IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2752-d175588.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2752/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2752/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
    2. Rahman, Imran & Vasant, Pandian M. & Singh, Balbir Singh Mahinder & Abdullah-Al-Wadud, M. & Adnan, Nadia, 2016. "Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1039-1047.
    3. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    4. Sung-Guk Yoon & Seok-Gu Kang, 2017. "Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands," Energies, MDPI, vol. 10(10), pages 1-16, September.
    5. Weige Zhang & Di Zhang & Biqiang Mu & Le Yi Wang & Yan Bao & Jiuchun Jiang & Hugo Morais, 2017. "Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids," Energies, MDPI, vol. 10(2), pages 1-19, January.
    6. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    7. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2017. "When do you charge your electric vehicle? A stated adaptation approach," Energy Policy, Elsevier, vol. 108(C), pages 565-573.
    8. Hu, Zechun & Zhan, Kaiqiao & Zhang, Hongcai & Song, Yonghua, 2016. "Pricing mechanisms design for guiding electric vehicle charging to fill load valley," Applied Energy, Elsevier, vol. 178(C), pages 155-163.
    9. Umetani, Shunji & Fukushima, Yuta & Morita, Hiroshi, 2017. "A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system," Omega, Elsevier, vol. 67(C), pages 115-122.
    10. García-Villalobos, J. & Zamora, I. & San Martín, J.I. & Asensio, F.J. & Aperribay, V., 2014. "Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 717-731.
    11. Sidra Mumtaz & Saima Ali & Saghir Ahmad & Laiq Khan & Syed Zulqadar Hassan & Tariq Kamal, 2017. "Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System," Energies, MDPI, vol. 10(11), pages 1-21, November.
    12. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    13. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    14. Cuiyu Kong & Raka Jovanovic & Islam Safak Bayram & Michael Devetsikiotis, 2017. "A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations," Energies, MDPI, vol. 10(5), pages 1-20, May.
    15. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    16. Wu, Fei & Sioshansi, Ramteen, 2017. "A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 55-82.
    17. Chunlin Guo & Jingjing Yang & Lin Yang, 2018. "Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply," Energies, MDPI, vol. 11(9), pages 1-17, September.
    18. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Ahmed Altameem & Torki Altameem & Ravi Sharma & Florin-Emilian Turcanu & Maria Simona Raboaca, 2022. "Blockchain and IoT-Driven Optimized Consensus Mechanism for Electric Vehicle Scheduling at Charging Stations," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Haider Ali & Inam Bari & Ben Horan & Saad Mekhilef & Muhammad Asif & Saeed Ahmed & Anzar Mahmood, 2020. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network," Sustainability, MDPI, vol. 12(23), pages 1-28, December.
    2. Gaizka Saldaña & Jose Ignacio San Martin & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation," Energies, MDPI, vol. 12(12), pages 1-37, June.
    3. Steffen Limmer, 2019. "Dynamic Pricing for Electric Vehicle Charging—A Literature Review," Energies, MDPI, vol. 12(18), pages 1-24, September.
    4. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    5. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    6. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    7. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    8. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    9. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    10. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    11. Dong, Xiaohong & Mu, Yunfei & Xu, Xiandong & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qi, Yan, 2018. "A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks," Applied Energy, Elsevier, vol. 225(C), pages 857-868.
    12. Zheng, Yanchong & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2019. "Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 424-439.
    13. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    14. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    15. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    16. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    17. Jun Bi & Yongxing Wang & Shuai Sun & Wei Guan, 2018. "Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing," Energies, MDPI, vol. 11(5), pages 1-18, April.
    18. Kayhan Alamatsaz & Sadam Hussain & Chunyan Lai & Ursula Eicker, 2022. "Electric Bus Scheduling and Timetabling, Fast Charging Infrastructure Planning, and Their Impact on the Grid: A Review," Energies, MDPI, vol. 15(21), pages 1-39, October.
    19. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2017. "When do you charge your electric vehicle? A stated adaptation approach," Energy Policy, Elsevier, vol. 108(C), pages 565-573.
    20. Hadi Suyono & Mir Toufikur Rahman & Hazlie Mokhlis & Mohamadariff Othman & Hazlee Azil Illias & Hasmaini Mohamad, 2019. "Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress," Energies, MDPI, vol. 12(8), pages 1-21, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2752-:d:175588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.