In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previous research work utilizes data that is not always complete or sufficiently granular in space. This is why this paper presents a methodology which allows a holistic determination of residential supply tasks based on orthophotos. To do this, buildings are first identified from orthophotos, then residential building types are classified, and finally the electricity demand of each building is determined. In an exemplary case study, we validate the presented methodology and compare the results with another supply task methodology. The results show that the electricity demand deviates from the results of a reference method by an average 9%. Deviations result mainly from the parameterization of the selected residential building types. Thus, the presented methodology is able to model supply tasks similarly as other methods but more granular.
翻译:为实现气候目标,个人流动性的电气化至关重要。然而,电动车辆的电网整合由于高电压和同时性而给配电网络带来了挑战。为了调查研究中的这些挑战,需要建模网络参考供应任务。以前的研究工作使用的数据并不总是完整,或者空间中的颗粒不够充分。这就是本文件提出一种方法的原因。为了做到这一点,首先从orthophotos中确定住宅供应任务,然后对住宅建筑进行分类,最后确定每座建筑的电力需求。在一项示范性案例研究中,我们验证了所提出的方法,并将结果与另一种供应任务方法进行比较。结果显示,电力需求与参考方法的结果相偏离,平均为9%。偏离主要由于选定住宅建筑类型参数化的结果。因此,所提出的方法能够将供应任务与其他方法一样,但更多的是颗粒。