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30 results for “capacity factor”
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Factors to predict above-ground biomass carbon carrying capacity
<p>The climate data (Mean annual temperature (°C, MAT), mean annual precipitation (mm, MAP), annually accumulated temperature with days ≥ 0°C (°C-days, AAT0), annually accumulated temperature with days ≥ 10°C (°C-days, AAT10), aridity index, and humidity index ), soil properties (soil texture and soil types) and DEM are available from the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/); The geological elements and hydrological elements data can be found at http://dcc.ngac.org.cn/geologicalData/rest/geologicalData/geologicalDataDetail/402881f75d9bc077015d9bc084160000and https://www.webmap.cn/commres.do?method=result25W; The geomorphology data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/data/63e290d7-7087-462a-acac-50195fba530b/). All data were resampled at 500m resolution.</p>
Capacity factors for wind turbines
<p>Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.</p> <p>File <em>FWA_coordinates_2500m.csv</em> holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km<sup>2</sup> resolution.</p> <p>To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance <span class="math-tex">\(\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}\)</span>, (where <em>v</em> is wind speed) to smooth (convolute) the original power curves [3, 4].</p> <p>The calculation of capacity factor cf at wind atlas grid point k is described by the formula<br> <span class="math-tex">\(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v\)</span>,<br> where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.</p> <p><strong>References</strong></p> <ol> <li>Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: <a href="http://www.windatlas.fi">http://www.windatlas.fi</a>. [Accessed: 28-Jun-2016]</li> <li>B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.</li> <li>Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.</li> <li>Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.</li> </ol> <p> </p>
Wind and solar capacity factor time series by year and grid cell over the contiguous U.S.
<p>This data set contains hourly capacity factor time series of wind and solar resources over the contiguous U.S.</p> <p> </p> <p>The included time series cover four individual years and 2,586 grid cells. The years range from 2016 to 2019. The grid cells correspond to the grid cells of the NASA's MERRA-2 reanalysis data set into which the contiguous U.S. is subdivided. The grid cells have a spatial resolution of 0.5° latitude x 0.625° longitude with dimensions ranging from about 55 km x 45 km to 55 km x 62 km.</p> <p> </p> <p>This data set is used to generate the results of the following journal article:</p> <p>Enrico G. A. Antonini, Tyler H. Ruggles, David J. Farnham, Ken Caldeira, "The quantity-quality transition in the value of expanding wind and solar power generation", iScience 25 (4), 104140, 2022.</p> <p> </p> <p>Code and instructions required to reproduce the results reported in the above paper are available in the GitHub repositories at <a href="https://github.com/eantonini/Distributed_wind_and_solar_generation">https://github.com/eantonini/Distributed_wind_and_solar_generation</a> and <a href="https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022">https://github.com/carnegie/MEM_public/tree/Antonini_et_al_2022</a>.</p>
Capacity factors for PV , wind onshore and wind offshore
<p>Hourly capacity factors of electricity generation for:</p> <ul> <li>PV (per raster)</li> <li>wind onshore (per raster)</li> <li>wind offshore (per country)</li> </ul>
Effects of Cardiac Rehabilitation (CR) on Functional Capacity and Cardiovascular Risk Factors
ClinicalTrials.gov study NCT02575976. IPD Sharing: NO. Countries: 1. Publications: 11.
Socio-demographic, institutional and governance factors influencing adaptive capacity of smallholder irrigation schemes
<p>Socio-demographic, institutional and governance factors influencing adaptive capacity of smallholder irrigation schemes</p>
Transcription factor Zbtb1 interacts with bridging factor Lmo2 and maintains the T-lineage differentiation capacity of lymphoid progenitor cells
GEO Series GSE199696. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Interferon Regulatory Factor 5 represses mitochondrial matrix protein GHITM to limit macrophage oxidative capacity in early response to obesity [bmdm]
GEO Series GSE208667. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing.
Transcription factors activate genes through the phase separation capacity of their activation domains [RNA-seq]
GEO Series GSE120474. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Knockout of zebrafish colony-stimulating factor 1 receptor by CRISPR/Cas9 affects metabolism and locomotion capacity
GEO Series GSE164728. Danio rerio. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcription factors activate genes through the phase separation capacity of their activation domains
GEO Series GSE120476. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
The Gcn4 Transcription Factor Reduces Protein Synthesis Capacity and Extends Yeast Lifespan [ChIP-Seq]
GEO Series GSE85588. Saccharomyces cerevisiae. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Interferon Regulatory Factor 5 represses mitochondrial matrix protein GHITM to limit macrophage oxidative capacity in early response to obesity [atm]
GEO Series GSE208648. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
A single TCF transcription factor, regardless of its activation capacity, is sufficient for effective trilineage differentiation of ES cells
GEO Series GSE101898. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
The Gcn4 Transcription Factor Reduces Protein Synthesis Capacity and Extends Yeast Lifespan [RNA-Seq]
GEO Series GSE85590. Saccharomyces cerevisiae. 50 samples. Type: Expression profiling by high throughput sequencing; Other.
Factors Associated With Normal Exercise Capacity in Patients With Single Ventricle
ClinicalTrials.gov study NCT04026542. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Physical Capacity of Spina Bifida and Factors Affecting
ClinicalTrials.gov study NCT06541366. IPD Sharing: NO. Countries: 1. Publications: 0.
Filamentation and Biofilm Formation are Regulated by the Phase-Separation Capacity of Network Transcription Factors in Candida albicans
GEO Series GSE245897. Candida albicans. 12 samples. Type: Expression profiling by high throughput sequencing.
The Gcn4 Transcription Factor Reduces Protein Synthesis Capacity and Extends Yeast Lifespan
GEO Series GSE85591. Saccharomyces cerevisiae. 52 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
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