Over recent years, numbers of electric vehicles (EVs) have shown a strong growth and sales are projected to continue to grow. For facilitating charging possibilities for EVs typically two rollout strategies have been applied; demand-driven and strategic rollout. This study focuses on determining the differences in performance metrics of the two rollout strategies by first defining key performance metrics. Thereafter, the root causes of performance differences between the two rollout strategies are investigated. This study analyzes charging data of 1,007,137 transactions on 1742 different CPs by use of 53,850 unique charging cards. This research concludes that demand-driven CPs outperform strategic CPs on weekly energy transfer and connection duration, while strategic CPs outperform their demand-driven counterparts on charging time ratio. Regarding users facilitated, there is a significant change in performance after massive EV-uptake. The root cause analysis shows effects of EV uptake and user type composition on the differences in performance metrics. This research concludes with implications for policy makers regarding an optimal portfolio of rollout strategies.
DOCUMENT
Within NHL Stenden University of Applied Science, a choice for a new virtual learning environment was made in mid-2021, primarily on policy and management grounds. Early in the migration process, it became clear that this approach could perturb the further rollout of the Design-Based Education (DBE, https://edu.nl/mwp8j) educational concept. Four templates were developed to intertwine technological and educational processes that structure different ways of "blended" learning and teaching within DBE. Initial user experiences show that the templates’ structures help teachers reconsider online learning activities to shape and facilitate blended DBE learning processes.
DOCUMENT
Since the first uptake of electric vehicles, policy makers are questioning how to rollout public charging infrastructure in an efficient manner, such that user convenience balances with costs of investment. In some metropolitan areas, the first phase of rollout has been passed, meaning an optimized deployment of future charging stations for electric vehicles (EVs) becomes important to improve the charging infrastructure and ensure customer satisfaction and sufficient service provision. Complex system literature shows that network vulnerability is an important metric, yet, charging infrastructure has not yet been a subject of these simulation models so far. This research, based on real-world data, provides a novel approach for improving the roll-out strategy of municipalities, by treating the charge infrastructure as a complex network of charging stations and defining vulnerability in respect to the availability of its surrounding charging stations within relevant walking distance.
DOCUMENT
