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An Electric Vehicle Migration Framework

Author

Listed:
  • El Mehdi Er Raqabi
  • Wenkai Li

    (IUJ Research Institute, International University of Japan)

Abstract

Electric vehicles (EVs), with lighter environmental footprint than traditional gasoline vehicles, are growing rapidly worldwide. Some countries such as Norway and Canada have successfully established EV networks and achieved a significant progress towards EV deployment. While the EV technology is becoming popular in developed countries, emerging countries are lacking behind mainly because of the huge investment hurdle to establishing EV networks. This paper developed an efficient Electric Vehicle Migration Framework (EVMF) aiming to minimize the total costs involved in establishing an EV network, using real world data from three major cities of Morocco: Rabat, Casablanca, and Fes. A given set of public institutions having a fleet of EVs are first grouped into zones based on clustering algorithms. MILP (Mixed Integer Linear Programming) models are developed to optimally select EV charging station locations within these organizations, with an objective to minimize the total cost. This paper can help to minimize the investment needed to establish EV networks. The transition towards EV networks can first take place in cities, especially at public institutions, followed by locations among cities. With the framework developed in this paper, policy makers can make better decisions on EV network migration.

Suggested Citation

  • El Mehdi Er Raqabi & Wenkai Li, 2022. "An Electric Vehicle Migration Framework," Working Papers EMS_2022_03, Research Institute, International University of Japan.
    • Handle: RePEc:iuj:wpaper:ems_2022_03
    as

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    File URL: https://www.iuj.ac.jp/workingpapers/index.cfm?File=EMS_2022_03.pdf
    File Function: First version, 2022
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    3. Yanan Niu & Ilja Kantorovitch, 2024. "Bitcoin and Shadow Exchange Rates," Papers 2410.22443, arXiv.org.
    4. Abdullah-Al-Nahid, Syed & Jamal, Taskin & Aziz, Tareq & Bhuiyan, Ashraf Hossain & Khan, Tafsir Ahmed, 2023. "Additive linear modelling and genetic algorithm based electric vehicle outlook and policy formulation for decarbonizing the future transport sector of Bangladesh," Transport Policy, Elsevier, vol. 136(C), pages 21-46.
    5. Mittelman, Gur & Eran, Ronen & Zhivin, Lev & Eisenhändler, Ohad & Luzon, Yossi & Tshuva, Moshe, 2023. "The potential of renewable electricity in isolated grids: The case of Israel in 2050," Applied Energy, Elsevier, vol. 349(C).
    6. Pilotti, L. & Colombari, M. & Castelli, A.F. & Binotti, M. & Giaconia, A. & Martelli, E., 2023. "Simultaneous design and operational optimization of hybrid CSP-PV plants," Applied Energy, Elsevier, vol. 331(C).
    7. Prakash, Abhijith & Ashby, Rohan & Bruce, Anna & MacGill, Iain, 2023. "Quantifying reserve capabilities for designing flexible electricity markets: An Australian case study with increasing penetrations of renewables," Energy Policy, Elsevier, vol. 177(C).

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    More about this item

    Keywords

    Electric vehicle; range anxiety; public transport; optimization; MILP; data mining; remote sensing; clustering.;
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