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Scenario generation for electric vehicles' uncertain behavior in a smart city environment

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  • Soares, João
  • Borges, Nuno
  • Fotouhi Ghazvini, Mohammad Ali
  • Vale, Zita
  • de Moura Oliveira, P.B.

Abstract

This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.

Suggested Citation

  • Soares, João & Borges, Nuno & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "Scenario generation for electric vehicles' uncertain behavior in a smart city environment," Energy, Elsevier, vol. 111(C), pages 664-675.
    • Handle: RePEc:eee:energy:v:111:y:2016:i:c:p:664-675
    DOI: 10.1016/j.energy.2016.06.011
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    7. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    8. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    9. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
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