Flexibility Scheduling for Microgrids with Electric Vehicle (EV) Penetration

Dang, Qiyun; Huo, Yuchong

doi:10.1109/ecce.2018.8558242

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…By the end of 2023, the stock of EVs in China has reached 20.4 million [1], and the gradual replacement of traditional gasoline vehicles by EVs will become an irreversible trend [2]. As a mobile power load [3,4], the widespread promotion of EVs will have a significant influence on the operation of the distribution network along with the urban transportation network [5][6][7]. If EVs are charging at fast charging stations without coordination, it will not only invoke security problems such as voltage deviation or line thermal stability damage issues but also easily cause transportation network congestion.…”

Section: Saturation Of Link N Ij α βmentioning

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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