**1. Introduction**

To contribute to the reduction of CO2 emissions, it is necessary to change over to alternative drive systems such as electric vehicles (EV) in the transport sector. The change to e-mobility leads to an increasing electricity demand, which can lead to a reduction of grid stability and security of supply [1–5]. The additional electricity demand of electric vehicles must be provided by renewable energy sources (RES) to achieve decarbonisation targets. The impact of the EVs electricity demand together with the fluctuating RES production can lead to additional challenges compared to a single consideration of e-mobility [6–9]. Munkhammar et al. [6] focused on home charging and investigated the interaction between the household power consumption, the electric vehicle home charging and the photovoltaic (PV) power production using a probability model. The investigation shows on the one hand the increasing peak load caused by EV charging and on the other hand, the time-shift between EV charging and PV power production. This time-shift is based on the fact that the PV power production and the resulting peak is available during the day, while the peak caused by charging at home occurs in the evening. To reduce this shift and thereby increase power self-consumption, a change in the user behaviour of EV drivers or the application of demand side measures becomes necessary. For a better use of the PV power production, stays during the day should be used for charging the EVs, such as charging at work and charging commercially. [10] Since the load profiles of e-mobility and thus the required charging infrastructure are strongly dependent on respective user behaviour of EV drivers [1,11], load shift potentials of electric vehicles for different available charging infrastructures (e.g., for charging at home vs. for charging at work) are determined in the references [10,12]. Even with an uncontrolled charging, the PV power production can be better utilised when charging at work compared to charging at home. This is caused by the fact that charging at work is usually performed during the day, while charging at home is generally performed in the evening-hours and at night. [10] Babrowski et al. [12] conclude that the greatest potential for controlled charging is in the area of workplace charging using PV power production.

Despite the large number of presented approaches and results on the interaction between PV power production and uncontrolled and controlled EV charging, to the best of our knowledge, the impact of this interaction on the distribution grids have not yet been sufficiently studied. Therefore, the case study presented in this paper will focus on charging at work and the impact of different penetrations of work-charged EVs and PV potentials on an urban medium-voltage grid. For this purpose, we have developed a grid model for the medium-voltage grid of a medium-sized city based on a cellular approach, using the example of Leoben in Austria. The application of the cellular approach simplifies the complex grid structure and allows reduced calculation times. The approach is therefore suitable for usually time-consuming time resolved load flow calculations, e.g., with annual load profiles. [11] Besides the modelling of existing consumer and producer profiles, we determine production profiles of PV potentials and synthetic charging load profiles of e-mobility. While Su et. al. [13], for example, characterise PV production profiles by averages of "rainy days", "cloudy days" and "sunny days", the case study presented here determines the time-resolved production profiles of PV potentials (15-minute mean value) for each calendar day on the basis of irradiation and temperature data [14] and a solar roof register depicting the actual roof areas [15]. The determination of synthetic charging load profiles for e-mobility is based on traffic analysis, mobility pattern and statistical data. [11] The method for this determination, we have already shown in [11], has been extended in this work, among others, by the function of taking controlled charging into account. In comparison to Gnann et al. [10], where summer charging is shifted to midday and in winter the focus is on peak-shaving by charging at night, here a combination of the summer and winter strategy for controlled charging is applied, regardless of the season. The method distinguishes between shifting the charging process into the peak of the PV production profile or, distributing the charging processes over a defined period of time. For each charging process, it is decided separately if it can be shifted under the defined parameters (for example duration of stay and distance travelled) into the intended period of controlled charging. By comparing the scenarios for uncontrolled charging with those of controlled charging, we investigate in addition to the energetic benefits for the direct use of PV production, whether controlled charging can prevent the negative effects of uncontrolled charging on the power grid. The focus of this analysis is therefore to determine the benefits of controlled charging on the existing infrastructure considering today's mobility behaviour, while Zhang and Chen [16] discuss smart charging management based on mobility behaviour and regional energy prices. Using the cell-based grid model and the determined annual load and production profiles, we perform time resolved load flow calculations for different scenarios. The scenarios differ between the penetrations of e-mobility and PV potentials, the charging power as well as controlled and uncontrolled charging. Based on the results of the load flow calculations, we present grid-side synergy effects and energy-related key performance indicators between e-mobility and PV potentials for the different scenarios.

### **2. Data Description**

In this section, all data required for the case study and its preparation is presented. Starting with the description of the medium-voltage grid of the city of Leoben, the data required to model the synthetic charging load profiles for e-mobility are presented. This includes traffic analyses, mobility patterns and vehicle-related specifications. Finally, the fundamental data for modelling the PV potential profiles are described.
