*3.2. Determination Load and Production Profiles*

Following the model development, annual time-resolved (15-min mean values) load and production profiles are modelled for the aggregated annual energy consumption and production. Therefore, we use the standard load profiles from the BDEW [20] and the synthetic load profiles from the e-control [21]. These profiles represent normalised load profiles for categories like household, commercial, agricultural, interruptible power supplies, and public lighting. Based on these normalised profiles (1000 kW/a), we calculate the time-resolved annual load profiles.

For the determination of the synthetic charging load profiles for the e-mobility we apply as shown in Figure 5, four steps: (i) data preparation, (ii) determination of each charging process, (iii) modelling charging curve and (iv) aggregation of the load profiles [11].

**Figure 5.** Overview: determination of synthetic charging load profiles of electric vehicles.

In step 1, the traffic grid points and the mobility behaviour presented in Section 2.2 are prepared for the further procedure. In addition to the aggregation of all grid points and the data contained therein (e.g., number of trips) within a cell, this includes the preparation of the distribution functions (arrival/departure/EV type), already mentioned in Section 2.2. Based on the average distances travelled at grid point level, a distribution function for the distance travelled per user group and cell is derived by aggregating all distances travelled within a cell. In step 2, each charging process (CP) within a user group and cell is determined based on the distribution functions by applying probabilistic approaches and the selected charging strategy (e.g., uncontrolled/controlled charging). Each charging process is described by time of arrival, time of departure, EV type and distance travelled. The number of charging processes per cell and user group on a weekday is derived from the aggregation of grid points performed in step 1. Based on the number of trips per weekday, empirical factors from statistical data are used to determine the number of trips for Saturday and Sunday for each user group and cell. The number of trips per day corresponds to today's mobility behaviour. A change in mobility behaviour is difficult to estimate according to traffic planners, therefore it is not taken into account. Hence, we assume that in a scenario with an EV penetration of 100%, each distance travelled represents a trip with an electric vehicle. In step 3, the charging curve for each charging process is modelled according to the state-of-the-art constant current (CC) – constant voltage (CV) charging process. In step 4, all charging processes within a cell are aggregated to a synthetic charging load profile. We describe the general procedure of determining synthetic cell-based charging load profiles for electric vehicles in detail in [11].

For this work we have expanded the method we have described in [11]: Firstly to consider only selected user groups like "trip to work with a private car" for modelling the synthetic charging load profiles. Secondly to enable "intelligent controlled charging", called charging strategy 2.

The extension regarding the selection of the considered user groups consists of creating a trip chain for the selected user groups, which takes place in step 2. The creation of such a trip chain is necessary because the selection of user groups means that there is no longer a charging process after each trip. Therefore, the trips of the non-selected user groups are cumulated into the selected ones, so that the distances travelled by the non-selected user groups can be considered. For this purpose, a trip of the selected user groups consists of several "sub trips", e.g., the trip chain: "working - living shopping - working" of the user group "trip to work with a private car" consists of three "sub-steps". For each trip of the selected user groups resulting from step 1, the total distance travelled is determined as shown in Figure 6. For this, the number of "sub trips" is defined by applying the probabilistic approach to the statistical distribution of the number of trips made per day and vehicle. Subsequently, the distances travelled are determined for each "sub trip" and summed up, so that the charging curve is modelled, based on the total distance travelled.

**Figure 6.** Determination of total distance travelled for each trip of the selected user groups.

The introduction of charging strategy 2, "intelligent controlled charging", also takes place in step 2. Table 1 shows the comparison of charging strategy 1 and 2: As shown, in charging strategy 1 the arrival of the EV starts the charging process, while in charging strategy 2, a time shift of the start of the charging process is considered.

**Table 1.** Comparison of charging strategy 1 and charging strategy 2.


The charging strategy 2 takes into account two different approaches, which depend on the mobility behaviour. For each peak in the synthetic charging load profile, a time span is defined in which the charging processes can be shifted depending on the occurrence (morning, midday, afternoon, evening) of the peak. Charging processes which occur during the morning and midday peak are shifted into the peak of the PV production. For the charging processes that take place during the afternoon and evening peaks, the aim is to reduce the charging load by sequencing the charging processes with the boundary condition that the EVs are fully charged in the morning. This does not involve a reduction of the charging power of the individual charging process.

The defined time span is used to decide for each charging process whether it can be shifted or not, see Figure 7. As a generic example, it is not possible to shift the charging processes CP 1 to CP 4 (red) into the time span, because their time of arrival and their time of departure are before or after the defined time span. This means that this charging process takes place at the original time as defined in charging strategy 1. In principle, the charging processes CP 5 to CP 9 can be shifted into or within the time span. During the shifting and sequencing of the charging process, it must be ensured that this is only possible within the duration of stay of the EV, with the boundary condition that the EV is fully charged at the time of departure. For example, if charging process CP5 is shifted to the start of the time span and the remaining time is not sufficient for a fully charged EV, the charging process will also take place at the original time as defined in charging strategy 1.

**Figure 7.** Generic examples of charging processes, which can be shifted in or within the time span in green and those, which cannot in red.

Following the identification of those charging processes that can be shifted into or within the time span, the shifting or sequencing of these charging processes is carried out according to defined criteria. These criteria represent the duration of stay, a priority factor, start of the charging process and the duration of the charging process. The priority factor describes the ratio between duration of stay and duration of the charging process. This means that the closer this ratio is to one, the shorter the period of time in which this charging process can be shifted within the duration of the vehicle's stay. Such charging processes are assigned with new starts of the charging process as soon as possible. Within the framework of this charging strategy, only the start of the charging processes is changed. All other data (e.g., EV type, charging power, distance travelled) for modelling the charging curves remain unchanged. Therefore, after applying charging strategy 2, the methodology described above can be continued in step 3.
