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The Impact of V2G Charging/Discharging Strategy on the Microgrid Environment Considering Stochastic Methods

Author

Listed:
  • Sheeraz Iqbal

    (Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Salman Habib

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Muhammad Ali

    (Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Aqib Shafiq

    (Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Anis ur Rehman

    (Department of Electrical Engineering, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan)

  • Emad M. Ahmed

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Tahir Khurshaid

    (Department of Electrical Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Korea)

  • Salah Kamel

    (Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

Although electric vehicles (EVs) play a vital role in realizing remarkable features, however, the integration of a huge number of EVs leads to grid congestion as well. As a result, uncontrolled charging might give rise to undervoltage and complex congestion in the electric grid. The reasons for the uncontrolled charging of EVs have been investigated in the recent past to mitigate the effects thereof. It is very challenging to achieve controlled charging due to different constraints at the customer end; therefore, it is better to take the benefits of power prediction schemes for the charging and discharging of EVs. The power prediction scheme is based on a practical power forecast system that exploits the needs of various patterns, and the current research focuses on considering users’ demands. The primary objective of this study is to develop an effective and efficient coordination system for the charging and discharging of EVs by exploiting a smart algorithm that intelligently tackles the possible difficulties to attain optimum power requirements. In this context, a model is proposed based on stochastic methods for analyzing the impact of vehicle-to-grid (V2G) charging and discharging in the microgrid environment. A Markov model is used to simulate the use of EVs. This method works well with the Markov model because of its ability to adjust to random changes. When considering an EV, its erratic travel patterns suggest a string of events that resemble a stochastic process. The proposed model ensures that high power requirements are met during peak hours in a cost-effective manner. In simpler words, the promising features of the proposed scheme are to meet electricity/power demands, monitoring and the efficient forecasting of power. The outcomes revealed an effective power system, EV scheduling, and power supply without compromising the electric vehicle’s presentation of the EV owner’s tour schedule. In terms of comprehensiveness, the developed algorithm exhibits a significant improvement.

Suggested Citation

  • Sheeraz Iqbal & Salman Habib & Muhammad Ali & Aqib Shafiq & Anis ur Rehman & Emad M. Ahmed & Tahir Khurshaid & Salah Kamel, 2022. "The Impact of V2G Charging/Discharging Strategy on the Microgrid Environment Considering Stochastic Methods," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13211-:d:942324
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    References listed on IDEAS

    as
    1. Zhang, Ning & Yu, Keren & Chen, Zhongfei, 2017. "How does urbanization affect carbon dioxide emissions? A cross-country panel data analysis," Energy Policy, Elsevier, vol. 107(C), pages 678-687.
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    3. Blanco, Herib & Faaij, André, 2018. "A review at the role of storage in energy systems with a focus on Power to Gas and long-term storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1049-1086.
    4. Salman Habib & Ghulam Abbas & Touqeer A. Jumani & Aqeel Ahmed Bhutto & Sohrab Mirsaeidi & Emad M. Ahmed, 2022. "Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System," Energies, MDPI, vol. 15(14), pages 1-18, July.
    5. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
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    Cited by:

    1. Zirui Wang & Bowen Zhou & Chen Lv & Hongming Yang & Quan Ma & Zhao Yang & Yong Cui, 2023. "Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    2. Rehman, Anis Ur & Lu, Junwei & Du, Bo & Bai, Feifei & Sanjari, Mohammad J., 2025. "Efficient Management of Electric Vehicle Charging Stations: Balancing user preferences and grid demands with energy storage systems and renewable energy," Applied Energy, Elsevier, vol. 393(C).

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