IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6697-d656991.html
   My bibliography  Save this article

A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study

Author

Listed:
  • Paolo Lazzeroni

    (Fondazione LINKS, Via Pier Carlo Boggio, 61, 10138 Torino, Italy)

  • Brunella Caroleo

    (Fondazione LINKS, Via Pier Carlo Boggio, 61, 10138 Torino, Italy)

  • Maurizio Arnone

    (Fondazione LINKS, Via Pier Carlo Boggio, 61, 10138 Torino, Italy)

  • Cristiana Botta

    (Fondazione LINKS, Via Pier Carlo Boggio, 61, 10138 Torino, Italy)

Abstract

The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO).

Suggested Citation

  • Paolo Lazzeroni & Brunella Caroleo & Maurizio Arnone & Cristiana Botta, 2021. "A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study," Energies, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6697-:d:656991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6697/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6697/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Burd, Joshua Thomas Jameson & Moore, Elizabeth A. & Ezzat, Hesham & Kirchain, Randolph & Roth, Richard, 2021. "Improvements in electric vehicle battery technology influence vehicle lightweighting and material substitution decisions," Applied Energy, Elsevier, vol. 283(C).
    2. Scorrano, Mariangela & Danielis, Romeo & Giansoldati, Marco, 2020. "Dissecting the total cost of ownership of fully electric cars in Italy: The impact of annual distance travelled, home charging and urban driving," Research in Transportation Economics, Elsevier, vol. 80(C).
    3. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Anders F. Jensen & Elisabetta Cherchi & Stefan L. Mabit & Juan de Dios Ortúzar, 2017. "Predicting the Potential Market for Electric Vehicles," Transportation Science, INFORMS, vol. 51(2), pages 427-440, May.
    6. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    7. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    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. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    2. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    3. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    4. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    5. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    6. Lu Xiao & Feiyue Yang & Yong Yang & Che Chen & Wuer Ha, 2024. "A Sustainable Production Planning Scheme for New Energy Vehicles in China," Sustainability, MDPI, vol. 16(19), pages 1-24, September.
    7. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    8. Ma, Peng, 2021. "Optimal generic and brand advertising efforts in a decentralized supply chain considering customer surplus," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    9. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    10. Klingler, Anna-Lena & Luthander, Rasmus, 2018. "Market diffusion of residential PV and battery systems driven by self-consumption: A comparison of Sweden and Germany," Working Papers "Sustainability and Innovation" S18/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    11. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "A segment-based analysis of Internet service adoption among UK households," Technology in Society, Elsevier, vol. 29(3), pages 339-350.
    12. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    13. Liberali, Guilherme & Gruca, Thomas S. & Nique, Walter M., 2011. "The effects of sensitization and habituation in durable goods markets," European Journal of Operational Research, Elsevier, vol. 212(2), pages 398-410, July.
    14. Desreveaux, A. & Bouscayrol, A. & Trigui, R. & Hittinger, E. & Castex, E. & Sirbu, G.M., 2023. "Accurate energy consumption for comparison of climate change impact of thermal and electric vehicles," Energy, Elsevier, vol. 268(C).
    15. Chul-Yong Lee & Jongsu Lee, 2009. "Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update," TEMEP Discussion Papers 200903, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2009.
    16. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    17. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
    18. Jakob Grazzini & Matteo G. Richiardi & Lisa Sella, 2013. "Analysis of Agent-based Models," LABORatorio R. Revelli Working Papers Series 135, LABORatorio R. Revelli, Centre for Employment Studies.
    19. Bessi, Alessandro & Guidolin, Mariangela & Manfredi, Piero, 2021. "The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    20. White, Reilly & Marinakis, Yorgos & Islam, Nazrul & Walsh, Steven, 2020. "Is Bitcoin a currency, a technology-based product, or something else?," Technological Forecasting and Social Change, Elsevier, vol. 151(C).

    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:14:y:2021:i:20:p:6697-:d:656991. 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.