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Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

Author

Listed:
  • George S. Fernandez

    (Department of Electrical and Electronics Engineering, SRM University, Kattankulathur 603-203, India)

  • Vijayakumar Krishnasamy

    (Department of Electrical and Electronics Engineering, SRM University, Kattankulathur 603-203, India)

  • Selvakumar Kuppusamy

    (Department of Electrical and Electronics Engineering, SRM University, Kattankulathur 603-203, India)

  • Jagabar S. Ali

    (Department of Electrical and Electronics Engineering, SRM University, Kattankulathur 603-203, India
    Renewable Energy Lab (REL), Department of Communication and Networks, College of Engineering, Prince Sultan University (PSU), Riyadh 11586, Saudi Arabia)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11991, Saudi Arabia
    Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Adel El-Shahat

    (Department of Electrical and Computer Engineering, Georgia Southern University (GSU), Statesboro, GA 30460-7995, USA)

  • Shady H. E. Abdel Aleem

    (Power Quality Department, ETA Electric Company, El Omraniya, Giza 12111, Egypt)

Abstract

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Suggested Citation

  • George S. Fernandez & Vijayakumar Krishnasamy & Selvakumar Kuppusamy & Jagabar S. Ali & Ziad M. Ali & Adel El-Shahat & Shady H. E. Abdel Aleem, 2020. "Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm," Energies, MDPI, vol. 13(23), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6384-:d:455449
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    References listed on IDEAS

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    1. Abdel Aleem, Shady H.E. & Zobaa, Ahmed F. & Abdel Mageed, Hala M., 2015. "Assessment of energy credits for the enhancement of the Egyptian Green Pyramid Rating System," Energy Policy, Elsevier, vol. 87(C), pages 407-416.
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    3. Mohammadi Landi, Meysam & Mohammadi, Mohammad & Rastegar, Mohammad, 2018. "Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems," Energy, Elsevier, vol. 158(C), pages 504-511.
    4. Su, Wencong & Chow, Mo-Yuen, 2012. "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, Elsevier, vol. 96(C), pages 171-182.
    5. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "Operational scheduling of electric vehicles parking lot integrated with renewable generation based on bilevel programming approach," Energy, Elsevier, vol. 139(C), pages 422-432.
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    Citations

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    Cited by:

    1. Sara Hsaini & Mounir Ghogho & My El Hassan Charaf, 2022. "An OCPP-Based Approach for Electric Vehicle Charging Management," Energies, MDPI, vol. 15(18), pages 1-14, September.
    2. Suchitra Dayalan & Sheikh Suhaib Gul & Rajarajeswari Rathinam & George Fernandez Savari & Shady H. E. Abdel Aleem & Mohamed A. Mohamed & Ziad M. Ali, 2022. "Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
    3. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    4. Cong Chen & Yibai Li & Guangqiao Cao & Jinlong Zhang, 2023. "Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm," Sustainability, MDPI, vol. 15(3), pages 1-20, January.

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