bibliography.bib

@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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Cormode et al/2019 IEEE 46th Photovoltaic Specialist Conference (PVSC)IEEE 46th Photovoltaic Specialists Conference (PVSC)/Cormode et al. - 2019 - A method for error compensation of modeled annual energy produ.pdf:pdf},
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{Kharait,
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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Kharait et al/2020 47th IEEE Photovoltaic Specialists Conference (PVSC)/Kharait et al. - 2020 - Energy Yield and Clipping Loss Corrections for Hourly Inputs in Climates with Solar Variability.pdf:pdf},
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{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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Anderson, Perry/2020 47th IEEE Photovoltaic Specialists Conference (PVSC)/Anderson, Perry - 2020 - Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data(2).pdf:pdf;:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Anderson, Perry/2020 47th IEEE Photovoltaic Specialists Conference (PVSC)/Anderson, Perry - 2020 - Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data.pdf:pdf},
isbn = {978-1-7281-6115-0},
keywords = {Index Terms-photovoltaic,clipping,high-frequency,inverter,irradiance,model-ing,satellite,satu-ration,variability},
month = {jun},
number = {June},
pages = {1433--1438},
publisher = {IEEE},
title = {{Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data}},
url = {https://ieeexplore.ieee.org/document/9300750},
year = {2020}
}
@inproceedings{Bradford,
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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Bradford et al/Unknown/Bradford et al. - Unknown - A Regression Model to Correct for Intra-Hourly Irradiance Variability Bias in Solar Energy Models.pdf:pdf},
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}
}
@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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Matsui et al/Unknown/Matsui et al. - 2020 - Solar Risk Assessment 2020 Quantitative Insights from the Industry Experts.pdf:pdf},
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{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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Boyd/Journal of Research of the National Institute of Standards and Technology/Boyd - 2017 - Performance Data from the NIST Photovoltaic Arrays and Weather Station.pdf:pdf},
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.},
mendeley-groups = {PVSC47},
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}
}
@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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Augustine, Deluisi, Long/Unknown/Augustine, Deluisi, Long - Unknown - SURFRAD - A national surface radiation budget network for atmospheric research.pdf:pdf},
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{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},
file = {:C$\backslash$:/Users/mikm/Documents/Mendeley Desktop/Freeman et al/Unknown/Freeman et al. - 2018 - System Advisor Model (SAM) General Description (Version 2017.9.5).pdf:pdf},
institution = {National Renewable Energy Laboratory (NREL)},
mendeley-groups = {PVSC48},
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{Stein,
author = {Stein, Joshua and Hansen, Clifford and Reno, Matthew},
year = {2012},
month = {05},
pages = {},
booktitle = {World Renewable Energy Forum},
title = {The Variability Index: A New and Novel Metric for Quantifying Irradiance and PV Output Variability}
}
@article{Sengupta2018,
  doi = {10.1016/j.rser.2018.03.003},
  _url = {https://doi.org/10.1016/j.rser.2018.03.003},
  year = {2018},
  month = jun,
  publisher = {Elsevier {BV}},
  volume = {89},
  pages = {51--60},
  author = {Manajit Sengupta and others},
  title = {The National Solar Radiation Data Base ({NSRDB})},
  journal = {Renewable and Sustainable Energy Reviews}
}
@article{solrad,
author = {Hicks, B. B. and DeLuisi, J. J. and Matt, D. R.},
title = {The {NOAA} Integrated Surface Irradiance Study ({ISIS})—A New Surface Radiation Monitoring Program},
journal = {Bulletin of the American Meteorological Society},
volume = {77},
number = {12},
pages = {2857-2864},
year = {1996},
doi = {10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2},
URL = {https://doi.org/10.1175/1520-0477(1996)077<2857:TNISIS>2.0.CO;2},
}
@article{bsrn,
author={Driemel, A. and others},
title={Baseline Surface Radiation Network ({BSRN}): structure and data description (1992–2017)},
journal={Earth Syst. Sci. Data},
number={10},
pages={1491-1501},
doi={10.5194/essd-10-1491-2018},
year={2018} 
}
@article{uosrml,
author={Josh Peterson and Frank Vignola},
title={Structure of a comprehensive solar radiation dataset},
journal={ASES Proceedings},
year={2017},
}
@misc{midc,
title = {{NREL MIDC}: {M}easurement and {I}nstrumentation {D}ata {C}enter},
url={https://midcdmz.nrel.gov/}
}
@article{Holmgren2018,
  doi = {10.21105/joss.00884},
  url = {https://doi.org/10.21105/joss.00884},
  year = {2018},
  publisher = {The Open Journal},
  volume = {3},
  number = {29},
  pages = {884},
  author = {William F. Holmgren and Clifford W. Hansen and Mark A. Mikofski},
  title = {pvlib python: a python package for modeling solar energy systems},
  journal = {Journal of Open Source Software}
}
@misc{Allen2015,
  title={Effect of time-averaging on PV production estimates on systems with high DC to AC ratios},
  author={Allen, Jonathan O. and Hobbs, William B},
  booktitle={4th PV Performance Modeling Workshop in Cologne, Germany},
  year={2015},
  organization={Sandia PV Performance Modeling Collaborative}
}

@misc{Allen2018,
  title={The effect of short-term inverter saturation on PV performance modeling},
  author={Allen, Jonathan O. and Hobbs, William B and Bolen, Michael},
  booktitle={2018 PV Systems Symposium in Albuquerque, New Mexico USA},
  year={2018},
  organization={Sandia PV Performance Modeling Collaborative},
  url = {https://pvpmc.sandia.gov/download/6707/}
}

@misc{Allen2019,
  title={Predicting the Effect of Short-Term Inverter Saturation on PV Performance Modeling},
  author={Allen, Jonathan O. and Hobbs, William B and Bolen, Michael},
  booktitle={2019 PV Systems Symposium in Albuquerque, New Mexico USA},
  year={2019},
  organization={Sandia PV Performance Modeling Collaborative},
  url = {https://pvpmc.sandia.gov/download/7415/}
}
@article{hofmann2014improved,
  title={Improved synthesis of global irradiance with one-minute resolution for PV system simulations},
  author={Hofmann, Martin and Riechelmann, Stefan and Crisosto, Cristian and Mubarak, Riyad and Seckmeyer, Gunther},
  journal={International Journal of Photoenergy},
  volume={2014},
  year={2014},
  publisher={Hindawi}
}