In order to reduce greenhouse gas emission and fossil fuel dependence, Electric Vehicle (EV) has drawn increasing attention due to its zero emission and high efficiency. However, new problems such as range anxiety, long charging duration and high charging power may threaten the safe and efficient operation of both traffic and power systems. This paper proposes a probabilistic approach to model the nodal EV load at fast charging stations in integrated power and transport systems. Following the introduction of the spatial-temporal model of moving EV loads, we extended the model by taking fast charging station into consideration. Fuzzy logic inference system is applied to simulate the charging decision of EV drivers at fast charging station. Due to increasing EV loads in power system, the potential traffic congestion in fast charging stations is modeled and evaluated by queuing theory with spatial-temporal varying arrival and service rates. The time-varying nodal EV loads are obtained by the number of operating fast chargers at each node of the power system. System studies demonstrate that the combination of AC normal and DC charging may share the EV charging demand and alleviate the impact to power system due to fast charging with high power.
Proceedings of 2016 International Conference on Probabilistic Methods Applied To Power Systems, 2016, p. 1-7
Charging stations; Estimation; Decision support systems; Electric vehicles; Load modeling; fast charging station; Probabilistic model; power system; electric vehicle
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2016 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)
International Conference on Probabilistic Methods Applied to Power Systems, 2016