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56 results for “Offshore wind”
Correlation challenges for North Sea offshore wind power: a Norwegian case study
<p>Dataset and results from this study: Hjelmeland, M., & Nøland, J. K. (2023). Correlation challenges for North Sea offshore wind power: a Norwegian case study. Scientific Reports 2023 13:1, 13(1), 1–17. <a href="https://doi.org/10.1038/s41598-023-45829-2">https://doi.org/10.1038/s41598-023-45829-2</a></p><p>The git hub repo with source code can be found here: <a href="https://github.com/martinhjel/wind-covariation">https://github.com/martinhjel/wind-covariation</a></p>
Data for: "Hydrogen for harvesting the potential of offshore wind: A North Sea case study"
<p>Supply and demand data for hydrogen and electricity for article "Hydrogen for harvesting the potential of offshore wind: A North Sea case study".</p><p> </p><p>The data is based on the following work:</p><p>G. Durakovic, P. C. del Granado, A. Tomasgard, Powering Europe with North Sea offshore wind: The impact of hydrogen investments on grid infrastructure and power prices, Energy 263 (2023) 125654. doi:10.1016/j.energy.2022.125654.</p><p>The data is obtained from the EMPIRE model found at https://github.com/Goggien/EMPIRE-Public.</p><p>The EMPIRE model is developed at NTNU in The Department of Industrial Economics and Technology Management.</p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Policy choices and outcomes for offshore wind auctions globally - Supplementary Data
<p>This is the dataset to the academic paper with the title:</p> <p>"Policy choices and outcomes for offshore wind auctions globally"</p> <p>Please cite the dataset as follows:</p> <p>Jansen,M.; Beiter, P.;Riepin, I. Müsgens, Felix; Juarez Guajardo-Fajardo, Victor, Staffell, I.; Bulder, B.; Kitzing, L. (2022) Policy choices and outcomes for offshore wind auctions globally. Energy Policy. DOI: https://doi.org/10.1016/j.enpol.2022.113000</p>
Brazil-Offshore Wind Model
<p><strong>Installation and running the model</strong></p> <p>It is necessary to install Calliope to run the model. Instructions for installation and running the model are available at:<a href="https://calliope.readthedocs.io/">https://calliope.readthedocs.io/</a>.</p> <p><strong>Temporal resolution</strong></p> <p>The temporal resolution of the model is 6 hours by default. You can set the model with another resolution in the "overrides" file: </p> <p>time_resampling:</p> <p> model.time: {function: resample, function_options: {'resolution': '6H'}}</p> <p>Note that running the model might be computationally expensive. The full model contains one year of data. To test the model, specify a shorter time subset in the "overrides" file > weather years. For instance, over ten days of data:</p> <p> year_2010:<br> model.subset_time: ['2010-01-01', '2010-01-10']</p> <p> </p> <p><strong>Scenarios</strong></p> <p>The scenario names are structured as follows: bias correction factor case + scenario name+ weather year.</p> <p>Example:</p> <p>low_baseline_2019</p> <p> </p> <p><em>Bias correction factor case:</em></p> <p>Low: represents the 25<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p>Median: represents the 50<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p>Up: represents the 75<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p> </p> <p><em>Scenario name</em></p> <p>baseline: status quo;</p> <p>offshore wind farm capex reduction: capex is reduced by 10%, 30%, 50%, and 70%;</p> <p>natural gas prices: in gas low, the gas price is US$ 24.87, while in gas high, US$ 62.05;</p> <p>offshore wind farm capex reduction + natural gas price: capex reduction (10%,30%,50%, and 70%) combined with the high price of natural gas.</p> <p> </p> <p><em>Weather year</em></p> <p>Weather years include data from 2000 to 2019.</p>
Datasets for the publication " Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil"
<p>This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil".</p> <ul> <li><strong>Bias correction</strong></li> <li>Technical data of existing farms (ABBEólica) </li> <li>Bias correction factors at the farm level</li> </ul> <p> </p> <ul> <li><strong>Demand</strong></li> <li>Simulated wind and solar power</li> <li>Biomass, nuclear, and small hydropower generation in 2019</li> <li>Raw demand data</li> <li>Updated demand </li> </ul> <p> </p> <ul> <li><strong>Hydropower time series</strong></li> <li>Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage</li> </ul> <p> </p> <ul> <li><strong>Offshore wind farms</strong></li> <li>Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity;</li> <li>Code to estimate offshore wind farm capex and opex.</li> </ul> <p> </p> <ul> <li><strong>Results of Calliope model </strong></li> <li>capacity</li> <li>carrier_prod (power generation)</li> <li>storage</li> <li>costs</li> <li>emissions</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Global offshore wind turbine analysis with Sentinel-1 - supplementary data
<p>Gloabl offshore wind turbine analysis with Sentinel-1 - supplementary data</p> <p>The files are supplementary data of the publication:</p> <p>Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications</p> <p>which is currently under review in the International Journal of Applied Earth Observation and Geoinformation</p> <p>supplementary_data_B_OWT_height_capacity.csv holds 50 pairs of offshore wind turbine hub heights and the corresponding installed capacities along with the offshore wind farm project name, the number of turbines of this wind farm, and the source the information originates from.</p> <p>supplementary_data_B_DeepOWT_1_21_2_plus.geojson is the extended version of the DeepOWT data set (https://zenodo.org/record/5933967) with all of the derived attributes in the respective publication e.g. OWT hub height and installed capacity.</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Dataset for "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling" article
<p>This dataset is used for analysis and visualization, that supports the article titled "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling", which has been accepted for publication in the Journal of Renewable Sustainable Energy.</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>
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
European offshore wind farms and marine energy deployements
<p>Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades:</p> <p>- European offshore wind farms</p> <p>- tidal energy converter deployements in Europe</p> <p>- wave energy converter deployements in Europe</p>
Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective - Supplementary material
<p>This is the additional material provided with the journal article "Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective" published in Renewable and Sustainable Energy Reviews (<a href="https://doi.org/10.1016/j.rser.2023.113699">https://doi.org/10.1016/j.rser.2023.113699</a>).</p> <p>Datasets are provided as NetCDF files for European maps and CSVfor Economically Attractive Resource curves.</p> <p>European and National plots are provided as PDF files.</p>
One year time series of relative electric (onshore and offshore) wind turbine power datasets
<p>The datasets (in dat file format) contain ordered time series (in unit of hours with 15 minutes time resolution) of relative electric wind turbine (WT) power (expressed in percentage) of one randomly selected year (05 August 2022 to 04 August 2023) and four of its constituting weeks (01 to 07 SEP 2022, 02 to 08 JAN 2023, 13 to 19 MAR 2023 and 16 to 22 JUL 2023) with their associated graphs (in PNG file format). The original data stem from the electricity grid of Flanders (onshore) and Belgium (offshore) as provided by Elia ( <a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/power-generation/,DanaInfo=.awxyCiqohHko,SSL+solar-pv-power-generation-data">https://www.elia.be/en/grid-data/power-generation/solar-pv-power-generation-data</a> ) under CC BY 4.0 license (<a href="https://priv-lu-myremote.tech.ec.europa.eu/en/grid-data/,DanaInfo=.awxyCiqohHko,SSL+elia-open-data-license?csrt=16568311101247852187">https://www.elia.be/en/grid-data/elia-open-data-license?csrt=16568311101247852187</a>). The relative electric WT power was derived by dividing the measured electric WT power by the monitored peak electric WT power multiplied by 100 %.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
<p>The expanding use of wind farms as a source of renewable energy can impact bird populations due to collisions and other factors. Globally, seabirds are one of the avian taxonomic groups most threatened by anthropogenic disturbance; adequately assessing the potential impact of offshore wind farms (OWFs) is important for developing strategies to avoid or minimize harm to their populations. We estimated avoidance rates of OWFs — the degree to which birds show reduced utilization of OWF areas — by Sandwich Terns <em>Thalasseus sandvicensis</em> at two breeding colonies in western Europe: Scolt Head (United Kingdom) and De Putten (the Netherlands). We modeled GPS tracking data using integrated Step Selection Functions (iSSFs) to estimate the relative selection of habitats at the scale of time between successive GPS relocations – in our case 10 minutes, in which terns traveled ca. 2 km on average. The foraging ranges of birds from each colony overlapped with multiple OWFs. iSSFs considered distance from the colony and habitat characteristics (water depth and sediment grain size) and movement characteristics. Macro-avoidance rates, where 1 means complete avoidance, were estimated at 0.54 (95% CrI = 0.35, 0.7) for birds originating from Scolt Head and 0.41 (95% CrI = 0.21, 0.56) for those from De Putten. Estimates for individual OWFs also indicated avoidance but were associated with considerable uncertainty. Our results were inconclusive with regard to the behavioral response to the areas directly surrounding OWFs (within 1.5 km); estimates suggested indifference and avoidance and were associated with large uncertainty. Avoidance rate of OWFs significantly increased with turbine density, suggesting OWF design may help to reduce the impact of OWFs on Sandwich Terns. The partial avoidance of OWFs by Sandwich Terns implies that the species will experience risks of collision and habitat loss due to OWFs constructed within their foraging ranges.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
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Data from: An ecological vulnerability index to assess impacts of offshore wind facilities on migratory song-birds
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Offshore wind power production in the North Sea
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Data from: Three dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms
A large increase in offshore wind turbine capacity is anticipated within the next decade, raising concerns about possible adverse impacts on birds as a result of collision risk. Birds' flight heights greatly influence this risk, yet height estimates are currently available only using methods such as radar- or ship-based observations over limited areas. Bird-borne data-loggers have the potential to provide improved estimates of collision risk and here, we used data from Global Position System (GPS)-loggers and barometric pressure loggers to track the three-dimensional movements of northern gannets rearing chicks at a large colony in south-east Scotland (Bass Rock), located <50 km from several major wind farm developments with recent planning consent. We estimated the foraging ranges and densities of birds at sea, their flight heights during different activities and the spatial variation in height during trips. We then used these data in collision-risk models to explore how the use of different methods to determine flight height affects the predicted risk of birds colliding with turbines. Gannets foraged in and around planned wind farm sites. The probability of flying at collision-risk height was low during commuting between colonies and foraging areas (median height 12 m) but was greater during periods of active foraging (median height 27 m), and we estimated that ˜1500 breeding adults from Bass Rock could be killed by collision with wind turbines at two planned sites in the Firth of Forth region each year. This is up to 12 times greater than the potential mortality predicted using other available flight-height estimates. Synthesis and applications: The use of conventional flight-height estimation techniques resulted in large underestimates of the numbers of birds at risk of colliding with wind turbines. Hence, we recommend using GPS and barometric tracking to derive activity-specific and spatially explicit flight heights and collision risks. Our predictions of potential mortality approached levels at which long-term population viability could be threatened, highlighting a need for further data to refine estimates of collision risks and sustainable mortality thresholds. We also advocate raising the minimum permitted clearance of turbine blades at sites with high potential collision risk from 22 to 30 m above sea level.
Offshore wind farms low-trophic aquaculture multi-use potential: Figure data
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.