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A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

Author

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  • Muhammad Usman

    (Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, Pakistan)

  • Wajahat Ullah Khan Tareen

    (Department of Electrical and Electronic Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia)

  • Adil Amin

    (Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, Pakistan)

  • Haider Ali

    (Department of Electrical and Electronic Engineering Technology, University of Technology Nowshera, Nowshera 24100, Pakistan)

  • Inam Bari

    (Department of System Engineering, Military Technological College, Muscat 111, Oman)

  • Muhammad Sajid

    (Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, Pakistan)

  • Mehdi Seyedmahmoudian

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia)

  • Alex Stojcevski

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia)

  • Anzar Mahmood

    (Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, Pakistan)

  • Saad Mekhilef

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122, Australia
    Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Electric vehicles’ (EVs) technology is currently emerging as an alternative of traditional Internal Combustion Engine (ICE) vehicles. EVs have been treated as an efficient way for decreasing the production of harmful greenhouse gasses and saving the depleting natural oil reserve. The modern power system tends to be more sustainable with the support of electric vehicles (EVs). However, there have been serious concerns about the network’s safe and reliable operation due to the increasing penetration of EVs into the electric grid. Random or uncoordinated charging activities cause performance degradations and overloading of the network asset. This paper proposes an Optimal Charging Starting Time (OCST)-based coordinated charging algorithm for unplanned EVs’ arrival in a low voltage residential distribution network to minimize the network power losses. A time-of-use (ToU) tariff scheme is used to make the charging course more cost effective. The concept of OCST takes the departure time of EVs into account and schedules the overnight charging event in such a way that minimum network losses are obtained, and EV customers take more advantages of cost-effective tariff zones of ToU scheme. An optimal solution is obtained by employing Binary Evolutionary Programming (BEP). The proposed algorithm is tested on IEEE-31 bus distribution system connected to numerous low voltage residential feeders populated with different EVs’ penetration levels. The results obtained from the coordinated EV charging without OCST are compared with those employing the concept of OCST. The results verify that incorporation of OCST can significantly reduce network power losses, improve system voltage profile and can give more benefits to the EV customers by accommodating them into low-tariff zones.

Suggested Citation

  • Muhammad Usman & Wajahat Ullah Khan Tareen & Adil Amin & Haider Ali & Inam Bari & Muhammad Sajid & Mehdi Seyedmahmoudian & Alex Stojcevski & Anzar Mahmood & Saad Mekhilef, 2021. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization," Energies, MDPI, vol. 14(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5336-:d:623591
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    References listed on IDEAS

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    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. 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.
    3. Chitchai Srithapon & Prasanta Ghosh & Apirat Siritaratiwat & Rongrit Chatthaworn, 2020. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    4. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
    5. Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    6. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    7. Zhou, Chao-bo & Qi, Shao-zhou & Zhang, Ji-hong & Tang, Si-yan, 2021. "Potential Co-benefit effect analysis of orderly charging and discharging of electric vehicles in China," Energy, Elsevier, vol. 226(C).
    8. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Kamran Ali Memon & Ben Horan & Anzar Mahmood & Saad Mekhilef, 2020. "An Integrated Approach to Optimal Charging Scheduling of Electric Vehicles Integrated with Improved Medium-Voltage Network Reconfiguration for Power Loss Minimization," Sustainability, MDPI, vol. 12(21), pages 1-15, November.
    9. 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.
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    Cited by:

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    2. Yong, Jin Yi & Tan, Wen Shan & Khorasany, Mohsen & Razzaghi, Reza, 2023. "Electric vehicles destination charging: An overview of charging tariffs, business models and coordination strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
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    4. Kłos, Marcin Jacek & Sierpiński, Grzegorz, 2023. "Siting of electric vehicle charging stations method addressing area potential and increasing their accessibility," Journal of Transport Geography, Elsevier, vol. 109(C).
    5. Anant Oonsivilai & Banyat Boribun & Padej Pao-la-or, 2023. "Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory," Energies, MDPI, vol. 16(7), pages 1-15, March.

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