Analyzing the Impact of Electric Vehicles on Local Energy Systems using Digital Twins

TL;DR

This paper tackles how sector coupling, and in particular electric vehicle integration, alters local electricity demand and grid operation. It introduces a hybrid methodology that couples a city-scale digital twin of the energy system with a mobility demand generator, informed by a residential survey and the MiD travel dataset, to produce building-resolved EV charging profiles. An application in Haßfurt shows that EV home charging can raise annual grid consumption by about 78% (from 18.5 to 33.0 GWh), but this rise is substantially mitigated when rooftop PV and battery energy storage are installed across buildings, potentially restoring consumption to or below current levels. The work demonstrates a practical, data-driven framework for evaluating EV-related demand, PV self-consumption, and storage strategies, with implications for urban planning, grid management, and policy decisions regarding sector coupling and rooftop PV deployment.

Abstract

The electrification of the transportation and heating sector, the so-called sector coupling, is one of the core elements to achieve independence from fossil fuels. As it highly affects the electricity demand, especially on the local level, the integrated modeling and simulation of all sectors is a promising approach for analyzing design decisions or complex control strategies. This paper analyzes the increase in electricity demand resulting from sector coupling, mainly due to integrating electric vehicles into urban energy systems. Therefore, we utilize a digital twin of an existing local energy system and extend it with a mobility simulation model to evaluate the impact of electric vehicles on the distribution grid level. Our findings indicate a significant rise in annual electricity consumption attributed to electric vehicles, with home charging alone resulting in a 78% increase. However, we demonstrate that integrating photovoltaic and battery energy storage systems can effectively mitigate this rise.

Figures (5)

• Figure 1: Methodology and scope of this paper.
• Figure 2: Visualization of the mode choice model that is extracted from the MiD dependent on the vehicle sharing type, i.e., vehicle shared between household members (lime) or vehicle exclusively attributed to one person (dark green), including a reference from the literature (gray).
• Figure 3: Modeling of the EV inside the digital twin as finite-state machine.
• Figure 4: Aggregated output of the mobility demand processing. The blue line in the leftmost plot shows the percentage of vehicles parking at home for an exemplary weekday (Tuesday) as simulated. The two plots in the center visualize the histogram of the departure and arrival times of all vehicle tours over the complete sampled week. The rightmost plot shows the number of days a vehicle is used per week.
• Figure 5: Distribution of SSR and SCR (left plots) over all considered buildings with added EV in the scenarios with EV-addition. Center plot: Maximum residential demand per month for different scenarios. Rightmost plot: Sum of annual electricity consumption over all considered buildings, separated by the actual electricity consumed from the local grid (green) and the consumption covered by the local rooftop PV installation (light blue).