Within the paradigm of the interconnection between transportation networks and power distribution networks, the charging demand for electric vehicles (EVs) manifests considerable spatiotemporal distribution characteristics. This paper proposes a spatiotemporal prediction method for EV charging loads based on both time and space dimensions. Firstly, a dynamic transportation network model is constructed using graph theory and cellular automata model to simulate traffic flow for EVs. Then, charging characteristics for two typical types of electric vehicles are modeled. A slow-charging demand model for private cars is developed based on the travel chain theory, while a fast-charging demand model for taxis is constructed based on the OD matrix. Subsequently, a Monte Carlo method is applied to predict the spatiotemporal distribution of EV charging demand in both the transport and electricity distribution networks. Finally, the spatiotemporal charging demand distribution characteristics of private cars and taxis are analyzed through a case study. Moreover, the experimental results show that voltage drops occur where the charging load is concentrated, which affects voltage stability.
Spatiotemporal Load Prediction of Electric Vehicle Charging Demand under the Coupling of Traffic Network and Distribution Network
16.05.2025
2179025 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Spatial Load Prediction Considering Spatiotemporal Distribution of Electric Vehicle Charging Load
DOAJ | 2021
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