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Development of an Energy Management System for the Charge Scheduling of Plug-in Electric Vehicles

  • Dario Cruz
  • Nelson Pinto
  • Jânio MonteiroEmail author
  • Pedro J. S. Cardoso
  • Cristiano Cabrita
  • Jorge Semião
  • Luís M. R. Oliveira
  • João M. F. Rodrigues
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10908)

Abstract

As the number of Plug-In Electric Vehicles continues to rise, existing electrical grids need to adapt to support the expected charging demand of such vehicles. Fortunately, a growing number of renewable energy sources are also being introduced in current electrical grids, reducing the dependency on fossil fuels. Leveraged by the self-consumption legislation in several countries, the introduction of renewable energy sources continue to happen well beyond the end of the feed-in tariff rates. However, due to their variable nature, renewable energy sources are frequently characterized as intermittent resources, which cause mismatches in the required equilibrium between production and demand. In this scenario, the role of end users is very important, since they are not only required to participate in energy generation - becoming the so-called prosumers – but also they should allow the adjustment of the consumption, according with the generation levels. Plug-In Electric Vehicles, due to their power requirements, just exacerbate this problem. Following our previous work concerning scheduling algorithms for self-consumption scenarios, in this paper we describe the implementation of an Energy Management System for the charge scheduling of Electric Vehicles. The proposed system considers several requirements, including electrical grid limitations, present time and subsequent tariff costs, actual and predicted renewable energy generation levels, and user preferences. It then runs an optimization algorithm that decides when the charging of such vehicles should happen and controls the power delivered to charge them, accordingly.

Keywords

Plug-in electric vehicles Load scheduling 

Notes

Acknowledgments

This work was supported by the European Union, under the FEDER (Fundo Europeu de Desenvolvimento Regional) and INTERREG programs, in the scope of the AGERAR (0076_AGERAR_6_E) project, and by the Portuguese Foundation for Science and Technology (FCT), project LARSyS (UID/EEA/50009/2013).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dario Cruz
    • 1
  • Nelson Pinto
    • 1
  • Jânio Monteiro
    • 1
    • 2
    Email author
  • Pedro J. S. Cardoso
    • 1
    • 3
  • Cristiano Cabrita
    • 1
    • 4
  • Jorge Semião
    • 1
    • 2
  • Luís M. R. Oliveira
    • 1
    • 5
  • João M. F. Rodrigues
    • 1
    • 3
  1. 1.ISEUniversity of AlgarveFaroPortugal
  2. 2.INESC-IDLisbonPortugal
  3. 3.LARSySLisbonPortugal
  4. 4.Centre of Intelligent SystemsIDMEC, ISTLisbonPortugal
  5. 5.CISE - Electromechatronic Systems Research CentreCovilhãPortugal

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