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Optimal planning of electric vehicle charging facilities considering demand stimulus effects

Yongzhong Wu et al.

International Journal of Applied Decision Sciences2025https://doi.org/10.1504/ijads.2025.144783article
AJG 1
Weight
0.37

What the paper says

The development of electric vehicle charging facilities can positively influence user demand and the demand landscape, a factor often neglected in existing models that focus on current demand and organic growth. We present models for determining the quantity and location of charging facilities while considering their impact on the future demand landscape. Employing a gravity model, we analyse the interaction between charging station network planning and future demand. We develop an optimisation model for charging facility planning, aiming to minimise total social costs. Our proposed solution employs the weighted Voronoi polygon graph algorithm. Through a case study, we demonstrate the significance of the proposed model by comparing the solution obtained that considers the impact of charging network construction on user demand with a solution that neglects this stimulus. The results underscore the importance of incorporating user demand stimulus in infrastructure planning, providing valuable insights for electric vehicle charging facility investors and operators.

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https://doi.org/https://doi.org/10.1504/ijads.2025.144783

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@article{yongzhong2025,
  title        = {{Optimal planning of electric vehicle charging facilities considering demand stimulus effects}},
  author       = {Yongzhong Wu et al.},
  journal      = {International Journal of Applied Decision Sciences},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijads.2025.144783},
}

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Optimal planning of electric vehicle charging facilities considering demand stimulus effects

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.