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Cited by 318

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Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations

Hajimiragha, Amirhossein, Canizares, Claudio A., Fowler, Michael W., Elkamel, Ali·2010·IEEE Transactions on Industrial Electronics

Abstract

This paper analyzes the feasibility of optimally utilizing Ontario's grid potential for charging plug-in hybrid electric vehicles (PHEVs) during off-peak periods. Based on a simplified zonal model of Ontario's electricity-transmission network and a zonal pattern of base-load generation capacity from 2009 to 2025, an optimization model is developed to find the optimal, as well as maximum, penetrations of PHEVs into Ontario's transport sector. The results of this paper demonstrate that the present and projected electricity grid in Ontario can be optimally exploited for charging almost 6% of the total vehicles in Ontario or 12.5% of the vehicles in Toronto's transport sector by 2025; this corresponds to approximately 500 000 PHEVs that can be charged from the grid without any additional transmission or power-generation investments beyond those currently planned.

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Metadata

Authors
Hajimiragha, A., Canizares, C. A., Fowler, M. W., & Elkamel, A.
Venue
IEEE Transactions on Industrial Electronics
Published
2010-02
Volume
57
Issue
2
Pages
690-701
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
OpenAlex
W2112249538
Cited by
318
References
0
FWCI
15.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations. https://app.cochat.ai/papers/10.1109/tie.2009.2025711
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Hajimiragha, A., Canizares, C. A., Fowler, M. W., & Elkamel, A. (2010). Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2009.2025711

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.