Wind power has risen continuously over the last 20 years and covered almost 25% of the total German power provision in 2019. To investigate the effects and challenges of increasing wind power on energy systems, spatiotemporally disaggregated data on the electricity production from wind turbines are often required. The lack of freely accessible feed-in time series from onshore turbines, e.g., due to data protection regulations, makes it necessary to determine the power generation for a certain region and period with the help of numerical simulations using publicly available plant and weather data. For this, a new approach is used for the wind power model which utilizes a sixth-order polynomial for the specific power curve of a turbine. After model validation with measured data from a single wind turbine, the simulations are carried out for an ensemble of 25,835 onshore turbines to determine the German wind power production for 2016. The resulting hourly resolved data are aggregated into a time series with daily resolution and compared with measured feed-in data of entire Germany which show a high degree of agreement. Such electricity generation data from onshore turbines can be applied to optimize and monitor renewable power systems on various spatiotemporal scales.

中文翻译：

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高时空分辨率的德国风电生产建模

在过去的20年中，风能一直在持续增长，并在2019年占德国总电力供应量的近25％。为了研究增加风能对能源系统的影响和挑战，通常会时空分解风力涡轮机发电量的数据必需的。由于数据保护法规的原因，陆上涡轮机缺乏可自由获取的馈入时间序列，因此有必要借助使用公开可用的电厂和天气数据的数值模拟来确定特定区域和时段的发电量。为此，针对风能模型使用了一种新方法，该方法将六阶多项式用于涡轮机的特定功率曲线。在使用来自单个风力涡轮机的测量数据进行模型验证之后，我们对25,835台陆上涡轮机进行了仿真，以确定2016年德国的风力发电量。将每小时得到的每小时分解数据汇总为具有每日分辨率的时间序列，并与整个德国的实测馈入数据进行比较，结果显示高度一致。来自陆上涡轮机的此类发电数据可用于在各种时空范围内优化和监控可再生能源系统。