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Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

Author

Listed:
  • Shahid Hussain

    (Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea)

  • Ki-Beom Lee

    (Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea)

  • Mohamed A. Ahmed

    (Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
    Department of Communications and Electronics, Higher Institute of Engineering and Technology–King Marriott, Alexandria 23713, Egypt)

  • Barry Hayes

    (School of Engineering, University College Cork, Cork T12K8AF, Ireland)

  • Young-Chon Kim

    (Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea)

Abstract

The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.

Suggested Citation

  • Shahid Hussain & Ki-Beom Lee & Mohamed A. Ahmed & Barry Hayes & Young-Chon Kim, 2020. "Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots," Energies, MDPI, vol. 13(18), pages 1-31, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4634-:d:409717
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    References listed on IDEAS

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    Cited by:

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    5. Magoua, Joseph Jonathan & Li, Nan, 2023. "The human factor in the disaster resilience modeling of critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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