bibliography.bib

@article{F.Holmgren2018,
abstract = {pvlib python is a community-supported open source tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python aims to provide reference implementations of models relevant to solar energy, including for example algorithms for solar position, clear sky irradiance, irradiance transposition, DC power, and DC-to-AC power conversion. pvlib python is an important component of a growing ecosystem of open source tools for solar energy (William F. Holmgren, Hansen, Stein, {\&} Mikofski, 2018).},
author = {Holmgren, William F. and Hansen, Clifford W. and Mikofski, Mark A.},
doi = {10.21105/joss.00884},
issn = {2475-9066},
journal = {Journal of Open Source Software},
month = {sep},
number = {29},
pages = {884},
title = {pvlib python: a python package for modeling solar energy systems},
url = {http://joss.theoj.org/papers/10.21105/joss.00884},
volume = {3},
year = {2018}
}
@article{Jordan2018,
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},
author = {Jordan, Dirk C. and Deline, Chris and Kurtz, Sarah R. and Kimball, Gregory M. and Anderson, Mike},
doi = {10.1109/JPHOTOV.2017.2779779},
issn = {21563381},
journal = {IEEE Journal of Photovoltaics},
keywords = {Degradation rates,PV lifetime,durability,photovoltaic (PV) field performance,reliability},
number = {2},
pages = {525--531},
title = {{Robust PV Degradation Methodology and Application}},
volume = {8},
year = {2018}
}
@article{Augustine2000,
abstract = {A surface radiation budget observing network (SURFRAD) has been established for the United States to support satellite retrieval validation, modeling, and climate, hydrology, and weather research. The primary measurements are the downwelling and upwelling components of broadband solar and thermal infrared irradiance. A hallmark of the network is the measurement and computation of ancillary parameters important to the transmission of radiation. SURFRAD commenced operation in 1995. Presently, it is made up of six stations in diverse climates, including the moist subtropical environment of the U.S. southeast, the cool and dry northern plains, and the hot and arid desert southwest. Network operation involves a rigorous regimen of frequent calibration, quality assurance, and data quality control. An efficient supporting infrastructure has been created to gather, check, and disseminate the basic data expeditiously. Quality controlled daily processed data files from each station are usually available via the Internet within a day of real time. Data from SURFRAD have been used to validate measurements from NASA's Earth Observing System series of satellites, satellite-based retrievals of surface erythematogenic radiation, the national ultraviolet index, and real-time National Environmental Satellite, Data, and Information Service (NESDIS) products. It has also been used for carbon sequestration studies, to check radiative transfer codes in various physical models, for basic research and instruction at universities, climate research, and for many other applications. Two stations now have atmospheric energy flux and soil heat flux instrumentation, making them full surface energy balance sites. It is hoped that eventually all SURFRAD stations will have this capability.},
author = {Augustine, John A. and DeLuisi, John J. and Long, Charles N.},
doi = {10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2},
issn = {00030007},
journal = {Bulletin of the American Meteorological Society},
number = {10},
pages = {2341--2357},
title = {{SURFRAD - A national surface radiation budget network for atmospheric research}},
volume = {81},
year = {2000}
}
@techreport{Matsui2020,
author = {Matsui, Richard and Moore, Jackson and Nunalee, Christopher and {Garcia da Fonseca}, Leila and Vadhavkar, Nikhil and Crimmins, Jim and Dise, Skip and Ahmad, Jackie and Gregory, Ian and Fort, Jonathan and Corbitt, Josh},
institution = {kWh Analytics},
title = {{Solar Risk Assessment: 2020 Quantitative Insights from the Industry Experts}},
url = {https://www.kwhanalytics.com/solar-risk-assessment},
year = {2020}
}
@article{Habte2017,
abstract = {This paper validates the performance of the physics-based Physical Solar Model (PSM) data set in the National Solar Radiation Data Base (NSRDB) to quantify the accuracy of the magnitude and the spatial and temporal variability of the solar radiation data. Achieving higher penetrations of solar energy on the electric grid and reducing integration costs requires accurate knowledge of the available solar resource. Understanding the impacts of clouds and other meteorological constituents on the solar resource and quantifying intra-/inter-hour, seasonal, and interannual variability are essential for accurately designing utility-scale solar energy projects. Solar resource information can be obtained from ground-based measurement stations and/or from modeled data sets. The availability of measurements is scarce, both temporally and spatially, because it is expensive to maintain a high-density solar radiation measurement network that collects good quality data for long periods of time. On the other hand, high temporal and spatial resolution gridded satellite data can be used to estimate surface radiation for long periods of time and is extremely useful for solar energy development. Because of the advantages of satellite-based solar resource assessment, the National Renewable Energy Laboratory developed the PSM. The PSM produced gridded solar irradiance -- global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance -- for the NSRDB at a 4-km by 4-km spatial resolution and half-hourly temporal resolution covering the 18 years from 1998-2015. The NSRDB also contains additional ancillary meteorological data sets, such as temperature, relative humidity, surface pressure, dew point, and wind speed. Details of the model and data are available at https://nsrdb.nrel.gov.},
author = {Habte, Aron and Sengupta, Manajit and Lopez, Anthony and Habte, Aron and Sengupta, Manajit and Lopez, Anthony},
number = {April},
pages = {1998--2015},
title = {{Evaluation of the National Solar Radiation Database ( NSRDB Version 2 ): 1998 – 2015 Evaluation of the National Solar Radiation Database ( NSRDB Version 2 ): 1998 – 2015}},
year = {2017},
journal = {NREL/TP-5D00-67722}
}
@techreport{Marion1995,
address = {Golden, CO (United States)},
author = {Marion, William and Urban, Ken},
doi = {10.2172/87130},
institution = {National Renewable Energy Laboratory (NREL)},
month = {jun},
number = {D},
title = {{User's Manual for TMY2s (Typical Meteorological Years) - Derived from the 1961-1990 National Solar Radiation Data Base}},
url = {https://www.osti.gov/biblio/87130},
year = {1995}
}
@techreport{Wilcox2008,
address = {Golden, CO},
author = {Wilcox, S. and Marion, W.},
doi = {10.2172/928611},
institution = {National Renewable Energy Laboratory (NREL)},
title = {{Users Manual for TMY3 Data Sets (Revised)}},
url = {https://www.osti.gov/biblio/928611-users-manual-tmy3-data-sets-revised},
year = {2008}
}
@article{ERBS1982293,
title = {Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation},
journal = {Solar Energy},
volume = {28},
number = {4},
pages = {293-302},
year = {1982},
issn = {0038-092X},
doi = {https://doi.org/10.1016/0038-092X(82)90302-4},
url = {https://www.sciencedirect.com/science/article/pii/0038092X82903024},
author = {D.G. Erbs and S.A. Klein and J.A. Duffie},
abstract = {Hourly pyrheliometer and pyranometer data from four U.S. locations are used to establish a relationship between the hourly diffuse fraction and the hourly clearness index kT. This relationship is compared to the relationship established by Orgill and Hollands and to a set of data from Highett, Australia, and agreement is within a few percent in both cases. The transient simulation program TRNSYS is used to calculate the annual performance of solar energy systems using several correlations. For the systems investigated, the effect of simulating the random distribution of the hourly diffuse fraction is negligible. A seasonally dependent daily diffuse correlation is developed from the data, and this daily relationship is used to derive a correlation for the monthly-average diffuse fraction.}
}
@article{HayDavies,
author = {Hay, J.E. and Davies, J.A.},
year = {1980},
month = {01},
pages = {59-72},
title = {Calculation of the solar radiation incident on an inclined surface},
journal = {Proceedings of the First Canadian Solar Radiation Data Workshop}
}
@article{Ineichen2002,
abstract = {We propose a new formulation for the Linke turbidity coefficient with the objective of removing its dependence upon solar geometry. In the process, we also develop two new simple clear sky models for global and direct normal irradiance. {\textcopyright} 2002 Elsevier Science Ltd. All rights reserved.},
author = {Ineichen, Pierre and Perez, Richard},
doi = {10.1016/S0038-092X(02)00045-2},
file = {:C\:/Users/mikm/Documents/Mendeley Desktop/Ineichen, Perez/Solar Energy/Ineichen, Perez - 2002 - A new airmass independent formulation for the Linke turbidity coefficient.pdf:pdf},
isbn = {0038-092X},
issn = {0038092X},
journal = {Solar Energy},
month = {sep},
number = {3},
pages = {151--157},
pmid = {179402900002},
title = {{A new airmass independent formulation for the Linke turbidity coefficient}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0038092X02000452},
volume = {73},
year = {2002}
}
@article{Faimain2008,
author = {Faiman, David},
title = {Assessing the outdoor operating temperature of photovoltaic modules},
journal = {Progress in Photovoltaics: Research and Applications},
volume = {16},
number = {4},
pages = {307-315},
keywords = {PV module temperature, Hottel–Whillier–Bliss},
doi = {https://doi.org/10.1002/pip.813},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pip.813},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pip.813},
abstract = {Abstract By a careful study of data collected from seven varieties of photovoltaic (PV) module it is demonstrated that a simple modified form of the Hottel–Whillier–Bliss (HWB) equation, familiar from the analysis of flat-plate solar–thermal collectors, can be employed to predict module temperatures within an accuracy comparable to the cell-to-cell temperature differences typically encountered within a module. Furthermore, for modules within the range of construction parameters employed in this study, the actual values of the two modified HWB constants do not appear to depend upon module type. The implication of these results for the accuracy of outdoor module characterization is discussed. Copyright © 2008 John Wiley \& Sons, Ltd.},
year = {2008}
}
@article{Dobos2012,
abstract = {This paper describes an improved algorithm for calculating the six parameters required by the California Energy Commission (CEC) photovoltaic (PV) Calculator module model. Rebate applications in California require results from the CEC PV model, and thus depend on an up-to-date database of module characteristics. Currently, adding new modules to the database requires calculating operational coefficients using a general purpose equation solver—a cumbersome process for the 300þ modules added on average every month. The combination of empirical regressions and heuristic methods presented herein achieve automated convergence for 99.87% of the 5487 modules in the CEC database and greatly enhance the accuracy and efficiency by which new modules can be characterized and approved for use. The added robustness also permits general purpose use of the CEC=6 parameter module model by modelers and system analysts when standard module specifications are known, even if the module does not exist in a preprocessed database.},
author = {Dobos, Aron P.},
doi = {10.1115/1.4005759},
file = {:C\:/Users/mikm/Documents/Mendeley Desktop/Dobos/Journal of Solar Energy Engineering/Dobos - 2012 - An Improved Coefficient Calculator for the California Energy Commission 6 Parameter Photovoltaic Module Model.pdf:pdf},
isbn = {0199-6231},
issn = {01996231},
journal = {Journal of Solar Energy Engineering},
keywords = {5-parameter model,6-parameter model,photovoltaics,solar},
number = {2},
pages = {021011},
title = {{An Improved Coefficient Calculator for the California Energy Commission 6 Parameter Photovoltaic Module Model}},
url = {http://dx.doi.org/10.1115/1.4005759},
volume = {134},
year = {2012}
}
@article{Muller2014,
abstract = {Solar resource assessments use solar radiation data from past observations to estimate the average annual solar radiation over the expected lifetime for a solar energy system. However, solar radiation at the Earth's surface is not stable over time but undergoes signif- icant long-term variations often referred to as “global dimming and brightening”. This study analyzes the effect of these long-term trends on solar resource assessments. Based on long-term measurement records in Germany, it is found that the additional uncertainty of solar resource assessments caused by long-term trends in solar radiation is about 3% on the horizontal plane and even higher for tilted or tracked planes. These additional uncertainties are not included in most uncertainty calculations for solar resource assessments up to now. Furthermore, for the measurement stations analyzed, the current irradiance level is about 5% above the long-term average of the years 1951–2010. Since the direction of future trends in solar radiation is not known, different possibilities to estimate the future solar resource are compared. In view of long-term trends that could extend beyond the period of past observations and beyond the projected lifetime of a solar energy application, a paradigm shift is proposed: instead of using the longest possible period to calculate an average value, only the 10 most recent years should be used as the estimator for future solar irradiance.},
author = {M{\"{u}}ller, Bj{\"{o}}rn and Wild, Martin and Driesse, Anton and Behrens, Klaus},
doi = {10.1016/j.solener.2013.11.013},
file = {:C\:/Users/mikm/Documents/Mendeley Desktop/M{\"{u}}ller et al/Solar Energy/M{\"{u}}ller et al. - 2014 - Rethinking solar resource assessments in the context of global dimming and brightening.pdf:pdf},
issn = {0038092X},
journal = {Solar Energy},
keywords = {global dimming and brightening,irradiance,solar radiation,solar resource assessment},
month = {jan},
pages = {272--282},
title = {{Rethinking solar resource assessments in the context of global dimming and brightening}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0038092X13004933},
volume = {99},
year = {2014}
}
@article{Wilcox2012,
abstract = {This user's manual provides information on the updated 1991-2010 National Solar Radiation Database. Included are data format descriptions, data sources, production processes, and information about data uncertainty.},
author = {Wilcox, Stephen},
file = {:C\:/Users/mikm/Documents/Mendeley Desktop/Wilcox/NrelTp-5500-54824/Wilcox - 2012 - National Solar Radiation Database 1991–2010 Update User's Manual.pdf:pdf},
journal = {Nrel/Tp-5500-54824 },
keywords = {August 2012,NREL,NREL/TP-5500-54824,NSRDB,National Renewable Energy Laboratory,National Solar Radiation Database,solar data,solar resource,user's manual},
number = {August},
title = {{National Solar Radiation Database 1991–2010 Update: User's Manual }},
url = {http://www.nrel.gov/docs/fy12osti/54824.pdf},
year = {2012}
}