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:: Volume 6, Issue 12 (3-2018) ::
ieijqp 2018, 6(12): 11-24 Back to browse issues page
A Probabilistic Model for Optimal Electric Vehicle Fast Charging Stations Allocation
Raziye Aghapour, Mohammad Sadegh Sepasian
Faculty of Electrical Engineering, University of Shahid Beheshti, Abbaspour School of Engineering, Tehran
Abstract:   (1145 Views)

Planning and construction of charging station are important aspect of promoting the development of electric vehicle. Considering the interactions between power and transportation industries, charging station planning require a systematic consideration of relevant issues including: traffic network characterizes, charging demand and electric network constraint.

The scope of this study is optimal sitting and sizing of fast charging stations in distribution network considering uncertainties. Firstly, a stochastic model based on queueing theory and user equilibrium based traffic assignment model is developed for electric vehicle charging demand. Then, annual cost of investment and energy losses is minimized under chance constrained framework. The probabilistic aspect of problem are solved using point estimation method and Gram-Charlier expansion. In Addition, to consider uncertainties in final decision making, probabilistic dominance models are introduced. Finally, planning result of coupled distribution and traffic systems are presented to illustrate the performance of the proposed approach.

Keywords: Fast charging station, queueing theory, traffic assignment, chance constraint, point estimation, probabilistic dominance
Full-Text [PDF 1482 kb]   (1126 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/11/25 | Accepted: 2017/10/31 | Published: 2018/03/7

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Aghapour R, Sepasian M S. A Probabilistic Model for Optimal Electric Vehicle Fast Charging Stations Allocation. ieijqp. 2018; 6 (12) :11-24
URL: http://ieijqp.ir/article-1-400-en.html

Volume 6, Issue 12 (3-2018) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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