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
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.
HABIB Salman, KHAN Muhammad Mansoor, ABBAS Farukh, NUMAN Muhammad, ALI Yaqoob, TANG Houjun, YAN Xuhui. 用于配电网络风险评估的电动汽车充电行为随机估计的框架[J]. Frontiers in Energy, 2020, 14(2): 298-317.
Salman HABIB, Muhammad Mansoor KHAN, Farukh ABBAS, Muhammad NUMAN, Yaqoob ALI, Houjun TANG, Xuhui YAN. A framework for stochastic estimation of electric vehicle charging behavior for risk assessment of distribution networks. Front. Energy, 2020, 14(2): 298-317.
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