Quantitive analysis of electric vehicle flexibility: A data-driven approach. (February 2018)

• Record Type: Journal Article
• Authors: Sadeghianpourhamami, N.; Refa, N.; Strobbe, M.; Develder, C.
• Is Part Of: International journal of electrical power & energy systems. Volume 95(2018)
• Page Start: 451
• Page End: 462
• Publication Date: 2018-02
• Subjects: Electric vehicles -- Flexibility quantization -- Smart grid; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2017.09.007
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Electric vehicle flexibility quantization based on a large-scale real-world dataset. Three behavioral clusters are identified based on car arrival and departure times. Offered flexibility duration and timing varies across the behavioral clusters. Seasonal changes and weekends influence the flexibility characteristics of clusters. Possible time shifting of EV charging is longer for cars arriving in the afternoons. Abstract: The electric vehicle (EV) flexibility, indicates to what extent the charging load can be coordinated (i.e., to flatten the load curve or to utilize renewable energy resources). However, such flexibility is neither well analyzed nor effectively quantified in literature. In this paper we fill this gap and offer an extensive analysis of the flexibility characteristics of 390k EV charging sessions and propose measures to quantize their flexibility exploitation. Our contributions include: (1) characterization of the EV charging behavior by clustering the arrival and departure time combinations that leads to the identification of type of EV charging behavior, (2) in-depth analysis of the characteristics of the charging sessions in each behavioral cluster and investigation of the influence of weekdays and seasonal changes on those characteristics including arrival, sojourn and idle times, and (3) proposing measures and an algorithm to quantitatively analyze how much flexibility (in terms of duration and amount) is used at various times of a day, for two representative scenarios. Understanding the characteristics of that flexibility (e.g., amount, time and duration of availability) and when it is used (in terms of both duration and amount) helps to develop more realistic price and incentive schemes in DR algorithms to efficiently exploit the offered flexibility or to estimate when to stimulate additional flexibility.