Individual Optimization

The approach of the individual optimization is to minimize the peak load of each individual charging process in order to reduce the total load. If in the original sce-nario the charging time is equal to the parking time, the constant assumed power consumption is already the optimum. In contrast, the situation is different with flex-ible sessions. The peak load can be reduced by using the entire parking time as the charging time. The optimal case is a constant charging power over the entire parking time. The aggregation of parallel charging processes finally provides the optimized load curve.

In order to realize the optimization, modifications to the input data have to be made in the first step. It is determined that the data will be read in on a daily basis, so that the data set must be divided accordingly. By splitting the total time series into individual time series, it should be noted that individual charging processes can also be split into two sub-processes.

Modified session Real session

Parking + Charging Only Parking

Day 1 Day 2

Figure 5.3: Split of charging sessions for the optimization - two different approaches

As an example, Figure 5.3 shows two splitting variants. In the first case (real ses-sion), a flexible charging process is split into a flexible and an inflexible part. In the second case (modified session), the charging time is divided between day 1 and day 2 in proportion to the parking time of the respective day. Both charging processes thus remain flexible. For better comparability of the results, the charging processes are split according to the modified session. Regardless of the optimization variant, the amount of energy consumed is therefore the same over the entire day. In

prin-5.4 Individual Optimization 46

ciple, it should be noted that the analysis at this point could also have been carried out directly over the entire period. Particularly with regard to a further optimization approach (see Chapter 5.5), however, the limitation of the analysis time frame is necessary. The implementation of the optimization is again realized with the help of a Java-based tool and is shown in Figure 5.4. As in the initial scenario, the data are first read in via a SQLite database connection. As already described, the analysis should be done separately for each day, so that some charging sessions have to be divided into two charging processes. The energy drawn on each day (energyday) can be calculated according to Formula 5.3. The total amount of energy (energytotal) is determined as a proportion of the parking time of one day (timeparking,day) to the total parking time (timeparking,total).

energy_day = energy_total∗ timeparking,day

timeparking,total

(5.3)

For optimization, the average charging power (powerind.opt.) over the entire parking time must be determined as well. Like for the analysis of the original scenario, the charging power is again assumed to be constant and is calculated according to Formula 5.4. In comparison to the original case, the amount of energy obtained is not divided by the original charging time but by the parking time.

power_ind.opt. = energy_day timeparking,day

(5.4)

Finally, a timer is used to check how many vehicles are present at what time, anal-ogous to the analysis of the original scenario. The sum of the optimized charging capacities of all present vehicles (Ptotal) results in the optimized load at the respec-tive time. The summary and evaluation of the results is performed in the same way as the original scenario and can be taken from Section 5.3.

Input

s = Current charging session energyday = Req. energy for charging day stotal = Total charging sessions powerind.opt. = Optimized charging power dnow = Day of charging session Ptotal = Aggregated charging power

dtotal = Total days tday = Daytime in minutes

Figure 5.4: Simplified illustration of the individual optimization

The presented optimization method represents a simple approach to influence the load curves. If it is possible to obtain information from the customers about their expected parking times and the amounts of energy to be charged, the optimization can be carried out within a single charging point. An exchange between individual charging points as well as a higher-level control mechanism are not necessary. For this reason, however, there is also a weakness in the optimization which is shown in Graphic 5.5. Due to the fact that all charging events are optimized independently, the optimum of the aggregated charging processes can be different. In the example

5.4 Individual Optimization 48

shown, car 1 and car 2 charge at different times in the original case. In the optimized case, the charging time of car 1 is extended with correspondingly lower power. The charging process of car 2, on the other hand, is inflexible so that the charging be-havior remains the same. Due to the resulting overlap of both charging processes, the peak of the resulting total load in the example shown is therefore greater than in the original case.

Figure 5.5: A weakness of the individual optimization - A higher resulting peak load is possible

It can be expected that the presented optimization method leads to a lower result-ing peak load for a large number of combination. However, as the above example shows, the total load may also increase due to the optimization method that has been selected. To improve the results, a further optimization strategy is presented in the following section.