Revenue Maximization of Multi-Class Charging Stations with Opportunistic Charger Sharing

Kihong Ahn, Aresh Dadlani, Kiseon Kim, Walid Saad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Distribution of limited smart grid resources among electric vehicles (EVs) with diverse service demands in an unfavorable manner can potentially degrade the overall profit achievable by the operating charging station (CS). In fact, inefficient resource management can lead to customer dissatisfaction arising due to prolonged queueing and blockage of EVs arriving at the CS for service. In this paper, a dynamic electric power allocation scheme for a charging facility is proposed and modeled as a bi-variate continuous-time Markovian process, with exclusive charging outlets being allotted to EVs of different classes in real-time. The presented mechanism enables the CS to guarantee the quality-of-service expected by customers in terms of blocking probability, while also maximizing its own overall revenue. By adopting a practical congestion pricing model within the defined profit function, the revenue optimization framework for a single CS is further extended to a load-balanced network of CSs. Simulation results for the single CS and networked models reveal considerably higher satisfaction levels for congested fast charging EV customers and improved attainable system revenue as compared to a baseline scenario which assumes no classification based on EV service preferences.
Original languageEnglish
Title of host publicationIEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)1938-1883
DOIs
Publication statusPublished - May 2018

Fingerprint Dive into the research topics of 'Revenue Maximization of Multi-Class Charging Stations with Opportunistic Charger Sharing'. Together they form a unique fingerprint.

Cite this