  Electric vehicles (EVs) play a crucial role in the transition towards
sustainable modes of transportation and thus are critical to the energy
transition. As their number grows, managing the aggregate power of EV charging
is crucial to maintain grid stability and mitigate congestion. This study
analyses more than 500 thousand real charging transactions in the Netherlands
to explore the challenge and opportunity for the energy system presented by EV
growth and smart charging flexibility. Specifically, it analyses the collective
ability to provide congestion management services according to the
specifications of those services in the Netherlands. In this study, a
data-driven model of charging behaviour is created to explore the implications
of delivering dependable congestion management services at various aggregation
levels and types of service. The probability of offering specific grid services
by different categories of charging stations (CS) is analysed. These
probabilities can help EV aggregators, such as charging point operators, make
informed decisions about offering congestion mitigation products per relevant
regulations and distribution system operators to assess their potential. The
ability to offer different flexibility products, namely re-dispatch and
capacity limitation, for congestion management, is assessed using various
dispatch strategies. Next, machine learning models are used to predict the
probability of CSs being able to deliver these products, accounting for
uncertainties. Results indicate that residential charging locations have
significant potential to provide both products during evening peak hours. While
shared EVs offer better certainty regarding arrival and departure times, their
small fleet size currently restricts their ability to meet the minimum order
size of flexible products.
