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Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm

Author

Listed:
  • Jorge García Álvarez

    (Department of Computer Science, University of Oviedo, 33204 Gijón, Spain)

  • Miguel Ángel González

    (Department of Computer Science, University of Oviedo, 33204 Gijón, Spain)

  • Camino Rodríguez Vela

    (Department of Computer Science, University of Oviedo, 33204 Gijón, Spain)

  • Ramiro Varela

    (Department of Computer Science, University of Oviedo, 33204 Gijón, Spain)

Abstract

Scheduling the charging times of a large fleet of Electric Vehicles (EVs) may be a hard problem due to the physical structure and conditions of the charging station. In this paper, we tackle an EV’s charging scheduling problem derived from a charging station designed to be installed in community parking where each EV has its own parking lot. The main goals are to satisfy the user demands and at the same time to make the best use of the available power. To solve the problem, we propose an artificial bee colony (ABC) algorithm enhanced with local search and some mating strategies borrowed from genetic algorithms. The proposal is analyzed experimentally by simulation and compared with other methods previously proposed for the same problem. The results of the experimental study provided interesting insights about the problem and showed that the proposed algorithm is quite competitive with previous methods.

Suggested Citation

  • Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2752-:d:175588
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    References listed on IDEAS

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    2. Davide Falabretti & Francesco Gulotta, 2022. "A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    3. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    4. Praveen Prakash Singh & Fushuan Wen & Ivo Palu & Sulabh Sachan & Sanchari Deb, 2022. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions," Energies, MDPI, vol. 16(1), pages 1-21, December.
    5. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).

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