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A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

Author

Listed:
  • Shahid Hussain

    (Department of Electrical and Electronic Engineering, Data Science Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland)

  • Subhasis Thakur

    (Department of Electrical and Electronic Engineering, Data Science Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland)

  • Saurabh Shukla

    (Department of Electrical and Electronic Engineering, Data Science Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland)

  • John G. Breslin

    (Department of Electrical and Electronic Engineering, Data Science Institute, National University of Ireland Galway, H91 TK33 Galway, Ireland)

  • Qasim Jan

    (National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
    Department of Computer Science, Attock Campus, COMSATS University Islamabad, Attock 43600, Pakistan)

  • Faisal Khan

    (College of Science and Engineering, National University of Ireland Galway, H91 TK33 Galway, Ireland)

  • Ibrar Ahmad

    (Department of Computer Science, Shangla Campus, University of Swat, Shangla 19100, Pakistan)

  • Mousa Marzband

    (Mathematics, Physics and Electrical Engineering, City Campus, Northumbria University, Newcastle upon Tyne NE1 8ST, UK)

  • Michael G. Madden

    (School of Computer Science, National University of Ireland Galway, H91 TK33 Galway, Ireland)

Abstract

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Suggested Citation

  • Shahid Hussain & Subhasis Thakur & Saurabh Shukla & John G. Breslin & Qasim Jan & Faisal Khan & Ibrar Ahmad & Mousa Marzband & Michael G. Madden, 2022. "A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1304-:d:746978
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    References listed on IDEAS

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    Cited by:

    1. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).
    2. Ahmed Abdu Alattab & Reyazur Rashid Irshad & Anwar Ali Yahya & Amin A. Al-Awady, 2022. "Privacy Protected Preservation of Electric Vehicles’ Data in Cloud Computing Using Secure Data Access Control," Energies, MDPI, vol. 15(21), pages 1-13, October.

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