Mobility Pattern Input Data

In order to adequately represent the mobility behavior of German drivers, pirical data from the German Mobility Panel (MOP), (BMVBS, 2008), was em-ployed to deduct valid driving profiles for EVs. Panel participants are randomly selected, but representative households which (self-) report their complete mo-bility behavior during one week. The weekly data sets are obtained from weeks from September to January, with a majority originating from September to November. This period was chosen in order to have representative data with-out vacations and other special events such that an approximation for the overall yearly mobility patterns can be obtained.

All trips within the mentioned weekly period are reported, this includes walk-ing and any other form of mobility. In addition the purpose, length, daytime and mean of transport are recorded. The panel data employed was collected between 1994 to 2007, amounting to an overall volume of 530,000 individual trips. This considerable amount of trips needed to be consolidated and filtered in order to obtain individual mobility profiles that can be attributed to a single person and have been performed by using a conventional vehicle. Range restrictions result-ing from the use of EVs are considered later in the simulation.

Table 3.1: Summary statistics of the employed mobility profiles.

[km / Week] Min. 1. Qu. Median Mean CV 3. Qu. Max.

Employees 1.0 84.0 184.6 225.1 0.76 322.2 956.0 Retired 2.0 48.8 97.5 120.9 0.84 158.5 1034.0 Part-time Employees 1.0 61.4 121.3 159.2 1.31 209.5 1347.0 Unemployed 0.8 34.0 77.2 113.8 1.27 144.2 1993.0

Coefficient of variation: ^σ_µ

Table 3.2: Relative share of the sociodemographic groups in the Mobility Panel and the German Population of 2007, BMVBS (2008).

Employees Part-Time Employees Retired Unemployed

Share MOP 40.3% 14.7% 28.0% 8.3%

Share Population 32.5% 11.9% 34.7% 10.3%

First the trip data was condensed based on person-IDs in order to obtain indi-vidual driving profiles. In this stage 17,705 weekly profiles of indiindi-vidual persons including only trips with a car can be obtained. Following the general filter pro-cess described in Figure 3.3, persons that were on vacation, at a service station or sick and have no valid driving permit were excluded from the data set. The individual trip data was further validated, which included the exclusion of trips that do not return to the home location or have invalid trip lengths and missing values for speed, km and duration.

Further filtering based on sociodemographic criteria and in particular the em-ployment status, leads to a group of 11,436 profiles of which the 1000 most re-cent for every group were selected for further analyses in this work. The groups are full-time employees, retirees, part-time employees and unemployed people, which amount to more than 90% of the profiles in the mobility panel (cf. Table 3.2).

When considered as shares of the overall population the two groups of employ-ees and retiremploy-ees make up 67,2%. In addition these two groups have quite diverse patterns in their mobility behavior, which is why most of the following analysis will only employ these polar sociodemographic groups as a simulation input.

Table 3.1 provides the summary statistics for the employed 4000 driving pro-files. It can be observed that the mean driving distance of employees is exceed-ing the other sociodemographic groups considerably. Also the median for the weekly travel distance is consistently higher with 184.6 km for employees as compared to 97.5 km for retirees. Part-time employees in turn have the second highest travel distances, whereas unemployed people (with some exceptions)

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Figure 3.4: Distribution of daily driving distances for the four sociodemographic groups.

have the lowest travel distances. The comparison on a daily basis shows that all groups have the majority of profiles with travel distances less than 50 km per day (cf. Figure 3.4). This is consistent with the observations made earlier in Section 2.6.1 and Table 2.1 and in particular the distribution presented in Figure 2.15.

The selection of a subset of driving profiles might weaken the level to which the results can be regarded to be representative for the whole population. But with respect to the major sociodemographic groups the main aspects that characterize a driving pattern (i.e. trip distance and frequency) are clearly addressed by the selected subset.

Figure 3.5 depicts a comparison of the weekly driving distance distribution within the four sociodemographic groups. In particular the distribution for ev-ery 50 km interval is compared to the value of all 4000 profiles respectively. The comparison shows that employees have a large share of profiles traveling more than 300 km per week as compared to the overall population. Retirees and part-time employees in turn are rather similar to the overall trip distance distribution.

The group of unemployed persons is different in the sense that it has a higher

relative share all 4000 profiles

(a) Employees all 4000 profiles

(b) Retired all 4000 profiles

(c) Part-time Employees

relative share all 4000 profiles

(d) Unemployed

Figure 3.5: Comparison of weekly driving distance distribution of the four sociodemo-graphic groups.

share of low distance profiles. Further individual details of the profile groups will be addressed in the respective sections in Chapter 4 and 5.

The availability of charging equipment at locations that are visited regularly (e.g. work or shopping places) in addition to the home location is an important factor for a reliable operation of EVs. The different generic location types of the vehicles can be derived from the trip purposes recorded in the mobility panel.

This enables an assessment of the potential availability for connection times with the power grid and thus provides a frame for the analysis of the temporal de-mand flexibility of EVs. In Figure 3.6 the availability of EVs at the home, work and leisure locations, as well as the share of EVs that are driving is depicted for employees and retirees over the course of one week.

It can be observed that employees are mainly characterized by the availability at the home and work location. Nearly 60% of the employee EVs are available at the work location during the week. Leisure activities and locations are not that prominent and are concentrated on evenings and in particular the week-end, but in overall less than 20% of the profiles can be found at these locations. What can also be noticed is that no more than 20% of the employees are driving in one

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Figure 3.6: Availability at home, work and leisure locations for employees and retirees over the course of the week.

period at a time. With respect to the availability of employee EVs for the power grid, charging locations at home and work are thus able to cover most demand requirements.

For retirees it can be observed that the difference between the availability at the home location and all other locations is not that substantial. The driving be-havior also varies as it is more distributed over the day and less concentrated as in the case of employees. The availability of retirees at the home locations is con-sistently around 80% or higher at any time of the day, which is a clear indicator for high temporal flexibility regarding charging demand. The absolute demand requirements are not as high as for employees which lowers their potential prac-tical demand response impact. The next subsections will further elaborate on the details of wholesale energy price and generation data employed.