Alice Callanan shows in her doctoral thesis that a fully electric vehicle fleet will not cause a general system failure to the regional power grid. However, certain local reinforcements are necessary to handle the increasing load.
Alice Callanan’s doctoral research has been conducted at Lund University within the Swedish Electromobility Centre (SEC) project ACTUAL – Grid and Road Simulation for E‑mobility.
Alice, can you describe your doctoral thesis at an overall level?
The main question I start from is how well the power grid can handle full electrification of the vehicle fleet. I have carried out a case study of the power grid in Skåne, with a focus on regional effects rather than local ones. Charging often takes place at the local grid level, but if the number of electric vehicles becomes very large, the effects will aggregate and create challenges at higher voltage levels.
How does this differ from previous research?
Much of the previous research has focused on local grids, which makes sense since problems often appear there first. I have instead looked at the system in a more aggregated way, up at the regional grid level, to understand how far electrification can go and what happens when the load builds up higher in the system.
What do your results show about the risk of overload?
The most important result is that the regional grid will not collapse under full electrification. However, there are clearly local challenges. In Skåne, it is around 10 out of 85 transformers where the risk of overload increases. This makes it possible to identify specific weak points in the grid, rather than talking about general system failure.
What does this mean in practice for grid expansion?
In some cases, grid reinforcement or new transformers would be required. In other cases, the risk is low and limited to a small number of hours per year. In those situations, alternative solutions such as flexibility measures or conditional agreements may be more reasonable than costly investments.
What methods have you used in your research?
I start from traditional grid‑planning methodology but develop it in a probabilistic direction. It is based on load‑flow analysis using real power‑grid models, the same types of models used by grid operators. Instead of focusing on a single worst‑case hour, I analyse statistical risks and probabilities of overload, based on variations in electric‑vehicle charging, generation and other loads in the system.
Why is it important to work probabilistically rather than relying on worst‑case scenarios?
The extreme cases are still included in the analysis, but the difference is that we also know how likely they are. That makes it possible to assess how often flexibility would actually be needed. For example, how many hours per year loads might need to be reduced, instead of dimensioning the entire system for a very rare and extreme scenario.
Were there any other findings that stood out to you?
Yes, one important insight is that it is not only important when charging takes place, but also where. Smart charging in time reduces peak power, but that does not automatically mean that the load on all grid components decreases proportionally. The location of charging infrastructure is at least as important, especially in relation to where electricity generation, such as wind power, is located.
What is your main overall conclusion?
Perhaps my most important conclusion is that the transport system and the power system must be simulated together. Many of the assumptions made when the systems are analysed separately do not hold. To properly understand loads and risks, the interaction between the two systems has to be modelled.
Do you have a message for decision‑makers?
Power‑based tariffs have proven effective in many situations at the regional grid level, but there is no single solution that works everywhere. Systems differ, and measures need to be adapted locally. That is an important conclusion from my work.
About Alice Callanan
Alice Callanan is from the Swedish county Småland and has lived in the city of Lund the past ten years. She has as a degree from Lund University in Master of Science in Engineering Physics, specializing in Energy Systems. After the PhD defense she will start working at the Swedish national grid operator, Svenska kraftnät, in Stockholm, where she will continue working with power‑grid modelling.
“I became interested in energy issues during my master’s studies, where I chose energy as my specialisation. I wanted to work with something that is clearly linked to society and infrastructure, and is important to a lot of people. My interest was strongly driven by climate issues and a desire to contribute to the energy and climate transition, even though I really enjoy the technical side of it. I come from a strong modelling background, and those kinds of methods can be applied to many complex systems. What made energy particularly appealing was the opportunity to apply modelling to something that felt both important and relevant.”
Link to the doctoral thesis: Quantifying the Grid Impact of Electric Vehicles: A Probabilistic Case Study of Southern Sweden
