4.3.3. Charging Strategy 2 – Controlled Charging

To determine the influence of controlled charging on the key performance indicators, two scenarios are compared, which differ only in the charging strategy. The basis for the results presented below is based on scenarios, which assume a fixed penetration of 60% PV, an increasing penetration of EV and a charging power of 3.7 kW. For the analysis, we take the cells 4, 5, 13 and 18 into account. Figure 24 shows the maximum and minimum relative deviation of selected cells that occur within the comparison of DSG, DSS and SCR of the respective scenarios. The relative deviation varies between −0.16 and 0.31 and is calculated for the respective key performance indicators (KPI) as follows:

**Figure 24.** Maximum and minimum relative deviation of selected cells for a fixed penetration of 60% PV and a variation of the penetration of EV for 3.7 kW charging power (**a**) DSG (**b**) DSS (**c**) SCR.

Since the focus of the presented case study is on the user group "charging at work" and these charging processes take place mainly during the day, the daily energy demand of e-mobility remains almost constant on weekdays. On weekends, the user group "trip home" is also selected, where, as already mentioned, charging takes place mainly in the evening or at night. To avoid load peaks of the e-mobility in the evening, in charging strategy 2 these charging processes are distributed over the night, this can lead to shifts in energy demand from one day to the other. Among the other effects, this shift leads to the relative deviations of the DSG as shown in Figure 24a. Since the DSS and SCR calculate the directly consumed part of the locally produced energy on the basis of the total energy demand or the total locally produced energy, the positive relative deviations shown in Figure 24b,c are based on the time shift of the charging processes into the peak of the PV potential. The negative relative deviations, which mean, that controlled charging has a negative effect compared to uncontrolled charging, can be caused by both methods, shifting of the charging processes into the peak of the PV and sequencing the charging processes overnight. For instance, through the distribution of charging processes overnight, the evening peak of e-mobility is reduced. Since in summer there is often sufficient PV production until the evening hours, this reduction of the evening peak can lead to a lower use of the PV potential and therefore to a decrease of DSS and SCR. As shown in Figure 24 for different cells, the benefit of controlled charging (charging strategy 2) depends on the one hand on the user behaviour of the sector-specific jobs within the cells and on the other hand from the balance between energy demand

and the PV potential. This means that the more charging processes take place in the early morning hours and can be shifted into the peak of the PV potential, the higher the DSS and SCR (positive relative deviation) with sufficient PV potential. However, if the ratio of EV to PV is very high, as for example in winter, the shifting of the charging processes has a minor influence on the DSS and SCR. Therefore, in addition to considering the optimal ratio between e-mobility and PV potential, the actual benefits of controlled charging should also be evaluated. However, applying controlled charging leads to a minor positive effect.

## **5. Conclusions**

This paper presents results from a case study for the city of Leoben (Austria), which investigates the synergy effects between e-mobility charged only at work and photovoltaic potentials. The basis for the determination of the grid-side synergy effects is a cell-based grid model. Taking into account the spatial cell division of the grid model, load and production profiles, synthetic charging load profiles for e-mobility and PV potential profiles are determined. For different scenarios (variation of the penetration of EV and PV, charging power, charging strategy), load flow calculations are performed for a simulation period of one year. The results of the load flow calculations are investigated regarding grid-side synergy effects. Additionally, four key performance indicators (residual load, DSG, DSS and SCR) are used to analyse energetic synergy effects between e-mobility and PV potentials.

By using the cellular approach to develop a grid model, there is always a compromise between accuracy, resolution and calculation time. By aggregating the original power grid into energy nodes, this grid model is not suitable for detailed planning, but provides an overview of the effects caused by increasing e-mobility and PV potential. The possibility of carrying out simulations on the grid model for one year means that annual trends, worst-case days and weeks as well as countermeasures can be derived and analysed. As a result, these findings can be used as a basis for detailed studies on the original power grid.

The analyses of the grid-side effects show that with increasing penetration of EV and PV as well as increasing charging power, the grid utilisation and thus the number of line overloads increases. Although line overloads cannot be avoided completely, the maximum line utilisation and the duration of the overloads can be reduced by the correct ratio between e-mobility and PV potential. By using controlled charging (charging strategy 2), the charging processes are shifted to the peak of the PV potential, therefore the power requirement of e-mobility can increase. On the one hand, this increase relieves the grid because it reduces the excess power of the PV potential and on the other hand, it leads to an increased grid load if there is not sufficient PV production available. The comparison of a worst-case week in winter with one in summer shows the need to consider seasonal effects during grid-side and energy analysis. For example, with fixed penetration of PV and increasing penetration of EV, production surpluses are reduced in summer, while increasing e-mobility in winter leads to an increase in line utilisation due to insufficient PV potential. However, if the PV potential is much higher than the demand for e-mobility, controlled charging has little impact on the reduction of line utilisation and thus on overloads caused by PV potentials. Furthermore, the four key performance indicators of the energy analysis demonstrate the importance of modelling PV potentials using real irradiation and temperature data. Besides the residual load, which indicates the fluctuating power surplus caused by the PV potential, DSG, DSS and SCR vary significantly between two consecutive days. The investigation of the residual loads of different scenarios shows that high negative residual loads, which lead to overloads mainly in summer, are reduced by an increasing penetration of EV while the penetration of PV remains the same. However, since the overloads cannot be avoided completely, further possibilities to reduce these high negative residual loads, like a reduction of the PV potential or the use of controlled charging need to be considered. A reduction of the PV potential as well as the increasing e-mobility lead to increasing positive residual loads, especially in winter on PV production weak days. The use of controlled charging can reduce both, negative and positive residual loads. While negative residual loads are reduced by shifting the charging processes into the peak of the PV potential, positive residual loads are reduced by sequencing the charging processes.

The analysis of grid and energy-side effects has shown the importance of balancing e-mobility and PV potential. The same penetration of EV and PV over all cells for the scenarios considered leads to large differences in the ratios between the energy demand of e-mobility and the PV potential of the individual cells. With penetrations of 60% PV and 100% EV, these ratios are in the range of 0.42 (cell 1) to 11.45 (cell 26) based on the annual energy production of the PV Potential and the annual energy demand of e-mobility. Due to this uneven distribution, for example, a penetration of 100% EV can lead to a line overload, whereas this penetration would be necessary to reduce a power surplus of the PV potential in another cell.

In summary, the key findings of the grid-side and energetic synergy effects between e-mobility charged only at work and PV potentials for the presented case study are as follows:


Although this study shows first trends for the synergies between e-mobility charged only at work and PV potentials, it is also subject to some limitations. Besides the limitations already mentioned, the lack of the possibility of detailed planning and constant penetration across all cells, the data basis is a dominating factor. The real irradiation and temperature data [14] and database for the electric power grid including consumption and generation data are from 2014. In addition, the traffic analyses and statistical data [23–25] used refer to today's mobility behaviour and thus mainly to vehicles with combustion engines. New technologies open up new possibilities, which influence mobility behaviour and therefore change it. To model the synthetic charging load profiles, the vehicles registered in Germany up to 2018 [27] (battery capacity, average energy consumption and charging efficiency) are used. Furthermore, the focus of the presented case study is on a grid-side and energetic consideration, this means that no ecological and economic factors are taken into account. In the area of controlled charging, the trends of line overloads identified in this case study and the associated necessary grid expansion should be analysed in combination with economic and ecological strategies for controlled charging. Furthermore, the use of storage systems at strategic grid points to avoid overloads should be investigated in further studies. Due to the limiting factor of a constant penetration across all cells, following studies should analyse and determine the optimal ratio between e-mobility and PV potentials at cell level to define the scenarios. Based on this optimum per cell, the impact on the power grid should be analysed to verify the hypothesis that overloads can be avoided. The possibility of increasing the determined optimal penetration rate of EV or PV by controlled charging or by the use of storages should also be investigated.

**Author Contributions:** Conceptualization, J.V.; methodology, J.V., T.K.; software, J.V.; validation, J.V.; formal analysis, J.V.; investigation, J.V.; data curation, J.V., U.B.; writing—original draft preparation, J.V., U.B.; writing—review and editing, J.V., T.K.; visualization, J.V.; supervision, T.K.; funding acquisition, T.K.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Austrian Federal Ministry of Transport, Innovation and Technology via the FFG program "Energie der Zukunft", grant number 854637 and by the "Klima- und Energiefonds" via the FFG program "Leuchttürme eMobilität", grant number 865447.

**Conflicts of Interest:** The authors declare no conflict of interest.
