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On last year's E-Mobility Integration Symposium we demonstrated a comprehensive approach for conducting grid integration studies in the context of electric mobility, including probabilistic modelling of vehicle charging, an analysis of simultaneity factor approaches as well as automated grid reinforcement. In this paper we present further research, that puts a greater focus on modelling and identification of realistic worst-case grid situations caused by EVs, charging in real low-voltage (LV) grid models. The proposed probabilistic method uses a pool of EV charging profiles and repeatedly places them randomly in a grid. The resulting distribution of different grid states contains all information that is necessary to derive realistic worst-case situations as well as the probabilities of their occurrence. Since probabilistic methods require a relatively high amount of computational resources, we present an acceleration of this approach based on artificial neural networks. In addition we demonstrate improved procedures for using simultaneity factors in order to asses worst-case grid situations in LV grids. The presented methods aim at a good balance between accuracy and practical feasibility. Especially the simultaneity factor based method is suitable for application in conventional grid planning processes in any common commercial grid planning software, while achieving similar worst-case estimations compared to the probabilistic method. The result of this work is a versatile toolkit for a variety of different use cases. [ABSTRACT FROM AUTHOR]

We present results from a grid integration study conducted for the geographic region of the German federal state of Hesse. Goal of the study is to determine the expected cost for grid reinforcement and expansion in this region for different Distributed Energy Resource (DER) scenarios, to identify effective measures for an advanced DER integration and the identification of needs for action in the regulatory framework. In this paper we focus on the low- and mediumvoltage level and present the applied simulation methodology as well as some key results. [ABSTRACT FROM AUTHOR]