltaics, Vol. 10, Issue 1
Multi-year field assessment of seasonal variability of photovoltaic soiling and environmental factors in a desert environment
journal, November 2020
- Javed, Wasim; Guo, Bing; Figgis, Benjamin
- Solar Energy, Vol. 211
Quantifying Year-to-Year Variations in Solar Panel Soiling from PV Energy-Production Data
conference, June 2017
- Deceglie, Michael G.; Micheli, Leonardo; Muller, Matthew
- 2017 IEEE 44th Photovoltaic Specialists Conference (PVSC), 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC)
An investigation of the key parameters for predicting PV soiling losses: Key parameters for predicting PV soiling losses
journal, January 2017
- Micheli, Leonardo; Muller, Matthew
- Progress in Photovoltaics: Research and Applications, Vol. 25, Issue 4
Techno-Economic Assessment of Soiling Losses and Mitigation Strategies for Solar Power Generation
journal, October 2019
- Ilse, Klemens; Micheli, Leonardo; Figgis, Benjamin W.
- Joule, Vol. 3, Issue 10
Soiling and rainfall effect on PV technology in rural Southern Europe
journal, August 2020
- Conceição, Ricardo; Vázquez, Iñigo; Fialho, Luis
- Renewable Energy, Vol. 156
Quantifying Soiling Loss Directly From PV Yield
journal, March 2018
- Deceglie, Michael G.; Micheli, Leonardo; Muller, Matthew
- IEEE Journal of Photovoltaics, Vol. 8, Issue 2
Economics of seasonal photovoltaic soiling and cleaning optimization scenarios
journal, January 2021
- Micheli, Leonardo; Fernández, Eduardo F.; Aguilera, Jorge T.
- Energy, Vol. 215
The National Solar Radiation Data Base (NSRDB)
journal, June 2018
- Sengupta, Manajit; Xie, Yu; Lopez, Anthony
- Renewable and Sustainable Energy Reviews, Vol. 89
The Effect of Soiling on Large Grid-Connected Photovoltaic Systems in California and the Southwest Region of the United States
conference, May 2006
- Kimber, A.; Mitchell, L.; Nogradi, S.
- 2006 IEEE 4th World Conference on Photovoltaic Energy Conference
Solar energy dust and soiling R&D progress: Literature review update for 2016
journal, February 2018
- Costa, Suellen C. S.; Diniz, Antonia Sonia A. C.; Kazmerski, Lawrence L.
- Renewable and Sustainable Energy Reviews, Vol. 82