E-mobility represents a key step towards ensuring green transition in the transport sector. However, owing
to the increasing number of electric vehicles (EVs), their impact is first felt in power systems. Consequently,
this thesis investigates the impact of e-mobility on the selected area of the distribution grid and its role in
the development of flexibility services. Based on the developed model that relies on spatiotemporal
modelling of EV charging, we evaluated the advantages and disadvantages of the vehicle-to-grid (V2G)
concept.
As the field of e-mobility lies at the intersection of power systems, transportation, and end users,
the developed model is the result of a multidisciplinary approach. Using real data on network topology and
telemetry, we developed a model for a distribution network in Velenje, which comprises 292 substations.
The research was structured into three parts owing to the broadness of this field. We began with
spatial-temporal traffic modeling, which represents the basis for generating realistic driving profiles for 2000
EVs during one working day. Based on this, we determined the locations of vehicles during the observed
period. This step was based on Monte Carlo simulations using Markov chains. We relied on statistical data
on daily passenger mobility in Slovenia, which were then expanded using a special sampling method with
data from foreign studies.
In the second part, we developed a tool for forecasting electricity consumption at the substation
level based on the Prophet tool in Python. Using this tool, we generated forecasts for one-day simulation
period. In addition to accuracy, we also focused on scalability, which is crucial for predicting the loads of a
large number of substations.
In the third and central part of the research, we focused on modeling different charging strategies.
For this purpose, we developed a model that generates EV charging profiles for three different charging
strategies, considering the daily EV driving profiles and network loads. To compare the impacts of these
strategies, in addition to the V2G concept, we examined uncontrolled and smart charging.
A comparison of the main characteristics of the generated driving profiles with the statistical data
showed that the obtained profiles were representative and suitable for further use. This also confirmed the
method itself, which was based on spatial-temporal modeling. In the context of load forecasting, we found
that the Prophet-based model enabled the generation of credible forecasts.
The final results of the charging modeling showed that the V2G concept is the most favorable
charging strategy from the grid perspective, while also providing the greatest potential for both positive and
negative flexibility. Until V2G reaches its full development, we consider smart charging as an interim
solution which, despite not allowing discharging, represents a significantly better solution than uncontrolled
charging from the network’s perspective.
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