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Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

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  • Bruno Canizes

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • João Soares

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Angelo Costa

    (ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal)

  • Tiago Pinto

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Fernando Lezama

    (GECAD—Knowledge Engineering and Decision Support Research Center—Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Paulo Novais

    (ALGORITMI Centre, University of Minho, 4710-057 Braga, Portugal)

  • Zita Vale

    (Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

Abstract

The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.

Suggested Citation

  • Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1470-:d:224008
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    References listed on IDEAS

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    9. Marta Borowska-Stefańska & Michał Kowalski & Paulina Kurzyk & Miroslava Mikušová & Szymon Wiśniewski, 2021. "Privileging Electric Vehicles as an Element of Promoting Sustainable Urban Mobility—Effects on the Local Transport System in a Large Metropolis in Poland," Energies, MDPI, vol. 14(13), pages 1-24, June.
    10. Marialisa Nigro & Marina Ferrara & Rosita De Vincentis & Carlo Liberto & Gaetano Valenti, 2021. "Data Driven Approaches for Sustainable Development of E-Mobility in Urban Areas," Energies, MDPI, vol. 14(13), pages 1-19, July.
    11. Maria Richert & Marek Dudek, 2023. "Selected Problems of the Automotive Industry—Material and Economic Risk," JRFM, MDPI, vol. 16(8), pages 1-14, August.
    12. Armin Razmjoo & Meysam Majidi Nezhad & Lisa Gakenia Kaigutha & Mousa Marzband & Seyedali Mirjalili & Mehdi Pazhoohesh & Saim Memon & Mehdi A. Ehyaei & Giuseppe Piras, 2021. "Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    13. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    14. Tobias Rodemann & Tom Eckhardt & René Unger & Torsten Schwan, 2019. "Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems," Energies, MDPI, vol. 12(15), pages 1-16, July.
    15. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.
    16. Daniele Menniti & Anna Pinnarelli & Nicola Sorrentino & Pasquale Vizza & Giovanni Brusco & Giuseppe Barone & Gianluca Marano, 2022. "Techno Economic Analysis of Electric Vehicle Grid Integration Aimed to Provide Network Flexibility Services in Italian Regulatory Framework," Energies, MDPI, vol. 15(7), pages 1-34, March.
    17. Poyrazoglu, Gokturk & Coban, Elvin, 2021. "A stochastic value estimation tool for electric vehicle charging points," Energy, Elsevier, vol. 227(C).
    18. Michel Noussan & Francesco Neirotti, 2020. "Cross-Country Comparison of Hourly Electricity Mixes for EV Charging Profiles," Energies, MDPI, vol. 13(10), pages 1-14, May.

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