IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p438-d1020409.html
   My bibliography  Save this article

Analysis and Modeling of Value Creation Opportunities and Governing Factors for Electric Vehicle Proliferation

Author

Listed:
  • Abhinav Tiwari

    (Department of Electrical Engineering and Computer Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada)

  • Hany Farag

    (Department of Electrical Engineering and Computer Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada)

Abstract

This research presents a comprehensive analysis of electric vehicle (EV) proliferation factors and various monetary and non-monetary value streams emerging in the EV domain. A comprehensive mathematical model is implemented to study EV proliferation and the resulting market share applicable to any geography and jurisdictional regime. Further, a novel framework is presented to analyze the interdependency between EV proliferation factors and value streams. The proposed model and framework can be leveraged to quantifiably evaluate the timeline available for grid operators to accommodate EV growth while utilizing those as Distributed Energy Resources (DERs) to improve grid reliability, commercial value, and environmental benefits. Compared to the previous studies, the analysis indicated that if all the factors which impact EV proliferation are addressed simultaneously, EV market share can surpass the internal combustion engine vehicle (ICV) in as quickly as 15–20 years. The study also highlighted the importance of policy making around EVs, which can offset EV market share by up to 10% between two countries following similar sustainability goals. Therefore, the study also helps aid decision making around policies and technology investments by public and private sector organizations in the space of EV.

Suggested Citation

  • Abhinav Tiwari & Hany Farag, 2022. "Analysis and Modeling of Value Creation Opportunities and Governing Factors for Electric Vehicle Proliferation," Energies, MDPI, vol. 16(1), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:438-:d:1020409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/438/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/438/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    2. Powell, Siobhan & Kara, Emre Can & Sevlian, Raffi & Cezar, Gustavo Vianna & Kiliccote, Sila & Rajagopal, Ram, 2020. "Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging," Applied Energy, Elsevier, vol. 276(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.
    2. Dominic Savio Abraham & Balaji Chandrasekar & Narayanamoorthi Rajamanickam & Pradeep Vishnuram & Venkatesan Ramakrishnan & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station," Energies, MDPI, vol. 16(6), pages 1-17, March.
    3. Piotr Bielaczyc & Rafal Sala & Tomasz Meinicke, 2021. "Analysis of Technical Capabilities, Methodology and Test Results of a Light-Commercial Vehicle Conversion to Battery Electric Powertrain," Energies, MDPI, vol. 14(4), pages 1-18, February.
    4. Behzad Zargar & Ting Wang & Manuel Pitz & Rainer Bachmann & Moritz Maschmann & Angelina Bintoudi & Lampros Zyglakis & Ferdinanda Ponci & Antonello Monti & Dimosthenis Ioannidis, 2021. "Power Quality Improvement in Distribution Grids via Real-Time Smart Exploitation of Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-26, June.
    5. Dominik Husarek & Vjekoslav Salapic & Simon Paulus & Michael Metzger & Stefan Niessen, 2021. "Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration," Energies, MDPI, vol. 14(23), pages 1-27, November.
    6. Mohammad Hossein Fouladfar & Nagham Saeed & Mousa Marzband & Giuseppe Franchini, 2021. "Home-Microgrid Energy Management Strategy Considering EV’s Participation in DR," Energies, MDPI, vol. 14(18), pages 1-12, September.
    7. Ahmed Abdu Alattab & Reyazur Rashid Irshad & Anwar Ali Yahya & Amin A. Al-Awady, 2022. "Privacy Protected Preservation of Electric Vehicles’ Data in Cloud Computing Using Secure Data Access Control," Energies, MDPI, vol. 15(21), pages 1-13, October.
    8. Peter Tauš & Marcela Taušová & Peter Sivák & Mária Shejbalová Muchová & Eva Mihaliková, 2020. "Parameter Optimization Model Photovoltaic Battery System for Charging Electric Cars," Energies, MDPI, vol. 13(17), pages 1-17, September.
    9. Shiping Xu & Lili Wang, 2023. "Do Green Information and Communication Technologies (ICT) and Smart Urbanization Reduce Environmental Pollution in China?," Sustainability, MDPI, vol. 15(19), pages 1-18, October.
    10. Kyo Beom Han & Jaesung Jung & Byung O Kang, 2021. "Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea," Energies, MDPI, vol. 14(19), pages 1-17, October.
    11. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    12. Kang, Zixuan & Ye, Zhongnan & Lam, Chor-Man & Hsu, Shu-Chien, 2023. "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving," Applied Energy, Elsevier, vol. 349(C).
    13. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    14. Daneshzand, Farzaneh & Coker, Phil J & Potter, Ben & Smith, Stefan T, 2023. "EV smart charging: How tariff selection influences grid stress and carbon reduction," Applied Energy, Elsevier, vol. 348(C).
    15. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    16. Hussain, Shahid & Teni, Abhishek Prasad & Hussain, Ihtisham & Hussain, Zakir & Pallonetto, Fabiano & Eichman, Josh & Irshad, Reyazur Rashid & Alwayle, Ibrahim M. & Alharby, Maher & Hussain, Md Asdaque, 2024. "Enhancing electric vehicle charging efficiency at the aggregator level: A deep-weighted ensemble model for wholesale electricity price forecasting," Energy, Elsevier, vol. 308(C).
    17. Moradi Amani, A. & Sajjadi, S.S. & Al Khafaf, N. & Song, H. & Jalili, M. & Yu, X. & Meegahapola, L. & McTaggart, P., 2023. "Technology balancing for reliable EV uptake in distribution grids: An Australian case study," Renewable Energy, Elsevier, vol. 206(C), pages 939-948.
    18. Leippi, Andre & Fleschutz, Markus & Davis, Kevin & Klingler, Anna-Lena & Murphy, Michael D., 2024. "Optimizing electric vehicle fleet integration in industrial demand response: Maximizing vehicle-to-grid benefits while compensating vehicle owners for battery degradation," Applied Energy, Elsevier, vol. 374(C).
    19. Ildar Daminov & Rémy Rigo-Mariani & Raphael Caire & Anton Prokhorov & Marie-Cécile Alvarez-Hérault, 2021. "Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime," Energies, MDPI, vol. 14(5), pages 1-27, March.
    20. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:438-:d:1020409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.