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Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment

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  • De Gennaro, Michele
  • Paffumi, Elena
  • Martini, Giorgio

Abstract

The large scale deployment of electric vehicles in urban environment will play a key-role over the next decades to reduce air-pollutants in densely populated areas, but it will also require the development of an adequate recharge infrastructure. The purpose of this paper is to demonstrate how driving patterns databases and data mining can be used to appropriately design this infrastructure. This application focuses on the Italian province of Firenze, involving about 12,000 conventional fuel vehicles monitored over one month, estimating a fleet share shift from conventional fuel vehicles to battery electric vehicles ranging from 10% to 57%, and a mileage share shift from 1.6% to 36.5%. The increase of electric energy demand from electric vehicles ranges from 0.7% to 18% of the total demand in the province, with a number of charging spots three-to-six times higher than the number of circulating electric vehicles. Additionally the results show that a Vehicle-to-Grid interaction strategy can contribute to reduce from 5% to 50% the average daily electric energy demand in specific locations. This paper provides a description of the developed model and focuses on the valuable potential of the proposed methodology to support future policies for designing alternative fuel infrastructure in urban areas.

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  • De Gennaro, Michele & Paffumi, Elena & Martini, Giorgio, 2015. "Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment," Energy, Elsevier, vol. 82(C), pages 294-311.
    • Handle: RePEc:eee:energy:v:82:y:2015:i:c:p:294-311
    DOI: 10.1016/j.energy.2015.01.039
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    Cited by:

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    4. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    5. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    6. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    7. Mario Porru & Alessandro Serpi & Mario Mureddu & Alfonso Damiano, 2020. "A Multistage Design Procedure for Planning and Implementing Public Charging Infrastructures for Electric Vehicles," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    8. Zou, Yuan & Wei, Shouyang & Sun, Fengchun & Hu, Xiaosong & Shiao, Yaojung, 2016. "Large-scale deployment of electric taxis in Beijing: A real-world analysis," Energy, Elsevier, vol. 100(C), pages 25-39.
    9. Sodenkamp, Mariya & Wenig, Jürgen & Thiesse, Frédéric & Staake, Thorsten, 2019. "Who can drive electric? Segmentation of car drivers based on longitudinal GPS travel data," Energy Policy, Elsevier, vol. 130(C), pages 111-129.
    10. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    11. Fabienne T. Schiavo & Rodrigo F. Calili & Claudio F. de Magalhães & Isabel C. G. Fróes, 2021. "The Meaning of Electric Cars in the Context of Sustainable Transition in Brazil," Sustainability, MDPI, vol. 13(19), pages 1-24, October.
    12. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    13. Davidov, Sreten & Pantoš, Miloš, 2017. "Planning of electric vehicle infrastructure based on charging reliability and quality of service," Energy, Elsevier, vol. 118(C), pages 1156-1167.
    14. Claudio Cubito & Federico Millo & Giulio Boccardo & Giuseppe Di Pierro & Biagio Ciuffo & Georgios Fontaras & Simone Serra & Marcos Otura Garcia & Germana Trentadue, 2017. "Impact of Different Driving Cycles and Operating Conditions on CO 2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle," Energies, MDPI, vol. 10(10), pages 1-18, October.
    15. Fathabadi, Hassan, 2015. "Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems," Energy, Elsevier, vol. 90(P1), pages 1100-1110.
    16. Paolo Sospiro & Lohith Amarnath & Vincenzo Di Nardo & Giacomo Talluri & Foad H. Gandoman, 2021. "Smart Grid in China, EU, and the US: State of Implementation," Energies, MDPI, vol. 14(18), pages 1-16, September.
    17. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    18. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    19. Milan Straka & Pasquale De Falco & Gabriella Ferruzzi & Daniela Proto & Gijs van der Poel & Shahab Khormali & v{L}ubov{s} Buzna, 2019. "Predicting popularity of EV charging infrastructure from GIS data," Papers 1910.02498, arXiv.org.
    20. Paffumi, Elena & De Gennaro, Michele & Martini, Giorgio, 2018. "Alternative utility factor versus the SAE J2841 standard method for PHEV and BEV applications," Transport Policy, Elsevier, vol. 68(C), pages 80-97.
    21. Qiu, Y.Q. & Zhou, P. & Sun, H.C., 2019. "Assessing the effectiveness of city-level electric vehicle policies in China," Energy Policy, Elsevier, vol. 130(C), pages 22-31.

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