*3.5. Scenario Definition*

To identify which of the seven user groups from the traffic analyses are particularly relevant for the investigation of the synergy effects between e-mobility and PV potential, the potential for the direct use of PV production per user group is examined. For the case study presented here, the user groups "trip for shopping", "trip for execution" and "trip to work" have the highest energy demand in relation to the total energy demand of e-mobility based on the traffic analysis. Since the user groups "trip for shopping" and "trip for execution" have the same characteristic load profile, only the user groups "trip for shopping" and "trip to work" are shown as examples in Figure 9. The figure is used to visualize the determination of the potential for the direct use of PV production for the user groups "trip for shopping" and "trip to work". For the user group "trip to work", the user groups "trip to work with a private car" and "trip to work with a company car" are aggregated due to their similar behaviour. The characteristic of the synthetic charging load profile for the user group "trip to work" is strongly dependent on the sector-specific jobs within the considered area. Therefore, the evening peak, which occurs due to an existing shift operation, can be omitted when looking at another area. The number of peaks for the user group "trip for shopping" is independent of the area under investigation. This means that as soon as there are shopping facilities in the area under consideration, and thus a shopping user group exists, a peak at noon and in the afternoon or evening occurs. In the example shown, around 80% of the energy demand of the user group "trip to work" can be directly covered by PV production, while only 60% can be directly supplied for the user group "trip for shopping".

**Figure 9.** Generic example of a load profiles of the user groups "trip to work" and "trip for shopping" as well as a PV production profile.

The analysis of all user groups indicates that with an increasing number of charging processes during the day, the direct use of the PV production increases and thus the potential. Based on the user behaviour and the share of PV use, the user group "trip to work" can be identified as the user group with the highest potential. Therefore, we have focused on the user groups "trip to work with a private car" and "trip to work with a company car" in combination with a trip chain model to investigate the synergy effects between e-mobility and PV potentials in the case study presented here. Using the trip chain model, the charging energy of the other five user groups is accumulated into these two user groups. Since the number of weekend trips for these user groups is decreasing significantly and it should be ensured that all distances travelled at the weekend are also taken into account, the user group "trip home" is also considered at the weekend.

As already mentioned, the scenarios differ among each other in the penetration of EV and PV. For this purpose, the penetration of EV and PV is varied by 0%, 20%, 40%, 60%, 80% and 100% respectively. In a scenario, all cells have always the same penetration of EV and PV, this means for example that all cells have a penetration of 20% PV and a penetration of 80% EV. This assumption was made, because there are no statistical distributions for urban areas that allow an individual penetration for each cell. Furthermore, two different charging powers (3.7 kW and 11 kW) are considered. For both charging powers, a distinction is made between charging strategy 1 (uncontrolled charging) and charging strategy 2 (controlled charging).

The reference scenario describes the status quo (without PV Potential and future e-mobility) of the medium-voltage grid to be examined. The overview of all simulated scenarios is given in Appendix B, Table A1.
