A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks

doi:10.1007/s11708-019-0648-5

Front. Energy

2020, Vol. 14

Issue (2) : 298-317 https://doi.org/10.1007/s11708-019-0648-5

RESEARCH ARTICLE

A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks

Salman HABIB¹(

), Muhammad Mansoor KHAN², Farukh ABBAS², Muhammad NUMAN², Yaqoob ALI², Houjun TANG², Xuhui YAN³

¹. Key Laboratory of Control of Power Transmission and Transformation of the Ministry of Education, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
². Key Laboratory of Control of Power Transmission and Transformation of the Ministry of Education, School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
³. State Grid Liyang Power Supply Company, Liyang 213300, China

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Abstract

Power systems are being transformed to enhance the sustainability. This paper contributes to the knowledge regarding the operational process of future power networks by developing a realistic and stochastic charging model of electric vehicles (EVs). Large-scale integration of EVs into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Unbalanced voltages prevent effective and reliable operation of RDNs. Diversified EV loads require a stochastic approach to predict EVs charging demand, consequently, a probabilistic model is developed to account several realistic aspects comprising charging time, battery capacity, driving mileage, state-of-charge, traveling frequency, charging power, and time-of-use mechanism under peak and off-peak charging strategies. An attempt is made to examine risks associated with RDNs by applying a stochastic model of EVs charging pattern. The output of EV stochastic model obtained from Monte-Carlo simulations is utilized to evaluate the power quality parameters of RDNs. The equipment capability of RDNs must be evaluated to determine the potential overloads. Performance specifications of RDNs including voltage unbalance factor, voltage behavior, domestic transformer limits and feeder losses are assessed in context to EV charging scenarios with various charging power levels at different penetration levels. Moreover, the impact assessment of EVs on RDNs is found to majorly rely on the type and location of a power network.

Keywords electric vehicles (EVs) residential distribution networks (RDNs) voltage unbalance factor (VUF) state-of charge (SOC) time-of-use (TOU)

Corresponding Author(s): Salman HABIB

Online First Date: 26 November 2019 Issue Date: 22 June 2020

Cite this article:

Salman HABIB,Muhammad Mansoor KHAN,Farukh ABBAS, et al. A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks[J]. Front. Energy, 2020, 14(2): 298-317.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-019-0648-5
https://academic.hep.com.cn/fie/EN/Y2020/V14/I2/298

Fig.1 Proposed framework for evaluation of distribution networks.

Tab.1 Plug-in specifications of EV charging at residential locations

Tab.2 Charging characteristics of EV models

Fig.2 Probability distributions of necessary parameters for realistic EV model.

Fig.3 PDF of battery SOC after daily mileage.

Fig.4 Process flowchart of EV stochastic charging demand.

Fig.5 Stochastic EV charging behavior indifferent charging strategies and charging power levels.

Fig.6 Average peak demand profiles of various domestic houses.

Fig.7 Benchmark system and placement of various residential houses.

Fig.8 Thermal loading of transformers with different charging strategies and penetration levels.

Fig.9 Feeder losses with different charging strategies and penetration levels.

Fig.10 Voltage behavior due to random phase-wise house and EV distribution with different charging levels (L1, L2), charging strategies (UC, CC), and penetration levels after quasi-dynamic simulations.

Fig.11 VUF due to random phase-wise house and EV distribution with different charging levels (L1, L2), charging strategies (UC, CC), and penetration levels after quasi-dynamic simulations.

Fig.12 Convergence test of Monte-Carlo method with different charging strategies.

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