Mr. Xin Zhao Profile

Xin Zhao

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Proceedings Article | 10 November 2022 Paper

Prediction of temporal and spatial distribution of electric vehicle charging load considering traffic flow

Tingting Xu, Xiaorui Hu, Bin Zhou, Huicai Wang, Lei Chi, Peng Li, Xin Zhao, Fangxing Chen, Hongyu Long

Proceedings Volume 12331, 1233110 (2022) https://doi.org/10.1117/12.2652465

KEYWORDS: Roads, Resistance, Networks, Computer simulations, Neural networks, Monte Carlo methods, Error analysis, Telecommunications, System on a chip, Solids

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Electric vehicle load forecasting is the basis for the safe and stable operation of the distribution network, and it is also a prerequisite for the planning and layout of electric vehicle infrastructure. Firstly, considering the characteristics of electric vehicles as participants in the transportation network and the characteristics and mobile load characteristics of electric vehicles as vehicles, a method for forecasting the temporal and spatial distribution of electric vehicle charging load considering traffic flow is proposed. This method first establishes a road network model that considers the flow-density-speed model and the road section impedance and node impedance based on the traffic flow of the road section based on the characteristics of the urban road network multiple intersections and the traffic flow of each section. Secondly, introduce the time function of charging probability and Freud's path search algorithm to assign start and end nodes and plan the driving path for electric vehicles to simulate its dynamic driving process and charging behavior. Finally, a simulation experiment of charging load prediction is carried out with a typical regional road network. The result shows that the distribution of electric vehicle charging load in different functional areas is different and the time distribution is uneven, which verifies the effectiveness and feasibility of the proposed method.

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