Abstract
• A data-driven agent-based model compared charging station roll-out strategies. • Increasing size, due to return to scale effects result in utilisation efficiency. • Policy makers face a trade-off between providing accessibility & convenience. • Mixed roll-out strategies (Level 2, hubs, Fast) satisfies EV driver demand best. 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.
Highlights
In most countries electric vehicles (EVs) constitute less than a 1% of all vehicles on the road (International Energy Agency, 2018)
The results illustrate the reciprocal effect between EVs and available charging stations
The analysis focuses on the two most important assumptions, the maximum willingness to walk for EV drivers and the price reduction for battery
Summary
In most countries electric vehicles (EVs) constitute less than a 1% of all vehicles on the road (International Energy Agency, 2018). At the eve of the large-scale introduction of EVs, policy makers are looking for the optimal approach to scale up charging infrastructure to facilitate increased charging demand. The main question(s) they face is how many and which type of charging stations should be installed where. These are long-term tactical decisions, as infrastructure in vestment costs are high and payback periods long. The main questions come down to operational decisions for policy makers such as: how many charging stations should be installed relative to the number of EVs on the road? Policy makers urgently need insights into which reciprocal effects between investments in charging infrastructure and EV adoption exist, to be able to capitalize on them in their decision making The main questions come down to operational decisions for policy makers such as: how many charging stations should be installed relative to the number of EVs on the road? Which EV to charging station ratio is optimal to service EV drivers and provides business opportunities for charging point operators (CPOs)? What is the trade-off in service to EV drivers between accessibility (ability to charge) and convenience (e.g. proximity and charging time)? Are charging stations best clustered in centralised hubs or should they be spread throughout a city to provide maximum geographical coverage? Can return to scale effects be expected? Can new fast charging technologies with increased charging speeds (from 50 kW up to 350 kW) provide an alternative centralised solution in urban envi ronments? Policy makers urgently need insights into which reciprocal effects between investments in charging infrastructure and EV adoption exist, to be able to capitalize on them in their decision making
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