Capacity Planning Frameworks for Electric Vehicle Charging Stations With Multiclass Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid bec...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2015-07, Vol.6 (4), p.1934-1943
Main Authors: Bayram, Islam Safak, Tajer, Ali, Abdallah, Mohamed, Qaraqe, Khalid
Format: Article
Language:English
Subjects:
Aggregates
Capacity planning
Charging stations
distributed control
electric vehicles (EVs)
performance evaluation
Pricing
Quality of service
Reliability
Urban areas
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Summary:In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling charging requests should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service (QoS). The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of QoS to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2015.2406532