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Distributed Charging Prioritization Methodology Based on Evolutionary Computation and Virtual Power Plants to Integrate Electric Vehicle Fleets on Smart Grids

Author

Listed:
  • J.I. Guerrero

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

  • Enrique Personal

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

  • Antonio García

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

  • Antonio Parejo

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

  • Francisco Pérez

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

  • Carlos León

    (Department of Electronic Technology, University of Seville, EPS. C/Virgen de África, 7, 41011 Seville, Spain)

Abstract

Electric vehicle fleets and smart grids are two growing technologies. These technologies have provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, a comparison of several evolutionary algorithms—namely genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution—is shown, in order to evaluate the proposed architecture. The proposed solution is presented as a means to prevent overload of the power grid.

Suggested Citation

  • J.I. Guerrero & Enrique Personal & Antonio García & Antonio Parejo & Francisco Pérez & Carlos León, 2019. "Distributed Charging Prioritization Methodology Based on Evolutionary Computation and Virtual Power Plants to Integrate Electric Vehicle Fleets on Smart Grids," Energies, MDPI, vol. 12(12), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2402-:d:242143
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    References listed on IDEAS

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    1. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
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    5. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    6. Bachmat, Eitan, 2019. "Airplane boarding meets express line queues," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1165-1177.
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    Cited by:

    1. Diego Francisco Larios & Enrique Personal & Antonio Parejo & Sebastián García & Antonio García & Carlos Leon, 2020. "Operational Simulation Environment for SCADA Integration of Renewable Resources," Energies, MDPI, vol. 13(6), pages 1-37, March.
    2. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.
    3. Tepe, Benedikt & Figgener, Jan & Englberger, Stefan & Sauer, Dirk Uwe & Jossen, Andreas & Hesse, Holger, 2022. "Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets," Applied Energy, Elsevier, vol. 308(C).

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