A machine learning approach to model the future distribution of e-mobility and its impact on the power grid

Abstract

Abstract It is to be expected that there will be a shift toward electromobility with regard to private passenger cars in the coming years. This will oblige the respective power grid providers to upgrade their networks in future years. So that grid operators can plan and operate their grids to meet future needs, they have to have as complete information as possible about the loads they will be required to handle. Depending on voltage level, geographic location, general grid load, and spread of e-mobility, the situation will vary. The assumption explored in this paper is that external factors influence the distribution of EV chargers. As a second task, the impact on the power grid is simulated by means of various scenarios on the basis of this identified distribution, with the focus on low voltage (LV) grids. Sociodemographic data is used as a geographic grid to determine potential distribution. For this, machine learning methods from the field of “Species Distribution Modeling” are applied for a prospective distribution concept. Using this distribution model, the results of simulation of power grid utilization reveal vulnerabilities scattered around the networks. It is shown that e-mobility will, in the future, present a challenge for power grid operators, for which solution concepts are needed.

Keywords

• E-mobility
• Distribution model
• Species distribution modeling
• Wallboxes
• Power grid capacity utilization
• LV grids