Applied data analysis for driving patterns - Anxiety about Electric Mobility Range often unsubstantiated
Authors: Philipp Artur Kienscherf, Karsten Schroer, Max Gierkink, Johannes Wagner and Arne Lilienkamp
Institute of Energy Economics (EWI) at the University of Cologne
Electrification is an important cornerstone in decarbonization. The use of sustainably generated electricity in formerly virtually dominated fossil fuel-dependent sectors offers abundant decarbonization potential. The mobility sector and the building sector are two of these sectors with rapidly increasing levels of electrification. A growing number of electric vehicles and heat pumps in distribution grids are the outcome of this development.
While this development has benefits for the climate, it may cause conflicts in electricity systems as electric vehicles and heat pumps increase the load in distribution grids. This can lead to critical situations in which the security of supply is endangered. Thus, the capability of flexible operation of these assets becomes a necessity. However, conflicting interests of different stakeholders can pose problems e.g., with respect to flexible electric vehicle charging.
These conflicts of interests need to be addressed and measures need to be designed to reconcile them. Digitization is a necessary technological development in this context that can help to understand and unravel the increasingly meshed energy landscape. But for being able to do so, necessary abilities need to be acquired in the first place. Educating relevant actors in the energy sector on the potential of digitalization is therefore an important step towards a successful energy transition. Within EDDIE, the foundation is laid to distribute knowledge on digitalization and corresponding key competencies into both, academia, and practice.
In the context of electric mobility, digitization can contribute on several levels to reconciling stakeholders’ interests. On the one hand, digitization can harmonize the interests surgically by data-driven operation approaches and the implementation of smart algorithms (e.g. smart metering, smart charging). On the other hand, it can contribute to dismantling preconceptions by quantifying subjective assumptions by data-driven analyses. This is illustrated by the subsequent case study carried out by a team from the Institute of Energy Economics (EWI) at the University of Cologne, the IT company green|connector, the Dortmund Energy and Water Supply (DEW21), and the Karlsruhe and Lübeck public utilities .
Commuting with electric vehicles
Electric vehicles (EV) are considered a central component of future mobility. One obstacle is “range anxiety” – the worry that the battery will not reach the target. But this concern is often unfounded. The range of electric cars is generally sufficient for everyday use – and charging at the workplace could play an important role in the future. This is the result of a data analysis of almost 5,000 trips conducted by the mentioned consortium.
A purpose-built smartphone app was rolled out in the utilities’ catchment areas. Using the app, users were able to record their routes over the course of a year, with a resolution exact to meter and minute. The app also made it possible to compare users’ driving patterns with currently available electric vehicle models. This enabled users to assess whether an EV would be compatible with their mobility needs and preferences. The anonymized, high-resolution data gave the project partners an relevant insight into future system effects of EV charging and possible business models.
The result: The journeys examined rarely exceed the range of typical electric vehicles. In addition, a survey of the more than 600 participants showed that they also want to recharge at their workplace.
Users were also able to enter detailed information about their charging preferences in the app. The identified commuter profile is clearly reflected here: especially during the day, after arrival at work, or in the evening, drivers want to charge their EVs. Using this information and scenarios of EV adoption, the research team can estimate the need for charging infrastructure. Figure 2 shows the charging capacity for a typical roll-out region in 2030. Depending on the specific adoption path of electric vehicles, high charging capacities are required at peak load times. These could pose major challenges for energy systems, especially at local hotspots.
In the extrapolated charging profile, the interaction of temporal and spatial components is particularly striking. In the evening, the required charging power is quite high, especially in suburbs or in the countryside. However, the peak load is even higher in the morning when commuters reach their workplaces. Because the arrival times in the morning are less dispersed than the arrival times in the evening, the peak load in the morning is particularly high. Thus, urban energy systems with many employers could be particularly burdened.
Future business model “Workplace charging”?
 Arne Lilienkamp, Philipp Artur Kienscherf, Karsten Schroer, Max Gierkink; 2020, In: et – Energiewirtschaftliche Tagesfragen, Vol. 70 (11), 2020, pp. 53-55