A Distributed Predictive Road Traffic Management System in Urban VANETs

Mejdoubi, Ahmed; Fouchal, Hacène; Zytoune, Ouadoudi; Ouadou, Mohamed

doi:10.1109/iwcmc.2019.8766717

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…( 2). Referring to the literature [21], we divide the condition of links into four levels based on the link saturation degree: unimpeded (0 < S ij ≤ 0.6), slow-moving (0.6 < S ij ≤ 0.8), congested (0.8 < S ij ≤ 1), and severely congested (1 < S ij ≤ 2). The values of nodal resistance and link resistance corresponding to different saturation levels are shown in Eqs.…”

Section: Dynamic Transportation Network Modelmentioning

confidence: 99%

EV Charging Station Load Prediction in Coupled Urban Transportation and Distribution Networks

Li,

Chang

2024

ENERGY

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The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupled urban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is formulated considering both nodal and path resistance. Then, a finer EV power consumption model is proposed by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method is applied to predict the distribution of EV charging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model. Moreover, a dynamic charging pricing scheme for EVs is devised based on the EV charging station load requirements and the maximum thresholds to ensure the security operation of distribution networks. Finally, the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model. The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from 32.87% to 37.21% compared to the BPR model. Additionally, the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from 27.2 to 31.49 MWh by considering the influence of ambient temperature and speed on power energy consumption.

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Section: Dynamic Transportation Network Modelmentioning

confidence: 99%