Fast charging is usually seen as a means to facilitate long distance driving for electric vehicles and roll-out therefore often happens with corridors in mind. Due to limited charging speeds, EV drivers usually tend to charge at home or work when the car is parked for a longer period to avoid unnecessarily time loss. However with increasing charging speeds and different modes (taxi, car sharing) also switching to electric vehicles, a different approach to fast charging is needed. This research investigates the different intentions of EV drivers at fast charging stations in inner-cities and along highways to see how usage at such stations differs to inform policy makers and charging point operators to accommodate an efficient roll-out strategy.
On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.
1. Evaluate priority incentive electrical taxis: Bji het Centraal Station is reeds een voorrangsincentive voor elektrische taxis ingesteld. Gedurende deze case zullen we het effect de huidige regeling toetsen en nagaan wat het effect is op kosten en baten alsmede business case van de e-taxi. Daarnaast zal een technische ontwerpstudie van een dergelijke standplaats onderdeel van dit subproject zijn. 2. Strategic placement of (semi) public charge infra in ArenA Area: In deze case wordt onderzocht op welke manier de laadpalen kunnen bijdragen aan het reguleren van verkeer richting de ArenA en waar deze laadpalen gepositioneerd dienen te worden. 3. Consolidation of city logistics at ArenA Area; In deze case wordt de haalbaarheid onderzocht van incentives op logistieke dienstverleners. Bij welke incentives is het voor vervoerders interessant om over te stappen op elektrisch vervoer? 4. Pilot incentive exemption from parking tax: Hierbij wordt de prijsprikkel “ontheffing van parkeerbelasting”, die de gemeente Amsterdam wil inzetten ter bevordering van e-taxis, onderzocht en gemonitord, waarbij kosten en baten worden vergeleken. 5. Determine hotspot location for e-taxi’s: Incentive-beschikbaarheid- Bepaling van meest kansrijke en faciliterende laadlocaties op basis van ritgegevens van taxi's (hotspot) inclusief vaststelling van eisen/wensen voor de laadfaciliteiten (e.g. (snel)laders) inclusief monitoring van het gebruik na plaatsing.
