bibliography.bib

@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{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},
institution  = {National Renewable Energy Laboratory},
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       = {NREL/TP-5500-54824},
title        = {{National Solar Radiation Database 1991–2010 Update: User's Manual }},
url          = {https://www.nrel.gov/docs/fy12osti/54824.pdf},
year         = {2012}
}
@inproceedings{Bowersox2021,
abstract  = {Utility-scale photovoltaic (PV) systems are generally designed with a greater DC power capacity than their inverters are capable of converting to AC power, resulting in system clipping. Modeling tools used to forecast the generation of photovoltaic systems predict the degree to which a system is expected to clip throughout the year. However, standard PV modeling software utilizes an input of hourly meteorological data to predict these losses. For periods of intermittent irradiance in which the incident irradiance at times may cause the system to clip, an average of this irradiance over the course of an hour may result in a prediction which fails to encompass clipping losses for those intermittent intervals. Presented within this paper is a model for predicting these losses by averaging high-resolution datasets and comparing losses through the NREL developed modeling tool System Advisor Model. This prediction model utilizes freely available data through the National Solar Radiation Database to predict these losses, so the methodology presented allows users and developers to predict subhourly clipping losses at a site where minute-resolution ground data would otherwise be unavailable.},
author    = {Bowersox, David A and MacAlpine, Sara M.},
booktitle = {2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC43889.2021.9518956},
isbn      = {978-1-6654-1922-2},
issn      = {01608371},
month     = {jun},
pages     = {2507--2509},
publisher = {IEEE},
title     = {{Predicting Subhourly Clipping Losses for Utility-Scale PV Systems}},
url       = {https://ieeexplore.ieee.org/document/9518956/},
year      = {2021}
}
@techreport{Matsui2021,
author      = {Matsui, Richard and Olson, Dana and Liu, Daniel and Narayanan, Tara and Erion-Lorico, Tristan and Ahmad, Jackie and Fort, Jonathan and Metaut, Pierre-Alexandre and Mattheis, Catlin and Fisher, Bryan and Whitfield, Kent and Casey, Ray and Roedel, Alex and Dise, Skip and Orsino, Erik and Post, Alex and McGoldrick, Peter and Jensen, Sam},
institution = {kWh Analytics},
publisher   = {kWh Analytics},
title       = {{Solar Risk Assessment: 2021}},
url         = {https://www.kwhanalytics.com/solar-risk-assessment},
year        = {2021}
}
@inproceedings{Parikh2021,
abstract  = {Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.8% and 1.2% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.8% and 2.7% reduction in mean bias error, respectively, when the clipping correction was applied.},
author    = {Parikh, Abhishek and Perry, Kirsten and Anderson, Kevin and Hobbs, William B. and Kharait, Rounak and Mikofski, Mark A.},
booktitle = {2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC43889.2021.9518564},
isbn      = {978-1-6654-1922-2},
issn      = {01608371},
keywords  = {TMY,clipping,inverter,irradiance,modeling,performance,solar,variability},
month     = {jun},
pages     = {1670--1675},
publisher = {IEEE},
title     = {{Validation of Subhourly Clipping Loss Error Corrections}},
url       = {https://ieeexplore.ieee.org/document/9518564/},
year      = {2021}
}
@inproceedings{Kharait2020,
abstract  = {High-frequency measurements of solar resource from the National Institute of Standards and Technology (NIST) PV test-bed in Gaithersburg, MD were down-sampled from 1-minute to 1-hour and used to predict energy yield and clipping losses. Three virtual systems based on the NIST ground array with increasing DC/AC ratio were compared. With hourly input, clipping losses were smaller and energy yield was larger than predictions with high-frequency input. Systems with higher DC/AC ratio were more sensitive to time-resolution. The average difference between hourly and 1-minute predicted energy yield was 2% during the month of July for a DC/AC ratio of 1.3. These results demonstrate the importance of high-frequency solar resource in climates with solar variability to avoid over-predictions of energy yield for inverter blocks with a DC/AC ratio greater than one. The results of this study were implemented to develop a sub-hourly clipping adjustment factor for a pre-construction project site codenamed, "Orion," near Northbridge, Massachusetts.},
author    = {Kharait, Rounak and Raju, Simran and Parikh, Abhishek and Mikofski, Mark A. and Newmiller, Jeff},
booktitle = {2020 47th IEEE Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC45281.2020.9300911},
isbn      = {978-1-7281-6115-0},
keywords  = {TMY,clipping,clouds,intermittent,irradiance,ramp rate,solar resource,variability,weather},
month     = {jun},
pages     = {1330--1334},
publisher = {IEEE},
title     = {{Energy Yield and Clipping Loss Corrections for Hourly Inputs in Climates with Solar Variability}},
url       = {https://ieeexplore.ieee.org/document/9300911/},
year      = {2020}
}
@inproceedings{Cormode2019,
abstract  = {Financial models of PV power plants that are created during the development phase of a project are typically based upon simulations performed at hourly timesteps. Historical irradiance data used in these simulations, such as a Typical Meteorological Year, are created from average irradiances over hour-long periods. We find that when actual weather conditions include significant intra-hour variability in irradiance, the annual energy production estimates based on modeling in hourly timesteps will not account for all inverter clipping which occurs during moments of high irradiance. This effect is particularly significant for systems with high a DC to AC nameplate ratio. At some PV plants already in operation, this phenomenon has been found to result in overestimated production estimates of nearly 5%. In this work, the average amount of this error is quantified for locations within the United States, and a methodology is proposed for compensating for it in post processing. This methodology applies a variable discount to each hour of the year for hourly energy output simulations based on irradiance. The discount function is intended to account for the difference in useful insolation as computed from hourly average irradiance data versus sub-hourly sampling rates.},
author    = {Cormode, Daniel and Croft, Nate and Hamilton, Rachel and Kottmer, Scott},
booktitle = {2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC40753.2019.8981206},
isbn      = {978-1-7281-0494-2},
keywords  = {performance analysis,photovoltaic systems,power system modeling,power system simulation,solar energy,solar power generation},
month     = {jun},
pages     = {2293--2298},
publisher = {IEEE},
title     = {{A method for error compensation of modeled annual energy production estimates introduced by intra-hour irradiance variability at PV power plants with a high DC to AC ratio}},
url       = {https://ieeexplore.ieee.org/document/8981206/},
year      = {2019}
}
@inproceedings{Bradford2020,
abstract  = {Industry-standard solar resource assessment methods assume hourly-resolution modeling, which typically overestimates generation due to irradiance variability within an hour. Depending on PV site location and configuration, the high bias introduced by hourly modeling methods is generally greater than 1.5% and can exceed 4% on the annual AC energy when compared to real-world operations. It is critical that bias corrections be applied to hourly solar energy simulations prior to making binding investment and financing decisions. This study presents a random forest regression model that accurately resolves the modeling bias attributed to intra-hour irradiance variability. The model considers site-specific meteorology and layout design parameters to resolve typical seasonal and diurnal variability patterns. It has been validated using minute-resolution observations from operational solar farms and pre-construction meteorological measurements, with model bias error shown to be-0.1% on annual energy.},
author    = {Bradford, Kristen and Walker, Richard and Moon, Dennis and Ibanez, Mario},
booktitle = {2020 47th IEEE Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC45281.2020.9300613},
isbn      = {978-1-7281-6115-0},
keywords  = {irradiance variability,performance modeling,photovoltaic systems,solar resource assessment},
month     = {jun},
pages     = {2679--2682},
publisher = {IEEE},
title     = {{A Regression Model to Correct for Intra-Hourly Irradiance Variability Bias in Solar Energy Models}},
url       = {https://ieeexplore.ieee.org/document/9300613/},
year      = {2020}
}
@inproceedings{Anderson2020,
abstract  = {Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning regressor ensemble model, comprised of a random forest and an XGBoost model, and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.},
author    = {Anderson, Kevin and Perry, Kirsten},
booktitle = {2020 47th IEEE Photovoltaic Specialists Conference (PVSC)},
doi       = {10.1109/PVSC45281.2020.9300750},
isbn      = {978-1-7281-6115-0},
keywords  = {Index Terms-photovoltaic,clipping,high-frequency,inverter,irradiance,model-ing,satellite,satu-ration,variability},
month     = {jun},
pages     = {1433--1438},
publisher = {IEEE},
title     = {{Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data}},
url       = {https://www.nrel.gov/docs/fy20osti/76021.pdf},
year      = {2020}
}
@article{Sengupta2018,
abstract  = {The National Solar Radiation Data Base (NSRDB), consisting of solar radiation and meteorological data over the United States and regions of the surrounding countries, is a publicly open dataset that has been created and disseminated during the last 23 years. This paper briefly reviews the complete package of surface observations, models, and satellite data used for the latest version of the NSRDB as well as improvements in the measurement and modeling technologies deployed in the NSRDB over the years. The current NSRDB provides solar irradiance at a 4-km horizontal resolution for each 30-min interval from 1998 to 2016 computed by the National Renewable Energy Laboratory's (NREL's) Physical Solar Model (PSM) and products from the National Oceanic and Atmospheric Administration's (NOAA's) Geostationary Operational Environmental Satellite (GOES), the National Ice Center's (NIC's) Interactive Multisensor Snow and Ice Mapping System (IMS), and the National Aeronautics and Space Administration's (NASA's) Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The NSRDB irradiance data have been validated and shown to agree with surface observations with mean percentage biases within 5% and 10% for global horizontal irradiance (GHI) and direct normal irradiance (DNI), respectively. The data can be freely accessed via https://nsrdb.nrel.gov or through an application programming interface (API). During the last 23 years, the NSRDB has been widely used by an ever-growing group of researchers and industry both directly and through tools such as NREL's System Advisor Model.},
author    = {Sengupta, Manajit and Xie, Yu and Lopez, Anthony and Habte, Aron and Maclaurin, Galen and Shelby, James},
doi       = {10.1016/j.rser.2018.03.003},
issn      = {13640321},
journal   = {Renewable and Sustainable Energy Reviews},
keywords  = {Satellite,Solar Radiation},
month     = {jun},
number    = {March 2018},
pages     = {51--60},
publisher = {Elsevier Ltd},
title     = {{The National Solar Radiation Data Base (NSRDB)}},
url       = {https://linkinghub.elsevier.com/retrieve/pii/S136403211830087X},
volume    = {89},
year      = {2018}
}
@article{Boyd2017a,
abstract = {Three grid-connected monocrystalline silicon arrays on the National Institute of Standards and Technology (NIST) campus in Gaithersburg, MD have been instrumented and monitored for 1 yr, with only minimal gaps in the data sets. These arrays range from 73kW to 271 kW, and all use the same module, but have different tilts, orientations, and configurations. One array is installed facing east and west over a parking lot, one in an open field, and one on a flat roof. Various measured relationships and calculated standard metrics have been used to compare the rela- tive performance of these arrays in their different configurations. Comprehensive performance models have also been created in the modeling software PVSYST for each array, and its predictions using measured on-site weather data are compared to the arrays' meas- ured outputs. The comparisons show that all three arrays typically have monthly performance ratios (PRs) above 0.75, but differ sig- nificantly in their relative output, strongly correlating to their operating temperature and to a lesser extent their orientation. The model predictions are within 5% of the monthly delivered energy values except during the winter months, when there was intermit- tent snow on the arrays, and during maintenance and other out- ages.},
author = {Boyd, Matthew T.},
doi = {10.1115/1.4038314},
issn = {0199-6231},
journal = {Journal of Solar Energy Engineering},
keywords = {PVSYST model,data acquisition,performance,photovoltaic (PV) array,solar,temperature},
month = {nov},
number = {1},
pages = {014503},
title = {{Comparative Performance and Model Agreement of Three Common Photovoltaic Array Configurations}},
url = {https://solarenergyengineering.asmedigitalcollection.asme.org/article.aspx?doi=10.1115/1.4038314},
volume = {140},
year = {2017}
}
@article{Boyd2017,
abstract = {Three grid-connected monocrystalline silicon photovoltaic arrays have been instrumented with research-grade sensors on the Gai- thersburg, MD campus of the National Institute of Standards and Technology (NIST). These arrays range from 73kW to 271kW and have different tilts, orientations, and configurations. Irradi- ance, temperature, wind, and electrical measurements at the arrays are recorded, and images are taken of the arrays to moni- tor shading and capture any anomalies. A weather station has also been constructed that includes research-grade instrumenta- tion to measure all standard meteorological quantities plus addi- tional solar irradiance spectral bands, full spectrum curves, and directional components using multiple irradiance sensor technol- ogies. Reference photovoltaic (PV) modules are also monitored to provide comprehensive baseline measurements for the PV arrays. Images of the whole sky are captured, along with images of the instrumentation and reference modules to document any obstruc- tions or anomalies. Nearly, all measurements at the arrays and weather station are sampled and saved every 1 s, with monitoring having started on Aug. 1, 2014. This report describes the instru- mentation approach used to monitor the performance of these photovoltaic systems, measure the meteorological quantities, and acquire the images for use in PV performance and weather moni- toring and computer model validation.},
author = {Boyd, Matthew T.},
doi = {10.1115/1.4035830},
issn = {0199-6231},
journal = {Journal of Solar Energy Engineering},
keywords = {data acquisition,inverter,meteorology,photovoltaic,solar,weather station},
number = {3},
pages = {034502},
title = {{High-Speed Monitoring of Multiple Grid-Connected Photovoltaic Array Configurations and Supplementary Weather Station}},
url = {https://solarenergyengineering.asmedigitalcollection.asme.org/article.aspx?doi=10.1115/1.4035830},
volume = {139},
year = {2017}
}
@article{Boyd2017b,
abstract = {In July 2012, the National Institute of Standards and Technology (NIST) completed construction of threephotovoltaic (PV) arrays on its Gaithersburg, MD campus. Comprehensive data acquisition systems were installed and an onsite weather station was also built to collect ancillary solar and meteorological measurements that are needed for the full characterization and modeling of the PV arrays. These datasets provide high-resolution, low-uncertainty, comprehensive PV performance and weather data for extended, continuous time periods. The creation of these datasets is fulfilling a need of the research and energy communities that few other datasets meet. Data from these systems have been collected for about three years at the time of this publication, between August 2014 and July 2017, and are being provided to the public via an online web portal for viewing and download.},
author = {Boyd, Matthew T.},
doi = {10.6028/jres.122.040},
issn = {2165-7254},
journal = {Journal of Research of the National Institute of Standards and Technology},
keywords = {040,10,122,2017,6028,PV,accepted,data acquisition,doi,https,inverter,jres,meteorology,november 1,october 27,org,photovoltaic,published,pv,solar,weather station,weather station.},
month = {nov},
number = {40},
pages = {40},
title = {{Performance Data from the NIST Photovoltaic Arrays and Weather Station}},
url = {https://nvlpubs.nist.gov/nistpubs/jres/122/jres.122.040.pdf},
volume = {122},
year = {2017}
}
@inproceedings{solarfarmer2018,
abstract  = {Accurate performance prediction of large PV systems with shading is challenging because computational complexity increases with system size. Solar Farmer is a new PV performance model with 3-D shading. Comparing predictions with measurements from the NIST PV test bed we observed a decrease in the annual difference of 17% between module and submodule shading. By varying the resolution of shading from module to cell level, we also determined that 5 points persubmodule, resulting in a 0.5% annual difference, was sufficient to accurately predict performance of shaded systems. Therefore, a balance of accuracy and computational expense was achieved allowing performance predictions of large PV systems with shade.},
author    = {Mikofski, Mark A. and Lynn, Matthew and Byrne, James and Hamer, Mike and Neubert, Anja and Newmiller, Jeff},
booktitle = {2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC \& 34th EU PVSEC)},
doi       = {10.1109/PVSC.2018.8547323},
isbn      = {978-1-5386-8529-7},
issn      = {0160-8371},
keywords  = {,3D shading,Arrays,Computational modeling,Geometry,Inverters,Mathematical model,Meteorology,NIST,NIST PV test bed,PV performance model,PV systems,Solar Farmer,computational complexity,mismatch,performance,photovoltaic power systems,shaded systems,shading,solar cells,submodule mismatch calculation,submodule shading},
month     = {jun},
pages     = {3635--3639},
publisher = {IEEE},
title     = {{Accurate Performance Predictions of Large PV Systems with Shading using Submodule Mismatch Calculation}},
url       = {https://ieeexplore.ieee.org/document/8547323/},
year      = {2018}
}
@article{pvlib2018,
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). pvlib python is developed on GitHub by contributors from academia, national laboratories , and private industry. pvlib python is released with a BSD 3-clause license allowing permissive use with attribution. pvlib python is extensively tested for functional and algorithm consistency. Continuous integration services check each pull request on multiple platforms and Python versions. The pvlib python API is thoroughly documented and detailed tutorials are provided for many features. The documentation includes help for installation and guidelines for contributions. The documentation is hosted at readthe-docs.org as of this writing. A Google group and StackOverflow tag provide venues for user discussion and help. The pvlib python API was designed to serve the various needs of the many subfields of solar power research and engineering. It is implemented in three layers: core functions, the Location and PVSystem classes, and the ModelChain class. The core API consists of a collection of functions that implement algorithms. These algorithms are typically implementations of models described in peer-reviewed publications. The functions provide maximum user flexibility, however many of the function arguments require an unwieldy number of parameters. The next API level contains the Location and PVSystem classes. These abstractions provide simple methods that wrap the core function API layer. The method API simplification is achieved by separating the data that represents the object (object attributes) from the data that the object methods operate on (method arguments). For example, a Location is represented by a latitude, longitude, elevation, timezone, and name, which are Location object attributes. Then a Location object method operates on a datetime to get the corresponding solar position. The methods combine these data sources when calling the function layer, then return the results to the user. The final level of API is the ModelChain class, designed to simplify and standardize the process of stitching together the many modeling steps necessary to convert a time series of weather data to AC solar power generation, given a PV system and a location.},
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 = {https://joss.theoj.org/papers/10.21105/joss.00884},
volume = {3},
year = {2018}
}
@techreport{Freeman2018,
abstract = {This document describes the capabilities of the System Advisor Model (SAM) developed and distributed by the U.S. Department of Energy's National Renewable Energy Laboratory. The document is for potential users and others wanting to learn about the model's capabilities. SAM is a techno-economic computer model that calculates performance and financial metrics of renewable energy projects. Project developers, policy makers, equipment manufacturers, and researchers use graphs and tables of SAM results in the process of evaluating financial, technology, and incentive options for renewable energy projects. SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating, wind, geothermal, biomass, and conventional power systems. The financial models are for projects that either buy and sell electricity at retail rates (residential and commercial) or sell electricity at a price determined in a power purchase agreement (PPA). SAM's simulation tools facilitate parametric and sensitivity analyses, Monte Carlo simulation and weather variability (P50/P90) studies. SAM can also read input variables from Microsoft Excel worksheets. For software developers, the SAM software development kit (SDK) makes it possible to use SAM simulation modules in their applications written in C/C plus plus, C sharp, Java, Python, MATLAB, and other languages. NREL provides both SAM and the SDK as free downloads at https://sam.nrel.gov. SAM is an open source project, so its source code is available to the public. Researchers can study the code to understand the model algorithms, and software programmers can contribute their own models and enhancements to the project. Technical support and more information about the software are available on the website.},
address = {Golden, CO (United States)},
author = {Freeman, Janine M. and DiOrio, Nicholas A. and Blair, Nathan J. and Neises, Ty W. and Wagner, Michael J. and Gilman, Paul and Janzou, Steven},
doi = {10.2172/1440404},
institution = {National Renewable Energy Laboratory (NREL)},
month = {may},
number = {NREL/TP-6A20-70414},
title = {{System Advisor Model (SAM) General Description (Version 2017.9.5)}},
url = {https://www.nrel.gov/docs/fy18osti/70414.pdf},
year = {2018}
}
@INPROCEEDINGS{9519024,
author={Mikofski, Mark A. and Kharait, Rounak},
booktitle={2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)},
title={Comparison of Predicted PV System Performance with SURFRAD versus TMY},
year={2021},
pages={2155-2159},
doi={10.1109/PVSC43889.2021.9519024},
url={https://ieeexplore.ieee.org/document/9519024}}
@ARTICLE{9095219,
author={Prilliman, Matthew and Stein, Joshua S. and Riley, Daniel and Tamizhmani, Govindasamy},
journal={IEEE Journal of Photovoltaics},
title={Transient Weighted Moving-Average Model of Photovoltaic Module Back-Surface Temperature},
year={2020},
volume={10},
number={4},
pages={1053-1060},
doi={10.1109/JPHOTOV.2020.2992351},
url={https://ieeexplore.ieee.org/document/9095219}}
@techreport{King2007,
abstract = {This document provides an empirically based performance model for grid-connected photovoltaic inverters used for system performance (energy) modeling and for continuous monitoring of inverter performance during system operation. The versatility and accuracy of the model were validated for a variety of both residential and commercial size inverters. Default parameters for the model can be obtained from manufacturers' specification sheets, and the accuracy of the model can be further refined using measurements from either well-instrumented field measurements in operational systems or using detailed measurements from a recognized testing laboratory. An initial database of inverter performance parameters was developed based on measurements conducted at Sandia National Laboratories and at laboratories supporting the solar programs of the California Energy Commission},
address = {Albuquerque, NM, and Livermore, CA (United States)},
author = {King, David and Gonzalez, Sigifredo and Galbraith, Gary and Boyson, William},
doi = {10.2172/920449},
institution = {Sandia National Laboratories (SNL)},
month = {sep},
number = {September},
title = {{Performance model for grid-connected photovoltaic inverters.}},
url = {https://www.osti.gov/servlets/purl/920449/},
year = {2007}
}
@techreport{osti_1797569,
title = {Proceedings of the PV Reliability Workshop, February 22-26, 2021},
author = {Muller, Matthew and Repins, Ingrid},
abstractNote = {The 2021 Photovoltaic Reliability Workshop (PVRW) continued in the longstanding PVRW tradition of engaged, passionate presentation and discussion, though in a new virtual format for 2021. The workshop was hosted over 5 days, with each day featuring two oral presentation sessions and one interactive poster session. In these proceedings, you will find links to video recordings for most of the oral presentations and poster presentations along with Q&A sessions and panel discussions. Each presentation is linked from its title in this agenda.},
doi = {},
url = {https://www.osti.gov/biblio/1797569}, journal = {},
number = {NREL/CP-5K00-80055},
institution = {National Renewable Energy Laboratory (NREL)},
place = {United States},
year = {2021},
month = {6}
}