Multi-objective charging scheduling for electric vehicles at charging stations with renewable energy generation

The rapid adoption of electric vehicles (EVs) in recent years has posed significant challenges to the safe operation of local grids, particularly regarding charging operations at public charging stations.This paper proposes a real-time charging scheduling scheme to enable efficient Vehicle-to-Grid (V2G) interactions and facilitate renewable energy integration at public charging stations, while accounting for real-world EV charging behaviors. First, an EV charging/discharging behavior database is developed to capture the temporal uncertainty and charging characteristics of both fast- and slow-charging operations on weekdays and weekends. Then a charging pile allocation mechanism is introduced to optimize the charging power distribution for each EV, thereby maximizing the overall operational efficiency of the studied charging station. A micro-grid system model is developed incorporating efficient V2G interactions and renewable energy integration. Finally, a comprehensive charging scheduling scheme is proposed to achieve a balanced optimization of multiple objectives. Extensive simulation studies are conducted to evaluate the performance of the proposed scheduling method. The results demonstrate that the proposed scheme achieves strong performance across all three selected indicators. • A charging behaviour database is built using massive EV operating data. • A charging scheduling scheme is proposed for a public charging station. • A charging pile allocation method is presented for charging power control. • A micro-grid system model is developed for renewable energy integration. • Simulation studies verify the effectiveness of the proposed scheme.

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Publication Details

Journal
Green Energy and Intelligent Transportation
Published
2025-03-01
DOI
https://doi.org/10.1016/j.geits.2025.100283
Citations
17
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
12.42

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Multi-objective charging scheduling for electric vehicles at charging stations with renewable energy generation

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17 citations
Green Energy and Intelligent Transportation
Electric Vehicles and Infrastructure
12.42
article

Multi-objective charging scheduling for electric vehicles at charging stations with renewable energy generation

Lei Zhang, Fabian Breer, M. Junker, Yingjun Ji, Dingsong Cui, Dirk Uwe Sauer, Zhijia Huang, Xiaohui Li, Haibo Chen, Jingyu Gong
article en
17 citations

Abstract

The rapid adoption of electric vehicles (EVs) in recent years has posed significant challenges to the safe operation of local grids, particularly regarding charging operations at public charging stations.This paper proposes a real-time charging scheduling scheme to enable efficient Vehicle-to-Grid (V2G) interactions and facilitate renewable energy integration at public charging stations, while accounting for real-world EV charging behaviors. First, an EV charging/discharging behavior database is developed to capture the temporal uncertainty and charging characteristics of both fast- and slow-charging operations on weekdays and weekends. Then a charging pile allocation mechanism is introduced to optimize the charging power distribution for each EV, thereby maximizing the overall operational efficiency of the studied charging station. A micro-grid system model is developed incorporating efficient V2G interactions and renewable energy integration. Finally, a comprehensive charging scheduling scheme is proposed to achieve a balanced optimization of multiple objectives. Extensive simulation studies are conducted to evaluate the performance of the proposed scheduling method. The results demonstrate that the proposed scheme achieves strong performance across all three selected indicators. • A charging behaviour database is built using massive EV operating data. • A charging scheduling scheme is proposed for a public charging station. • A charging pile allocation method is presented for charging power control. • A micro-grid system model is developed for renewable energy integration. • Simulation studies verify the effectiveness of the proposed scheme.

Green Energy and Intelligent TransportationVol. 4(4)
Beijing Institute of Technology (CN), University of Leeds (GB), RWTH Aachen University (DE)
European Commission, Ministry of Science and Technology of the People's Republic of China
Affordable and clean energy
Openalex Percentile: Top 1%
Electric Vehicles and Infrastructure
12.42
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