Organisation of Electric Vehicle Charging
Bart Nijenhuis is a PhD student in the Department of Mathematics of Operations Research. (Co)Promotors are prof.dr. J.L. Hurink and dr.ir. G. Hoogsteen from the Faculty of Electrical Engineering, Mathematics and Computer Science.
Addressing climate change requires a fundamental shift from fossil fuels to renewable energy sources. Unlike traditional centralised power systems, renewable technologies such as wind and solar are decentralised and variable, posing significant challenges to the electricity grid. The current grid was not designed to handle such decentralised, intermittent generation, resulting in issues like grid congestion and reduced operational flexibility.
Alongside the energy transition, electrification of mobility through electric vehicles (EVs) is crucial for reducing greenhouse gas emissions. However, the increasing demand for EV charging, especially during peak hours, further strains distribution grids that are already under pressure. This raises important questions about how to effectively organise and manage EV charging.
This thesis investigates the organisation of EV charging from three key perspectives: Distribution System Operators (DSOs), end users, and Charge Point Operators (CPOs). For DSOs, EV charging introduces risks of grid overload due to potentially synchronised charging behaviour. The research demonstrates that if communication systems and access to relevant information are in place, DSOs can significantly mitigate these impacts through controlled EV charging.
End users play a critical role by offering flexibility in their charging behaviour. By leveraging predictable mobility patterns, EV smart charging can shift energy demand to better align with local renewable energy availability. Field tests conducted in this thesis show that asking users for departure time and required energy at the start of a charging session led to a 48% reduction in peak load and nearly doubled the self-consumption of local solar energy.
CPOs, meanwhile, must balance user demand, local energy constraints, and grid limitations. This thesis introduces a congestion-aware scheduling method tailored for EV charging hubs. Simulations and field trials show that it can reduce operational costs for CPOs, lower grid loads, and maintain quality of service for users.
Despite these promising results, structural issues remain. Market mechanisms and incentives for congestion reduction are underdeveloped, and lack of proper interoperability among stakeholders limits effectiveness of smart charging initiatives. Enhancing open standards such as OCPI to support smart charging coordination is key to unlocking the full potential of EV charging in the energy transition.
