4.3.1. Interaction of Energy Surpluses between the Cells

The annual energy demand for each cell is determined on the basis of the synthetic charging profiles for e-mobility. Figure 18a illustrates the annual energy demand spatially resolved for a penetration of 100% EV. Depending on the cell, the annual energy demand is in the range of 20 MWh (cell 26) to 12,500 MWh (cell 13). Figure 18b shows the determined annual energy production of the PV potentials for a penetration of 100% spatially resolved. Depending on the cell, these vary between 290 MWh (cell 19) and 22,500 MWh (cell 13).

**Figure 18.** (**a**) Annual energy demand e-mobility and (**b**) annual PV potential at cell level for the city of Leoben.

In addition to the highest PV potential and the highest demand for work-related e-mobility, cell 13 is a very industry intensive cell. Furthermore, two of the three lines connecting cell 13 with its neighbour cells are among the most stressed lines (line 1 and line 3). The overloads of the lines 1 and 3 are mainly caused by the PV potentials. Moreover, lines 1 and 3 belong to a closed ring system in which another most stressed line (line 2) is located. Figure 19 shows a schematic section of the cell-based grid model shown in Figure 10 and the relevant load flows for the closed ring structure. The directions of the load flows correspond to those at the time of the maximum utilisation of line 1 for a penetration of 60% PV and 100% EV, a charging power of 3.7 kW and charging strategy 1. For this time step, the directions of the load flows indicate that the unused PV potential of cell 12 covers the electricity demand of the cells 5, 6 and 14 (ratio PV to EV < 1, see Figure 20) and leads to a feed into the 30 kV level.

**Figure 19.** Schematic grid section - illustration of the direction of load flows for a penetration of 60% PV and 100% EV, 3.7 kW charging power and charging strategy 1 for the time of maximum utilisation of line 1.

**Figure 20.** Energetic ratio of the annual energy production of 60% PV potential to the annual energy demand of 100% EV.

As already mentioned, the optimal ratio between e-mobility and PV potential is important to avoid overloads. For this purpose, Figure 20 shows the energetic ratio of the annual energy production of 60% of the PV potential to the annual energy demand for 100% e-mobility. The ratio varies between 0.42 (cell 1) and 11.45 (cell 26). While cell 13 has a ratio between e-mobility and PV potential of 1.07 for these penetrations, the neighbour cell (cell 12) with a ratio of 8.43 has a significant production surplus. This surplus is transported from cell 12 to cell 13 and from there via lines 1 and 3 to cells 5 and 10, where this surplus leads to overloads. To reduce overloads caused by the PV potential that cannot be avoided at a penetration of 100% EV, it is sometimes necessary to reduce the PV potential of these cells from a penetration of 60% to 40%.

An optimal energetic ratio between e-mobility and PV potential is, however, not sufficient to avoid overloads, since the annual energy demand of e-mobility does not allow conclusions to be drawn about the load curve and thus about the power ratio of the individual time step. This means that with an energetic optimum, there can be significant differences in the peak values between e-mobility and PV potential on the one hand, and a time shift between the two peaks on the other. Cell 13, for example, is characterised by shift operation and office workplaces. This characteristic results in a division of the

energy demand into four load peaks. This means that the optimum balance between e-mobility and PV potential must be analysed and determined in terms of both energy and power.

The self-consumption of the cells, for example from households, also has an important influence on the cell balance and the load flows between the cells. For this reason, the self-consumption of the cells is taken into account in our further energy analyses.

### 4.3.2. Charging Strategy 1 – Uncontrolled Charging

The calculation of the residual load enables the determination of the maximum power surpluses and power demand. While the energetic ratio between 60% PV potential and 100% e-mobility is 1.07, Figure 21 shows that the power profile of the PV potential is significantly higher than the load demand (e-mobility and consumption) of cell 13. Due to this high difference, the PV production profile during the day can be seen from the residual load curve in Figure 21. The significant fluctuations in the negative residual load are due to the use of real irradiation and temperature data during the modelling of the PV potentials. The profiles therefore consider, for example, seasonal effects or shading by clouds. Especially at night, there is a positive residual load and thus an increased power demand. As shown in Figure 21a, depending on the weather, negative residual loads of up to 5 MW also occur in the worst-case week in winter. The residual load for the represented worst-case week in summer (identified in the grid study) indicates that June 5 is a cloudy and rainy day due to the predominantly positive residual load also during the day (see Figure 21b). With increasing penetration of EV and a fixed penetration of 60% PV, the power surplus of the PV potential decreases and the load demand at night increases, regardless of the season.

**Figure 21.** Residual load of cell 13 for the selected worst-case weeks for a fixed penetration of 60% PV and a variation of the penetration of EV for 3.7 kW charging power and charging strategy 1 as well as for the reference scenario (without PV and EV) (**a**) winter (**b**) summer.

The sorted annual duration curve of the residual load of cell 13 in Figure 22 shows that in an observation period of one year, about 2/3 of the year show a positive residual load. In the last third, there is a rapid increase to a maximum power surplus of 11.8 MW at a penetration of 60% PV and no e-mobility. The influence of increasing e-mobility at a fixed penetration of 60% PV can be seen clearly from the annual duration curve. The curves are shifted in the direction of positive residual loads. In the case of a fixed penetration of EV and increasing penetration of PV, the annual duration curves behave in exactly the opposite way.

**Figure 22.** Sorted duration curve of the residual load of cell 13 for a fixed penetration of 60% PV and a variation of the penetration of EV for 3.7 kW charging power and charging strategy 1 and a simulation period of one year (8760 h).

The key performance indicators DSG, DSS and SCR are determined for each day for the simulation period of one year and are presented in Figure 23 for cell 13 at a fixed penetration of PV and increasing penetration of EVs.

**Figure 23.** Key performance indicator of cell 13 for the simulation period of one year and for a fixed penetration of 60% PV and a variation of the penetration of EV for 3.7 kW charging power and charging strategy 1 (**a**) DSG (**b**) DSS (**c**) SCR.

The DSG decreases with increasing penetration of EV at a fixed penetration of PV (Figure 23a) and vice versa. Due to the high-power profile of the PV potential compared to the synthetic charging load profile of cell 13, maximum DSG of up to four are possible (without EV). The DSS has its maximum in summer with up to 0.65. This means that up to 65% of the locally produced PV Potential can be consumed directly. The DSS behaves like the DSG, which means that the DSS decreases with increasing penetration of EV at a fixed penetration of PV (Figure 23b) and vice versa. The seasonal dependence is less significant for SCR compared to DSG and DSS. In contrast to the DSG and DSS, the SCR increases with increasing penetration of EV at a fixed penetration of PV (Figure 23c) and vice versa.
