The transition towards e-mobility represents a crucial cornerstone to fulfill global and national climate protection regulations. However, the integration of future electric vehicles into the power system must be planned adequately to prevent grid restrictions in the upcoming years. On this account, the presented thesis initially examines the suitability of conventional grid simulation methods applied to identify future grid reinforcement needs. Considering static deterministic grid simulations, conventional approaches to model grid customers’ electrical loads neglect temporal interdependences between different customer classes (e.g., households and electric vehicles). Furthermore, most approaches applied in the current state of research underestimate the grid customers’ coincidence at the end of the grid’s feeders. Consequently, future grid conditions caused by future electric vehicles, photovoltaic modules, or electric heat pumps are misjudged. In addition, the presented thesis demonstrates how to accurately quantify future grid reinforcement measures and costs in a large-scale area. Due to the heterogeneity of real-life grids, the simulation of a few individually selected grids and the scaling of their results might lead to erroneous assessments. In fact, several thousand low-voltage grids must be simulated to quantify total grid reinforcement costs adequately. This thesis develops and presents a fully automated large-scale grid simulation tool to overcome the highlighted shortcomings. The developed tool allows the simulation of several thousand grid structures while keeping the required computing time adequate. Furthermore, it uses novel coincidence factors, modeled and validated in this thesis, to take temporal interactions between all customer classes into account. Thereby, future grid conditions are estimated correctly, and grid reinforcement costs are accurately quantified. Besides identifying future grid restrictions, the presented thesis analyzes how various e-mobility use cases can be integrated into the existing distribution grid in a grid-friendly way. Considering electric vehicles charged at home or work, classic grid reinforcement measures can successfully be prevented by the analyzed voltage-controlled measures. However, limiting the available charging power, e.g., by implementing adequate charging tariffs, is the most effective measure to prevent future grid congestions. Furthermore, decentralized energy storage systems enable the grid-friendly supply of e-mobility use cases with strict schedules, e.g., electric busses, requesting high-power charging to fulfill their mobility needs. Implementing the findings, tools, and methods acquired and developed in this work will increase the accuracy and the level of detail of future grid planning processes. Thereby, this thesis helps design the future power system more adequately to enable the integration of future e-mobility.