论文标题
从经验数据中创建电池电动车时间序列的开放工具 - emobpy
An open tool for creating battery-electric vehicle time series from empirical data -- emobpy
论文作者
论文摘要
对未来电池电动车辆将如何与电力部门互动的未来舰队具有重大的研究兴趣。为此,各种类型的能源模型取决于有意义的输入参数,特别是车辆移动性的时间序列,驱动电力消耗,电网可用性或电网电力需求。由于此类数据的可用性受到很高的限制,因此我们介绍了开源工具EMOBPY。基于移动性统计,车辆的物理特性以及其他可自定义的假设,它得出了可以在广泛的模型应用中轻松使用的时间序列数据。为了进行插图,我们为德国创建并表征了200个电池电动车辆配置文件。根据一天中的小时,一百万辆汽车的车队的网格可用性在5到7吉瓦之间,因为车辆大部分时间都在停车。四个示例网格需求时间序列序列说明了平衡充电策略的平滑效果。
There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. To this end, various types of energy models depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 battery-electric vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.