4.2.1. Charging Strategy 1 – Uncontrolled Charging

The number of overloaded lines of the 5 kV voltage level with corresponding penetration of EV and PV for the charging capacities 3.7 kW and 11 kW and charging strategy 1 is shown in Figure 11. Since a penetration of 0% EV only takes into account the demand for e-mobility that is already present in the status quo of the power grid, no additional charging power is assigned to these scenarios and they are therefore referred to as without EV. With increasing EV and PV penetration as well as charging power, the number of overloaded lines increases. The use of PV potentials without e-mobility leads to line overloads at penetration rates of 40% PV and higher. E-mobility without PV potential, too, only causes line overloads when the penetration rate is over 40% and the charging power is 11 kW. With a penetration of 20% PV, the line overload caused by a penetration of 40% EV and a charging power of 11 kW can be avoided.

**Figure 11.** Number of overloaded lines of the 5 kV voltage level for charging strategy 1.

In addition to the number of overloaded lines, the model also determines which lines are affected and where they are located in the power grid model and geographically. Figure 12 shows the cell division for the energy cells of the city of Leoben and the geographical location of the 13 overloaded lines, with the six most stressed lines in red. The identification of the most stressed lines is based on the maximum utilisation of the overloaded lines and the duration of the overload for all scenarios. These overloaded lines are also illustrated in Figure 10, which shows the grid plan of the cell-based grid model.

**Figure 12.** Cell division of the city of Leoben and detailed view with geographical location of the identified overloaded lines - in red: most stressed lines.

With the exception of the already mentioned avoidance of line overload at a penetration of 40% EV with a penetration of 20% PV, the different combinations of the penetrations of PV and EV do not lead to a reduction of the number of overloaded lines. Therefore, according to Figure 11 there are no obvious positive synergies between e-mobility and PV potential for charging strategy 1 for the number of overloaded lines. To investigate the interaction between e-mobility and PV potential in more detail, we determine the maximum line utilisation and the duration of overloads. For this purpose, we examine when the maximum utilisations of the lines occur and whether this is caused by the penetration of EV or PV. As illustrated for the most stressed lines in Figure 13, line overloads occur mainly in winter as well as in April and June. While the overloads in winter are due to load

peaks of e-mobility, those in April and June are mainly due to production peaks of PV potentials. Furthermore, the worst-case weeks are identified based on the time of occurrence of the maximum load. Those worst-case weeks, for example, are used to compare scenarios with uncontrolled and controlled charging (charging strategy 1 vs. charging strategy 2).

**Figure 13.** Month of occurrence for the maximum line utilisation of all scenarios with charging strategy 1 during the simulation period of one year.

Two of the six most stressed lines are selected to analyse the duration of the line overloads in detail. For this purpose, annual duration curves are modelled for each line and scenario based on the sorted utilisation of the line. While line 1 is overloaded for the first time at a penetration of 40% PV, the overload on line 5 occurs first at a penetration of 40% EV and a charging power of 11 kW. Since the duration of overload is less than 10 h for both lines, the next higher penetration of PV and EV is chosen for demonstration purposes of the interaction between e-mobility and PV potential. Table 2 shows the comparison of the duration of the line overloads of lines 1 and 5 depending on the charging power. For line 1 we investigate the influence of an increasing e-mobility penetration at a fixed penetration of 60% PV. With a penetration of 60% PV and the current demand, the duration of overload is 506.5 h per year. By integrating a penetration of 100% EV, the duration of overload can be reduced to 258.5 h per year at a charging power of 3.7 kW and 206.75 h per year at a charging power of 11 kW respectively. The comparison of the duration of overloads for a penetration of 100% EV shows that at a charging power of 11 kW, the reduction in the duration of the overload is greater by around 50 h per year than at a charging capacity of 3.7 kW. This higher reduction is due to the increased power requirement of e-mobility at a charging power of 11 kW compared to 3.7 kW. As a result of this increase, the production surplus at the peak of the PV potential is reduced and thus the duration of the line overload.


**Table 2.** Duration of overloads (utilisation > 100%) for the lines 1 and 5 – charging power 3.7 kW and 11 kW, using charging strategy 1.

However, an increased power requirement of e-mobility due to the higher charging power can also lead to an increasing grid load. This increase in grid load can be seen in Table 2 for line 5. While the duration of the line overload at a fixed penetration of 60% EV and a charging power of 3.7 kW is reduced from 154.50 h per year without PV potential to 39.00 h per year with a penetration of 100% PV, the duration of the overload at a charging power of 11 kW is reduced from 155.75 h per year only to 67.00 h per year.

The duration of the overload shown in Table 2 is graphically illustrated in Figure 14 using the sorted annual duration curves of the line utilisation for the lines 1 (a) and 5 (b) for the range 0 to 600 h at a charging power of 3.7 kW. In addition to the already described reduction of the duration of the line overload, the decrease of the utilisation can also be seen. For the shown range (0 to 600 h) of the sorted annual duration curves, the comparison of the utilisation of line 1 at a penetration of 0% EV with the utilisation at a penetration of 100% EV shows a reduction of the utilisation between 6% and 19%. For line 5, a decrease in line utilisation of up to 15% is achieved for the range of 0 to 600 h of the sorted annual duration curve with an increase in penetration from 0% PV to 100% PV.

**Figure 14.** Annual duration curve of the utilisation for the period 0 to 600 h for 3.7 kW charging power and charging strategy 1 (**a**) line 1 and a fixed penetration of 60% PV (**b**) line 5 and a fixed penetration of 60% EV.

As demonstrated by the example of the lines 1 and 5, the higher the penetration of EV with fixed penetration of PV, the lower the duration of the line overloads as well as the maximum utilisation and vice versa. The charging power also has a considerable influence on the line utilisation and thus on the overload of the lines. While a higher charging power in summer further reduces the power surplus caused by PV production and thus relieves the grid, in winter it can lead to higher and longer overloads.

In the scenarios presented here, the same penetration of EV and PV is considered for all cells, therefore in some cells large differences between PV production and the power requirements of e-mobility occur. For further reduction or possibly complete avoidance of overloads, it is necessary to find the optimal balance between PV production and the power requirements of e-mobility for each cell.

### 4.2.2. Charging Strategy 2—Controlled Charging

A comparison of the scenarios with charging strategy 1 and 2 based on two worst-case weeks is carried out to determine the influence of controlled charging on the grid utilisation compared to uncontrolled charging and the resulting synergy effects between e-mobility and PV potential. For this purpose, one week is in winter (20 January to 26 January) and one in summer (2 June to 8 June). The most stressed days of this week are 24 January and 2 June, respectively. The comparison of the line utilisation in Figure 15 shows for line 1 for a penetration of 40% EV and 60% PV and a charging power of 3.7 kW that controlled charging can lead to both, a reduction but also to an increase in the utilisation of the line. By applying charging strategy 2, the charging processes during the day are shifted into the peak of the PV production; thereby an increase of the power requirement of e-mobility can occur within the period of the peak of the PV potentials as shown in Figure 16.

**Figure 15.** Comparison of the utilisation of the line 1 for a penetration of 40% EV and 60% PV with 3.7 kW charging power and charging strategy 1 and 2 for (**a**) 24 January (**b**) 2 June.

**Figure 16.** PV potential profile and synthetic charging load profiles for the e-mobility with 3.7 kW charging power, charging strategy 1 and 2 for cell 13 (**a**) 24 January (**b**) 2 June.

This increase in power demand leads to an increased line utilisation if the PV potential is not sufficient, as demonstrated in Figure 15a for the 24 January. On 2 June the PV potential is significantly higher than the power demand from EV (see Figure 16b), which means that the line utilisation can be reduced at noon. As shown in Figure 15b, however, it is by far not possible to avoid the overloads. The difference of about 12 MW between the maximum peak value of the PV production on the 2 June and that on the 24 January, shown in Figure 16, does not allow any conclusions concerning other days and their maximum peak values due to the PV potentials modelled in dependence on irradiation and temperature data.

The utilisation of line 1 cannot be directly compared with the difference between the EV charging load and the PV potential of cell 13 (Figure 16). On the one hand, the utilisation of the line depends on the balance of the total demand (e-mobility and other consumption) as well as the PV potential and the resulting power requirement or power surplus of cell 13. On the other hand, this line is in a closed ring system and the line utilisation is therefore dependent on several other cell balances, see Figure 10 (cell-based grid model).

The described increase or reduction of the utilisation of line 1 by controlled charging can be transferred to other lines. The amount of this positive as well as negative effect is strongly dependent on the demand of e-mobility and the PV potential of the respective cell as well as the grid topology (mesh, ring, radial) in which the investigated line is located.

Figure 17 shows the sorted duration curves of the line utilisation of line 1 for different scenarios for both worst-case weeks. In addition to the charging strategy, the scenarios differ between the penetration of 40% and 100% EV. Furthermore, a fixed penetration of 60% PV and a charging power of 3.7 kW is specified. As shown in Figure 17a, in winter with a fixed penetration of 60% PV, the utilisation on line 1 for a penetration of 100% EV is higher than for 40% EV regardless of the charging strategy. The increase in line utilisation is caused by the lower PV potential in winter compared to summer (see Figure 16). The increase in the power requirement of e-mobility within the peak of the PV potential resulting from controlled charging leads to an increase in the line utilisation at a penetration of 100% EV compared to uncontrolled charging. Due to the higher PV potential in summer, a higher production surplus is produced in the worst-case week in summer with a penetration of 40% EV, compared to a penetration of 100% EV. This surplus leads to an increase in line utilisation, as shown in Figure 17b. The duration of the overload is increased by about one hour at a penetration of 40% EV by controlled charging, while controlled charging has no effect on the duration of the overload at a penetration of 100%.

**Figure 17.** Sorted duration curve of the utilisation of line 1 at a fixed penetration of 60% PV and 3.7 kW charging power for the selected worst-case weeks (**a**) winter (**b**) summer.

Regardless of the charging strategy, the comparison of the line utilisation of a worst-case week in winter with one in summer clearly shows the need to consider seasonal effects. A fixed penetration of PV generates significantly more power in summer than in winter (Figure 16). The high penetrations of EV, which are used to reduce line overloads (caused by PV potentials), can lead to increased grid load in winter due to insufficient PV production. In order to avoid increased loads and to ensure sufficient reserves in the grid, the seasonal effect should therefore always be taken into account. Instead of completely expanding e-mobility in these cells, reducing the penetration of the PV potential, for example, could be the better solution.

Although the influence of controlled charging is small for the worst-case weeks presented here, controlled charging is most effective for the use of PV production in summer. However, it is important to find the optimal ratio between e-mobility and PV potential in order to make best use of the effect of reducing line utilisation in summer. In winter, controlled charging can also lead to higher line utilisation by shifting the charging processes into the peak of the PV, so the seasonal effect between PV production in summer and winter should be considered more closely.
