A smart charging profile was implemented on 39 public charging stations in Amsterdam on which the current level available for electric vehicle (EV) charging was limited during peak hours on the electricity grid (07:00-08:30 and 17:00-20:00) and was increased during the rest of the day. The impact of this profile was measured on three indicators: average charging power, amount of transferred energy and share of positively and negatively affected sessions. The results are distinguished for different categories of electric vehicles with different charging characteristics (number of phases and maximum current). The results depend heavily on this categorisation and are a realistic measurement of the impact of smart charging under real world conditions. The average charging power increased as a result of the new profile and a reduction in the amount of transferred energy was detected during the evening hours, causing outstanding demand which was solved at an accelerated rate after limitations were lifted. For the whole population, 4% of the sessions were positively affected (charged a larger volume of energy) and 5% were negatively affected. These numbers are dominated by the large share of plug-in hybrid electric vehicles (PHEVs) in Amsterdam which are technically not able to profit from the higher current levels. For new generation electric vehicles, 14% of the sessions were positively affected and the percentage of negatively affected sessions was 5%.
The Netherlands is a frontrunner in the field of public charging infrastructure, having a high number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012-2015) a large percentage of the EV fleet were Plugin Hybrid Electric Vehicles (PHEV)due to the subsidy scheme at that time. With an increasing number of Full Electric Vehicles (FEVs) on the market and a current subsidy scheme for FEV only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have effect on charging behavior of the complete fleet, reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing Agent Based Model. Results show important effects of this transitionon charging infrastructure performance.
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.
