Updates to joss

joss2024/paper.bib

@@ -1,3 +1,18 @@
+@article{ranalli_cloud_2021,
+	title = {Cloud advection model of solar irradiance smoothing by spatial aggregation},
+	volume = {13},
+	url = {https://aip.scitation.org/doi/abs/10.1063/5.0050428},
+	doi = {10.1063/5.0050428},
+	number = {3},
+	urldate = {2021-06-23},
+	journal = {Journal of Renewable and Sustainable Energy},
+	author = {Ranalli, Joseph and Peerlings, Esther E. M.},
+	month = may,
+	year = {2021},
+	note = {Publisher: American Institute of Physics},
+	pages = {033704},
+}
+
 @article{Ranalli2024_JPV,
     author = {Joseph Ranalli and William B. Hobbs},
     title = {{PV} {Plant} {Equipment} {Labels} and {Layouts} {Can} {Be} {Validated} by {Analyzing} {Cloud} {Motion} in {Existing} {Plant} {Measurements}},
@@ -21,6 +36,153 @@ @inproceedings{Ranalli2024_PVSC
 	booktitle = {52nd {IEEE} {PV} {Specialists} {Conference}}
 }
 
+@article{virtanen_scipy_2020,
+	title = {{SciPy} 1.0: fundamental algorithms for scientific computing in {Python}},
+	volume = {17},
+	issn = {1548-7105},
+	shorttitle = {{SciPy} 1.0},
+	doi = {10.1038/s41592-019-0686-2},
+	abstract = {SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.},
+	language = {eng},
+	number = {3},
+	journal = {Nature Methods},
+	author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and van der Walt, Stefan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C. J. and Polat, Ilhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Antonio H. and Pedregosa, Fabian and van Mulbregt, Paul and {SciPy 1.0 Contributors}},
+	month = mar,
+	year = {2020},
+	pmid = {32015543},
+	pmcid = {PMC7056644},
+	keywords = {Algorithms, Computational Biology, Computer Simulation, History, 20th Century, History, 21st Century, Linear Models, Models, Biological, Nonlinear Dynamics, Programming Languages, Signal Processing, Computer-Assisted, Software},
+	pages = {261--272},
+}
+
+@article{macke_hdcp2_2017,
+	title = {The {HD}({CP})$^{\textrm{2}}$ {Observational} {Prototype} {Experiment} ({HOPE}) – an overview},
+	volume = {17},
+	issn = {1680-7316},
+	url = {https://www.atmos-chem-phys.net/17/4887/2017/acp-17-4887-2017-discussion.html},
+	doi = {10.5194/acp-17-4887-2017},
+	abstract = {{\textless}p{\textgreater}{\textless}strong{\textgreater}Abstract.{\textless}/strong{\textgreater} The HD(CP)$^{\textrm{2}}$ Observational Prototype Experiment (HOPE) was performed as a major 2-month field experiment in Jülich, Germany, in April and May 2013, followed by a smaller campaign in Melpitz, Germany, in September 2013. HOPE has been designed to provide an observational dataset for a critical evaluation of the new German community atmospheric icosahedral non-hydrostatic (ICON) model at the scale of the model simulations and further to provide information on land-surface–atmospheric boundary layer exchange, cloud and precipitation processes, as well as sub-grid variability and microphysical properties that are subject to parameterizations. HOPE focuses on the onset of clouds and precipitation in the convective atmospheric boundary layer. This paper summarizes the instrument set-ups, the intensive observation periods, and example results from both campaigns. {\textless}br{\textgreater}{\textless}br{\textgreater} HOPE-Jülich instrumentation included a radio sounding station, 4 Doppler lidars, 4 Raman lidars (3 of them provide temperature, 3 of them water vapour, and all of them particle backscatter data), 1 water vapour differential absorption lidar, 3 cloud radars, 5 microwave radiometers, 3 rain radars, 6 sky imagers, 99 pyranometers, and 5 sun photometers operated at different sites, some of them in synergy. The HOPE-Melpitz campaign combined ground-based remote sensing of aerosols and clouds with helicopter- and balloon-based in situ observations in the atmospheric column and at the surface. {\textless}br{\textgreater}{\textless}br{\textgreater} HOPE provided an unprecedented collection of atmospheric dynamical, thermodynamical, and micro- and macrophysical properties of aerosols, clouds, and precipitation with high spatial and temporal resolution within a cube of approximately 10 × 10 × 10 km$^{\textrm{3}}$. HOPE data will significantly contribute to our understanding of boundary layer dynamics and the formation of clouds and precipitation. The datasets have been made available through a dedicated data portal. {\textless}br{\textgreater}{\textless}br{\textgreater} First applications of HOPE data for model evaluation have shown a general agreement between observed and modelled boundary layer height, turbulence characteristics, and cloud coverage, but they also point to significant differences that deserve further investigations from both the observational and the modelling perspective.{\textless}/p{\textgreater}},
+	number = {7},
+	urldate = {2020-01-02},
+	journal = {Atmospheric Chemistry and Physics},
+	author = {Macke, Andreas and Seifert, Patric and Baars, Holger and Barthlott, Christian and Beekmans, Christoph and Behrendt, Andreas and Bohn, Birger and Brueck, Matthias and Bühl, Johannes and Crewell, Susanne and Damian, Thomas and Deneke, Hartwig and Düsing, Sebastian and Foth, Andreas and Girolamo, Paolo Di and Hammann, Eva and Heinze, Rieke and Hirsikko, Anne and Kalisch, John and Kalthoff, Norbert and Kinne, Stefan and Kohler, Martin and Löhnert, Ulrich and Madhavan, Bomidi Lakshmi and Maurer, Vera and Muppa, Shravan Kumar and Schween, Jan and Serikov, Ilya and Siebert, Holger and Simmer, Clemens and Späth, Florian and Steinke, Sandra and Träumner, Katja and Trömel, Silke and Wehner, Birgit and Wieser, Andreas and Wulfmeyer, Volker and Xie, Xinxin},
+	month = apr,
+	year = {2017},
+	pages = {4887--4914},
+}
+
+@article{lave_cloud_2013,
+	title = {Cloud speed impact on solar variability scaling – {Application} to the wavelet variability model},
+	volume = {91},
+	issn = {0038-092X},
+	url = {http://www.sciencedirect.com/science/article/pii/S0038092X13000406},
+	doi = {10.1016/j.solener.2013.01.023},
+	abstract = {The Wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) powerplant output given a single irradiance sensor as input has been developed and validated previously. Central to the WVM method is a correlation scaling coefficient (A) that calibrates the decay of correlation of wavelet modes as a function of distance and timescale, and which varies by day and geographic location. Previously, a local irradiance sensor network was required to derive A. In this work, we determine A from cloud speeds. Cloud simulator results indicated that the A value is linearly proportional to the cloud speed (CS): A=12CS. Cloud speeds from a numerical weather model (NWM) were then used to create a database of daily A values for North America. For validation, the WVM was run to simulate a 48MW PV plant with both NWM A values and with ground A values found from a sensor network. Both WVM methods closely matched the distribution of ramp rates (RRs) of measured power, and were a strong improvement over linearly scaling up a point sensor. The incremental error in using NWM A values over ground A values was small. The ability to use NWM-derived A values means that the WVM can be used to simulate a PV plant anywhere a single high-frequency irradiance sensor exists. This can greatly assist in module siting, plant sizing, and storage decisions for prospective PV plants.},
+	urldate = {2019-11-21},
+	journal = {Solar Energy},
+	author = {Lave, Matthew and Kleissl, Jan},
+	month = may,
+	year = {2013},
+	keywords = {Cloud speed, PV powerplant, Smoothing, Variability, Wavelet},
+	pages = {11--21},
+}
+
+@article{marcos_power_2011,
+	title = {Power output fluctuations in large scale pv plants: {One} year observations with one second resolution and a derived analytic model},
+	volume = {19},
+	copyright = {Copyright © 2010 John Wiley \& Sons, Ltd.},
+	issn = {1099-159X},
+	shorttitle = {Power output fluctuations in large scale pv plants},
+	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pip.1016},
+	doi = {10.1002/pip.1016},
+	abstract = {The variable nature of the irradiance can produce significant fluctuations in the power generated by large grid-connected photovoltaic (PV) plants. Experimental 1 s data were collected throughout a year from six PV plants, 18 MWp in total. Then, the dependence of short (below 10 min) power fluctuation on PV plant size has been investigated. The analysis focuses on the study of fluctuation frequency as well as the maximum fluctuation value registered. An analytic model able to describe the frequency of a given fluctuation for a certain day is proposed. Copyright © 2010 John Wiley \& Sons, Ltd.},
+	number = {2},
+	urldate = {2019-11-26},
+	journal = {Progress in Photovoltaics: Research and Applications},
+	author = {Marcos, Javier and Marroyo, Luis and Lorenzo, Eduardo and Alvira, David and Izco, Eloisa},
+	year = {2011},
+	keywords = {analytic model, grid-connected, large PV plants, power fluctuations},
+	pages = {218--227},
+}
+
+@article{hoff_quantifying_2010,
+	title = {Quantifying {PV} power {Output} {Variability}},
+	volume = {84},
+	issn = {0038-092X},
+	url = {http://www.sciencedirect.com/science/article/pii/S0038092X10002380},
+	doi = {10.1016/j.solener.2010.07.003},
+	abstract = {This paper presents a novel approach to rigorously quantify power Output Variability from a fleet of photovoltaic (PV) systems, ranging from a single central station to a set of distributed PV systems. The approach demonstrates that the relative power Output Variability for a fleet of identical PV systems (same size, orientation, and spacing) can be quantified by identifying the number of PV systems and their Dispersion Factor. The Dispersion Factor is a new variable that captures the relationship between PV Fleet configuration, Cloud Transit Speed, and the Time Interval over which variability is evaluated. Results indicate that Relative Output Variability: (1) equals the inverse of the square root of the number of systems for fully dispersed PV systems; and (2) could be further minimized for optimally-spaced PV systems.},
+	number = {10},
+	urldate = {2019-11-21},
+	journal = {Solar Energy},
+	author = {Hoff, Thomas E. and Perez, Richard},
+	month = oct,
+	year = {2010},
+	keywords = {Distributed PV generation, Photovoltaics, Solar resource, Utility, Variability},
+	pages = {1782--1793},
+}
+
+@inproceedings{pelland_spatiotemporal_2021,
+	title = {Spatiotemporal {Interpolation} of {High} {Frequency} {Irradiance} {Data} for {Inverter} {Testing}},
+	doi = {10.1109/PVSC43889.2021.9518827},
+	abstract = {A model for interpolating irradiance data in space and time is developed using cloud motion vectors. It is tested using two networks of photodiodes in Eastern Canada that measure irradiance at time scales of milliseconds and spatial scales of tens of meters. The model captures network-average variability with skill scores of up to 86\% compared to nearest neighbor interpolation. We provide case studies showing how to use this model to study inverter response to rapid irradiance fluctuations. We also use AC power measurements from an inverter collocated with one of the photodiode networks to benchmark our approach and the wavelet variability model.},
+	booktitle = {2021 {IEEE} 48th {Photovoltaic} {Specialists} {Conference} ({PVSC})},
+	author = {Pelland, Sophie and Gagné, Alexandre and Allam, Mahmoud A. and Turcotte, Dave and Ninad, Nayeem},
+	month = jun,
+	year = {2021},
+	note = {ISSN: 0160-8371},
+	keywords = {Benchmark testing, Cloud computing, Data models, Interpolation, Inverters, Robustness, Time measurement, cloud motion vector, interpolation, inverter, irradiance, photovoltaic, variability, wavelet},
+	pages = {0211--0218},
+}
+
+@article{jamaly_robust_2018,
+	title = {Robust cloud motion estimation by spatio-temporal correlation analysis of irradiance data},
+	volume = {159},
+	issn = {0038-092X},
+	url = {http://www.sciencedirect.com/science/article/pii/S0038092X17309556},
+	doi = {10.1016/j.solener.2017.10.075},
+	abstract = {The main contributor to spatio-temporal variability in the solar resource is clouds passing over photovoltaic (PV) modules. Cloud velocity is a principal input to many short-term forecast and variability models. In this paper spatio-temporal correlations of irradiance data are analyzed to estimate cloud motion. The analysis is performed using two spatially and temporally resolved simulated irradiance datasets generated from large eddy simulation. Cloud motion is estimated using two different methods; the cross-correlation method (CCM) applied to two or a few consecutive time steps and cross-spectral analysis (CSA) where the cloud speed and direction are estimated by cross-spectral analysis of a longer time series. CSA is modified to estimate the cloud motion direction as the case with least variation for all the velocities in the cloud motion direction. To ensure reliable cloud motion estimation, quality control (QC) is added to the CSA and CCM analyses. The results show 33\% (52°) and 21\% (6°) improvement in the cloud motion speed (direction) estimation using the modified CSA and CCM over the original methods (without QC), respectively. In general, CCM results are accurate for all the different cloud cover fractions with average relative mean bias error (rMBE) of cloud speed and mean absolute error of cloud direction equal to 3\% and 3°, respectively. For low cloud cover fractions, CSA estimates the cloud motion speed and direction with rMBE and mean absolute error equal to 10\% and 11°, respectively. However, for high cloud cover fractions and unsteady cloud speed, CSA results are not reliable for 3–4 h time series; however, splitting the whole time series into shorter time intervals reduces the rMBE and mean absolute error to 15\% and 16° respectively.},
+	urldate = {2020-10-26},
+	journal = {Solar Energy},
+	author = {Jamaly, Mohammad and Kleissl, Jan},
+	month = jan,
+	year = {2018},
+	keywords = {Cloud motion, Solar forecast, Solar radiation, Spatio-temporal variability},
+	pages = {306--317},
+}
+
+@inproceedings{gagne_directional_2018,
+	title = {Directional {Solar} {Variability} {Analysis}},
+	doi = {10.1109/EPEC.2018.8598442},
+	abstract = {The irradiance at ground level mostly fluctuates due to cloud coverage. As clouds are moving toward a certain direction, the cardinal orientation of photovoltaic arrays affects the variability of the output power, and thus the impact on the electric power grid. This paper presents a new methodology with a circular layout for irradiance monitoring units to assess the solar variability in different directions of any site based on cloud speed-direction trend and directional variability reduction. The proposed methodology is used to assess the directional variability for a site at Varennes, QC, Canada using 1 year of measured data. The cloud speed direction is studied in order to observe any trend from a month-to-month and from an hour-to-hour. Overall the cloud direction has a trend of West to East direction, especially during the winter months. The variability reduction for each axis is estimated using the variability index (VI). The largest VI reduction is observed close to the cloud direction axis.},
+	booktitle = {2018 {IEEE} {Electrical} {Power} and {Energy} {Conference} ({EPEC})},
+	author = {Gagné, Alexandre and Ninad, Nayeem and Adeyemo, John and Turcotte, Dave and Wong, Steven},
+	month = oct,
+	year = {2018},
+	note = {ISSN: 2381-2842},
+	keywords = {Cloud computing, Clouds, Inverters, Layout, Market research, Monitoring, Time series analysis, directional variability, inverter, irradiance, power quality, solar variability, variability index, variability reduction},
+	pages = {1--6},
+}
+
+@inproceedings{stein_variability_2012,
+	address = {Denver, CO},
+	title = {The {Variability} {Index}: {A} {New} and {Novel} {Metric} for {Quantifying} {Irradiance} and {PV} {Output} {Variability}},
+	booktitle = {Proceedings of the {World} {Renewable} {Energy} {Forum}},
+	author = {Stein, Joshua S. and Hansen, Clifford W. and Reno, Matthew J.},
+	month = may,
+	year = {2012},
+	pages = {13--17},
+}
+
+@article{lave_characterizing_2015,
+	title = {Characterizing local high-frequency solar variability and its impact to distribution studies},
+	volume = {118},
+	journal = {Solar Energy},
+	author = {Lave, Matthew and Reno, Matthew J. and Broderick, Robert J.},
+	year = {2015},
+	pages = {327--337},
+}
+
 @article{holmgren_pvlib_2018,
 	title = {pvlib python: a python package for modeling solar energy systems},
 	volume = {3},