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Quantitive analysis of electric vehicle flexibility : a data-driven approach

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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. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords
IBCN, CHARGING DEMAND, LOAD, TECHNOLOGIES, OPTIMIZATION, INTEGRATION, MANAGEMENT, BENEFITS, SERVICES, BEHAVIOR, ENERGY, Electric vehicles, Flexibility quantization, Smart grid

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Citation

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MLA
Sadeghianpourhamami, Nasrin et al. “Quantitive Analysis of Electric Vehicle Flexibility : a Data-driven Approach.” INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 95 (2018): 451–462. Print.
APA
Sadeghianpourhamami, N., Refa, N., Strobbe, M., & Develder, C. (2018). Quantitive analysis of electric vehicle flexibility : a data-driven approach. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 95, 451–462.
Chicago author-date
Sadeghianpourhamami, Nasrin, N. Refa, Matthias Strobbe, and Chris Develder. 2018. “Quantitive Analysis of Electric Vehicle Flexibility : a Data-driven Approach.” International Journal of Electrical Power & Energy Systems 95: 451–462.
Chicago author-date (all authors)
Sadeghianpourhamami, Nasrin, N. Refa, Matthias Strobbe, and Chris Develder. 2018. “Quantitive Analysis of Electric Vehicle Flexibility : a Data-driven Approach.” International Journal of Electrical Power & Energy Systems 95: 451–462.
Vancouver
1.
Sadeghianpourhamami N, Refa N, Strobbe M, Develder C. Quantitive analysis of electric vehicle flexibility : a data-driven approach. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS. Oxford: Elsevier Sci Ltd; 2018;95:451–62.
IEEE
[1]
N. Sadeghianpourhamami, N. Refa, M. Strobbe, and C. Develder, “Quantitive analysis of electric vehicle flexibility : a data-driven approach,” INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol. 95, pp. 451–462, 2018.
@article{8541682,
  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. (C) 2017 Elsevier Ltd. All rights reserved.},
  author       = {Sadeghianpourhamami, Nasrin and Refa, N. and Strobbe, Matthias and Develder, Chris},
  issn         = {0142-0615},
  journal      = {INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS},
  keywords     = {IBCN,CHARGING DEMAND,LOAD,TECHNOLOGIES,OPTIMIZATION,INTEGRATION,MANAGEMENT,BENEFITS,SERVICES,BEHAVIOR,ENERGY,Electric vehicles,Flexibility quantization,Smart grid},
  language     = {eng},
  pages        = {451--462},
  publisher    = {Elsevier Sci Ltd},
  title        = {Quantitive analysis of electric vehicle flexibility : a data-driven approach},
  url          = {http://dx.doi.org/10.1016/j.ijepes.2017.09.007},
  volume       = {95},
  year         = {2018},
}

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