Zoekresultaten

Evaluating Electric Vehicle Charging Infrastructure Policies

Different charging strategies for electric vehicle fleets in urban freight transport.

The transition from diesel-driven urban freight transport towards more electric urban freight transport turns out to be challenging in practice. A major concern for transport operators is how to find a reliable charging strategy for a larger electric vehicle fleet that provides flexibility based on different daily mission profiles within that fleet, while also minimizing costs. This contribution assesses the trade-off between a large battery pack and opportunity charging with regard to costs and operational constraints. Based on a case study with 39 electric freight vehicles that have been used by a parcel delivery company and a courier company in daily operations for over a year, various scenarios have been analyzed by means of a TCO analysis. Although a large battery allows for more flexibility in planning, opportunity charging can provide a feasible alternative, especially in the case of varying mission profiles. Additional personnel costs during opportunity charging can be avoided as much as possible by a well-integrated charging strategy, which can be realized by a reservation system that minimizes the risk of occupied charging stations and a dense network of charging stations.

Improving Electric vehicle charging station efficiency through pricing

Recent studies show that charging stations are operated in an inefficient way. Due to the fact that electric vehicle (EV) drivers charge while they park, they tend to keep the charging station occupied while not charging. This prevents others from having access. This study is the first to investigate the effect of a pricing strategy to increase the efficient use of electric vehicle charging stations. We used a stated preference survey among EV drivers to investigate the effect of a time-based fee to reduce idle time at a charging station. We tested the effect of such a fee under different scenarios and we modelled the heterogeneity among respondents using a latent class discrete choice model. We find that a fee can be very effective in increasing the efficiency at a charging station but the response to the fee varies among EV drivers depending on their current behaviour and the level of parking pressure they experience near their home. From these findings we draw implications for policy makers and charging point operators who aim to optimize the use of electric vehicle charging stations.

Increasing hosting capacity of low-voltage distribution network using smart charging based on local and dynamic capacity limits

While the Municipality of Amsterdam wants to expand the electric vehicle public charging infrastructure to reach carbon-neutral objectives, the Distribution System Operator cannot allow new charging stations where low-voltage transformers are reaching their maximum capacity. To solve this situation, a smart charging project called Flexpower is being tested in some districts. Charging power is limited during peak times to avoid grid congestion and, therefore, enable the expansion of charging infrastructure while deferring grid investments. This work simulates the implementation of the Flexpower strategy with high penetration of electric vehicles, considering dynamic and local power limits, to assess the impact on both the satisfaction of electric vehicle users and the business model of the Charging Point Operator. A stochastic approach, based on Gaussian Mixture Models, has been used to model different profiles of electric vehicle users using data from the Amsterdam public electric vehicle charging infrastructure. Several key performance indicators have been defined to assess the impact of such charging limitations on the different stakeholders. The results show that, while Amsterdam’s existing public charging infrastructure can host just twice the current electric vehicle demand, the application of Flexpower will enable the growth in charging stations without requiring grid upgrades. Even with 7 times more charging sessions, Flexpower could provide a power peak reduction of 57% while supplying 98% of the total energy required by electric vehicle users.

Dynamic assignment vehicle routing problem with generalised capacity and unknown workload.

In this paper we present a modification to the Dynamic Assignment Vehicle Routing Problem. This problem arises in parcel to vehicle assignment where the destination of the parcels is not known up to the assignment of the parcel to a delivering route. The assignment has to be done immediately without the possibility of re-assignment afterwards. We extend the original problem with a generalisation of the definition of capacity, with an unknown workload, unknown number of parcels per day, and a generalisation of the objective function. This new problem is defined and various methods are proposed to come to an efficient solution method.

Dynamic assignment vehicle routing problem with time windows

Offering time windows to receivers of last-mile delivery is becoming a distinguishing factor. However, we see that in practice carriers have to create routes for their vehicles based on destination information, that is just being revealed when a parcel arrives in the depot. The parcel has to be assigned directly to a vehicle, making this a Dynamic Assignment Vehicle Routing Problem. Incorporating time windows is hard in this case. In this paper an approach is presented to solve this problem including Time Windows. A comparison is made with a real observation and with a solution method for the base problem