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Optimal electric vehicle charging with dynamic pricing, customer preferences and power peak reduction

Miguel F. Anjos et al.

INFOR (INFOR: Information Systems and Operational Research)2025https://doi.org/10.1080/03155986.2025.2463189article
AJG 1ABDC B
Weight
0.41

Abstract

We consider a provider of electric vehicle charging stations that operates a network of charging stations and use time varying pricing to maximize profit and reduce the impact on the electric grid. We propose a bilevel model with a single leader and multiple disjoint followers. The customers (followers) makes decisions independently from each other. The provider (leader) sets the prices for each station at each time slot, and ensures there is enough energy to charge. The charging choice of each customer is represented by a combination of a preference list of (station, time) pairs and a reserve price. The proposed model takes thus into accounts for the heterogeneity of customers with respect to price sensitivity and charging preferences. We define a single level reformulation based on the reformulation for the rank pricing problem. Numerical results put into highlight the efficiency of the new reformulation and the impact of the model on the grid peak. Electric vehicle charging, Dynamic pricing,

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https://doi.org/https://doi.org/10.1080/03155986.2025.2463189

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@article{miguel2025,
  title        = {{Optimal electric vehicle charging with dynamic pricing, customer preferences and power peak reduction}},
  author       = {Miguel F. Anjos et al.},
  journal      = {INFOR (INFOR: Information Systems and Operational Research)},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/03155986.2025.2463189},
}

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Optimal electric vehicle charging with dynamic pricing, customer preferences and power peak reduction

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 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.