Potential power scenario for solar, wind and hydropower in Europe

Data for power scenario used to assess climate impact on solar, wind and hydropower over a 35-year historical period. Structure and content of data repository 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. 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: Virtual Energy Storage – Hydropower, DOI: 10.5281/zenodo.3706758 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. 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: Virtual Energy Storage - Wind power, DOI: 10.5281/zenodo.7749150 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: 1. Temporal mean power for solar-wind-hydro at 1,055 areas 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). 2. Geographical data This folder contains the following sub-folders and information: Shape files for the 1,055 areas depicted above (shapefile_solar_domain) Shape file of Europe and parts of the Middle East including different nations (Europe_Shapefile) An Excel file with geodata för the 1,055 areas (areas_points_land) 3. GranD_Hydropower time-series This folder contains the following sub-folders and information: A ReadMe file Temporal mean values of potential hydropower production estimated at GranD hydropower stations (Temporal mean values) Linear scaling of the above time-series to match the reported national annual mean hydropower production 4. Solar power_Time-series_Excel This folder contains the following files: 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) 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. 5. Time-series of 1055 regions 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. A readme file is also provided. Various information, including electric consumption data Data on electric consumption extracted on 25/10/2022 13:35:55 from [ESTAT] Matlab file used to derive average monthly consumption pattern based on 6a) Energy storage capacity in Euopean Hydropower according to data collected by Prof. em. Killingtveit. National hydropower production used to scale hydropower estimated at GranD stations to the national production level Simulation results used for Figure 3 6. Various information including electric consumption References Beames at al., 2019. Global Reservoir and dam (GRanD) Database: technical documentation – version 1.3. February 2019. http://globaldamwatch.org 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: http://dx.doi.org/10.3390/en8065159. 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. 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:https://doi.org/10.1016/j.ejrh.2016.04.002, 2016. 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本数据集为电力场景数据，用于评估35年历史时段内气候变化对太阳能、风能和水电的影响。 数据仓库的结构与内容 此处我们提供了Wörman等人发表于《Nature Communications Engineering》的学术论文《可再生能源组合的大陆互补性》（Continental complementarity of renewable energy mixes）研究中所用数据的相关信息。 水文气候数据取自哥白尼欧洲中期天气预报中心（Copernicus ECMWF）数据库，覆盖欧洲大部与中东地区，总面积达13106平方千米。水电潜力基于GranD数据库（GranD database）中收录的水电站点位计算得到（Beams等，2019）。径流量通过E-HEPE模型（Hundecha等，2016）计算得出，以此估算各水电站点位的水电潜力（Wörman等，2017），并将估算结果推广至覆盖欧洲的1055个均匀分布子区域中的362个（详见下图）。本仓库中水电数据的原始数据可通过以下链接获取： 虚拟储能——水电，DOI: 10.5281/zenodo.3706758 1979年1月1日至2020年12月31日的地表向下太阳辐射（Surface Solar Radiation Downwards, SSRD）日数据取自哥白尼欧洲中期天气预报中心（Copernicus ECMWF）数据库，将其转换为入射到固定朝南、倾角等于所在纬度的光伏面板的辐射量，随后依据Huld等（2011、2015）的方法转换为光伏电力潜力。考虑太阳高度角与方位角的变化，对24小时（涵盖夜间与日间）的电力潜力取平均值，并针对1055个子区域中的995个完成聚合。相关数据报告可在目录4中获取。 与风电潜力相关的气象数据取自ERA5，这是欧洲中期天气预报中心（ECMWF）通用环流模型的再分析产品，可在哥白尼气候数据服务中心（Copernicus Climate Data Store）获取。作为对照，研究同时从Merra 2与JRA 55获取数据，基于“世界风电场数据库”收录的20010个陆上风电场点位，推导得到1979年1月1日至2019年12月31日的风速时间序列。本仓库中光伏电力数据的原始数据可在本仓库目录4中获取。本仓库中风电数据的原始数据可通过以下链接获取： 虚拟储能——风电，DOI: 10.5281/zenodo.7749150 代表太阳能、风能与水电的电力场景数据分为5个文件夹，分别存储不同变量及其自然地理特征相关信息。每个文件夹均提供ReadMe文件，用于说明各文件的格式： 1. 1055个区域的太阳能-风能-水电时序平均功率 该文件夹提供三种可再生能源的平均功率，对应地理分区为（Mean_Hydro、Mean_Solar、Mean_Wind）。本目录还包含指向地理数据文件的区域ID信息，以及区域数值与坐标信息（ReadMe_mean power CSV）。 2. 地理数据 该文件夹包含以下子文件夹与信息： - 上述1055个区域的形状文件（shapefile_solar_domain） - 包含不同国家的欧洲及中东部分区域形状文件（Europe_Shapefile） - 包含1055个区域地理数据的Excel文件（areas_points_land） 3. GranD水电时间序列 该文件夹包含以下子文件夹与信息： - ReadMe文件 - GranD数据库水电站点位估算的潜在水电生产时序平均值（Temporal mean values） - 上述时间序列的线性缩放结果，用于匹配报告的国家年度平均水电产量 4. 光伏电力时间序列_Excel 该文件夹包含以下文件： - 数据报告，说明如何利用哥白尼ERA5数据估算太阳辐射密度，并将其转换为不同类型光伏面板的面板电力（Readme_Accessing_Solar_Data） - 包含入射太阳辐射（cSolarTimeSeries_ssrd24.xlsx）与两种光伏面板类型（cSolarTimeSeries_ssrd24、cSolarTimeSeries_CdTe24）的功率密度时间序列的Excel表格，所有数值均为24小时平均值。 5. 1055个区域的时间序列 该文件夹包含用于全面评估太阳能、风能与水电系统的每日时间序列，数据基于前述太阳能电力、风电电力与水电数据构建。本文件夹同样提供ReadMe文件。 其他各类信息（含电力消费数据） - 2022年10月25日13:35:55从[ESTAT]提取的电力消费数据 - 基于6a)推导月度平均消费模式的Matlab文件 - 依据Killingtveit荣誉教授收集的数据得到的欧洲水电储能容量 - 用于将GranD数据库水电站估算的水电产量缩放至国家产量水平的国家水电生产数据 - 用于绘制图3的模拟结果 6. 包含电力消费数据在内的各类其他信息 参考文献 Beames等，2019。全球水库与大坝（GRanD）数据库：技术文档——版本1.3。2019年2月。http://globaldamwatch.org Huld, T. 与Ana M.G. Amillo。《大地理区域内光伏组件性能估算：辐照度、气温、风速与太阳光谱的作用》，载于《Energies》8(2015)，第5159-5181页。doi: http://dx.doi.org/10.3390/en8065159. Huld, T.A.、Friesen, G.、Skoczek, A.、Kenny, R.A.、Sample, T.、Field, M.、Dunlop, E.D.。《晶体硅光伏组件功率额定模型》，《Solar Energy Materials and Solar Cells》2011, 95, 3359–3369. Hundecha, Y.、Arheimer, B.、Donnelly, C.与Pechlivanidis, I.：《泛欧洲多流域模型的区域参数估算方案》，《Journal of Hydrology: Regional Studies》6(增刊C), 90–111, doi:https://doi.org/10.1016/j.ejrh.2016.04.002, 2016. Wörman, A.、Lindström, G.、Riml, J., 2017。《径流的力量》，《Journal of Hydrology》548(2017): 784-793, dx.doi.org/10.1016/j.jhydrol.2017.03.041