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3 results for “Wind Power Potential”
Wind speed and power potential for Switzerland
<p>When using the provided data, please cite the following article:</p> <p>Amato, F., Guignard, F., Walch, A., Mohajeri, N., Scartezzini, J. L., & Kanevski, M. (2021). Spatio-temporal estimation of wind speed and wind power using machine learning: predictions, uncertainty and technical potential. arXiv preprint arXiv:2108.00859.</p> <p> </p> <p><strong>Summary: </strong></p> <p>This dataset contains an estimation of the average yearly wind speed and of the wind power potential for Switzerland, at a spatial resolution of 250 x 250 meters and over the period from 2008 to 2017.</p> <p>Wind speed data are obtained by modelling data collected at an hourly frequency on a set of up to 208 monitoring stations over the country. The data are then interpolated using a spatio-temporal machine learning model, allowing the estimation of wind speed and its uncertainty at unsampled locations. Then, the modelled spatio-temporal wind speed field is used to estimate the wind power. This is computed based on the characteristic parameters of an Enercon E-101 wind turbine at 100 meters hub height. The latter indicates the distance from the turbine platform to the rotor of an installed wind turbine, showing how high the turbine stands above the ground without considering the length of the turbine blades.</p> <p>The hourly estimations of wind speed are then averaged over each of the ten years studied, for each 250 x 250 spatial location, while wind power data are summed over each year for each spatial unit. Advantages and limitations of the proposed method are discussed in Amato et al. (2021).</p> <p><strong>Data description: </strong></p> <p>The hourly estimation of wind speed and power for Switzerland from 2008 to 2017 are available under request. Here we share the annual values. For both wind speed and power, the data are available over 660697 spatial units of 250 x 250 meters each, covering the entire Swiss territory. Check details in the file Data_description.pdf.</p> <p>Data are provided in the pickle format, see <a href="https://docs.python.org/3/library/pickle.html#module-pickle">https://docs.python.org/3/library/pickle.html#module-pickle</a>.</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>
Potential power scenario for solar, wind and hydropower in Europe
<p>Data for power scenario used to assess climate impact on solar, wind and hydropower over a 35-year historical period.</p><p> </p><p><strong>Structure and content of data repository</strong></p><p>Here, we provide information on the data used in the investigation of the scientific article "Continental complementarity of renewable energy mixes" by Wörman et al., Nature Communications Engineering.</p><p>Hydro-climatic data was obtained from the Copernicus ECMWF database for an area of 13 106 km2 covering most parts of Europe and the Middle East. The hydropower potential was calculated at the locations of hydropower stations included in the GranD data base (Beams et al., 2019). Runoff was calculated based on the E-HEPE model (Hundecha et al., 2016) and this was used to estimate the hydropower potential at station locations (Wörman et al., 2017) and to generalize these values to 362 of totally 1,055 uniformly distributed sub-areas covering Europe (see figure below). The primary data used to derive the hydropower data contained in this repository is available at this link:</p><p>Virtual Energy Storage – Hydropower, DOI: 10.5281/zenodo.3706758</p><p>Daily data of the Surface Solar Radiation Downwards (SSRD) from 01-01-1979 to 31-12-2020 was obtained from Copernicus ECMWF database and converted to radiation incident on a fixed, south-facing panel with an inclination equal to the latitude and, further, covered to PV power potential according to Huld et al (2011, 2015). The power potential was averaged over 24 hours (both night and day) under consideration of changes in the solar elevation and azimuth angles as well as aggregated for 995 of the 1,055 sub-areas. A data report is available in catalogue 4. </p><p>Meteorological data with the relevance to wind power potential was obtained from ERA5, a reanalysis product of the ECMWF's General Circulation Model available in the Copernicus Climate Data Store. For comparison, data was also taken from Merra 2 and JRA 55 and used to derive wind speed time-series from 01/01/1979 till 31/12/2019 at the location of 20,010 onshore wind farms from the "World Wind Farm Database". The primary data used to derive the solar PV power data contained in this repository is available in catalogue 4 of this repository. The primary data used to derive the wind power data contained in this repository is available at this link:</p><p>Virtual Energy Storage - Wind power, DOI: 10.5281/zenodo.7749150</p><p>The data representing power scenarios for solar, wind and hydropower are structured in five folders sharing information on different variables and their physiographic characteristics. A ReadMe file is provided in each folder to describe the format of every file:</p><p><strong>1. Temporal mean power for solar-wind-hydro at 1,055 areas</strong></p><p>This folder provides the mean power for the three renewable sources with the following geographical division (Mean_Hydro, Mean_Solar, Mean_Wind). This catalogue also contains information on area id referring to the geographical data files as well as area values and coordinates (ReadMe_mean power CSV).</p><p><strong>2. Geographical data</strong></p><p>This folder contains the following sub-folders and information:</p><ol><li>Shape files for the 1,055 areas depicted above (shapefile_solar_domain)</li><li>Shape file of Europe and parts of the Middle East including different nations (Europe_Shapefile)</li><li>An Excel file with geodata för the 1,055 areas (areas_points_land)</li></ol><p><strong>3. GranD_Hydropower time-series</strong></p><p>This folder contains the following sub-folders and information:</p><ol><li>A ReadMe file</li><li>Temporal mean values of potential hydropower production estimated at GranD hydropower stations (Temporal mean values)</li><li>Linear scaling of the above time-series to match the reported national annual mean hydropower production</li></ol><p><strong>4. Solar power_Time-series_Excel</strong></p><p>This folder contains the following files:</p><ol><li>A data report describing how Copernicus ERA5 data has been used to estimate solar radiation density and conversion to panel power for different panel types (Readme_Accessing_Solar_Data)</li><li>Excel sheets with power density time series for the incident solar radiation (cSolarTimeSeries_ssrd24.xlsx) and two panel types (cSolarTimeSeries_ssrd24, cSolarTimeSeries_CdTe24). The values represents 24h averages.</li></ol><p><strong>5. Time-series of 1055 regions </strong></p><p>This folder contains the daily time-series used in a full assessment of solar, wind and hydropower system based on the above solar power, wind power and hydropower.</p><p>A readme file is also provided.</p><ol><li>Various information, including electric consumption data</li><li>Data on electric consumption extracted on 25/10/2022 13:35:55 from [ESTAT]</li><li>Matlab file used to derive average monthly consumption pattern based on 6a)</li><li>Energy storage capacity in Euopean Hydropower according to data collected by Prof. em. Killingtveit.</li><li>National hydropower production used to scale hydropower estimated at GranD stations to the national production level</li><li>Simulation results used for Figure 3</li></ol><p><strong>6. Various information including electric consumption</strong></p><p> </p><p><strong>References</strong></p><p>Beames at al., 2019. Global Reservoir and dam (GRanD) Database: technical documentation – version 1.3. February 2019. <a href="http://globaldamwatch.org/">http://globaldamwatch.org</a></p><p>Huld, T. and Ana M.G. Amillo. Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum. In: Energies 8 (2015), pp. 5159{5181. doi: <a href="http://dx.doi.org/10.3390/en8065159">http://dx.doi.org/10.3390/en8065159</a>.</p><p>Huld, T.A.; Friesen, G.; Skoczek, A.; Kenny, R.A.; Sample, T.; Field, M.; Dunlop, E.D. A, power-rating model for crystalline silicon PV modules. Solar Energy Mater. Solar Cells 2011, 95, 3359–3369.</p><p>Hundecha, Y., Arheimer, B., Donnelly, C. and Pechlivanidis, I.: A regional parameter estimation scheme for a pan-European multi-basin model, Journal of Hydrology: Regional Studies, 6(Supplement C), 90–111, doi:<a href="https://doi.org/10.1016/j.ejrh.2016.04.002">https://doi.org/10.1016/j.ejrh.2016.04.002</a>, 2016.</p><p>Wörman, A., Lindström, G., Riml, J., 2017. "The Power of Runoff", J. Hydrology, 548(2017): 784-793, dx.doi.org/10.1016/j.jhydrol.2017.03.041</p>
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