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Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

Author

Listed:
  • Kang Miao Tan

    (Power Quality Research Group, Institute of Power Engineering, Department of Electrical Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor 43000, Malaysia)

  • Vigna K. Ramachandaramurthy

    (Power Quality Research Group, Institute of Power Engineering, Department of Electrical Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor 43000, Malaysia)

  • Jia Ying Yong

    (Power Quality Research Group, Institute of Power Engineering, Department of Electrical Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor 43000, Malaysia)

  • Sanjeevikumar Padmanaban

    (Department of Electrical and Electronics Engineering, University of Johannesburg, 2006 Auckland Park, South Africa)

  • Lucian Mihet-Popa

    (Faculty of Engineering, Østfold University College, Kobberslagerstredet 5, 1671 Kråkeroy-Fredrikstad, Norway)

  • Frede Blaabjerg

    (Centre for Reliable Power Electronics (CORPE), Department of Energy Technology, Aalborg University, 9000 Aalborg, Denmark)

Abstract

The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

Suggested Citation

  • Kang Miao Tan & Vigna K. Ramachandaramurthy & Jia Ying Yong & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg, 2017. "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling," Energies, MDPI, vol. 10(11), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1880-:d:119050
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    References listed on IDEAS

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    Cited by:

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    2. Solanke, Tirupati U. & Khatua, Pradeep K. & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao, 2021. "Control and management of a multilevel electric vehicles infrastructure integrated with distributed resources: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Shahid Hussain & Ki-Beom Lee & Mohamed A. Ahmed & Barry Hayes & Young-Chon Kim, 2020. "Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots," Energies, MDPI, vol. 13(18), pages 1-31, September.
    4. Theron Smith & Joseph Garcia & Gregory Washington, 2022. "Novel PEV Charging Approaches for Extending Transformer Life," Energies, MDPI, vol. 15(12), pages 1-17, June.

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