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Economic and Technical Aspects of Power Grids with Electric Vehicle Charge Stations, Sustainable Energies, and Compensators

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  • Minh Phuc Duong

    (Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • My-Ha Le

    (Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City 700000, Vietnam)

  • Thang Trung Nguyen

    (Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Minh Quan Duong

    (University of Science and Technology, The University of Danang, Danang City 550000, Vietnam)

  • Anh Tuan Doan

    (University of Science and Technology, The University of Danang, Danang City 550000, Vietnam)

Abstract

The study applies the black kite algorithm (BKA), equilibrium optimizer (EO), and secretary bird optimization algorithm (SBOA) to optimize the placement of electric vehicle charge stations (EVCSs), wind turbine stations (WTSs), photovoltaic units (PVUs), and capacitor banks (CAPBs) in the IEEE 69-node distribution power grid. Three single objectives, including power loss minimization, grid power minimization, and total voltage deviation improvement, are considered. For each objective function, five scenarios are simulated under one single operation hour, including (1) place-only EVCSs; (2) place EVCSs and PVUs; (3) place EVCSs, PVUs, and CAPBs; (4) EVCSs and WTSs; and (5) EVCSs, PVUs, WTSs, and CAPBs. The results indicate that the EO can find the best solutions for the five scenarios. The results indicate that the EO and SBOA are the two powerful algorithms that can find optimal solutions for simulation cases. For one operating day, the total grid energy that is supplied to base loads and charge stations is 80,153.1 kWh, and many nodes at high load factors violate the lower limit of 0.95 pu. As for installing more renewable power sources, the energy that the base loads and charge stations need to supply from the grid is 39,713.4 kWh. As more capacitor banks are installed, the energy demand continues to be reduced to 39,578.9 kWh. The energy reduction is greater than 50% of the demand of all base loads and charge stations. Furthermore, the voltage can be significantly improved up to higher than 0.95 pu, and a few nodes at a few hours fall into the lowest range. Thus, the study concludes that the economic and technical aspects can be guaranteed for DPGs with additional installation of EVCSs.

Suggested Citation

  • Minh Phuc Duong & My-Ha Le & Thang Trung Nguyen & Minh Quan Duong & Anh Tuan Doan, 2025. "Economic and Technical Aspects of Power Grids with Electric Vehicle Charge Stations, Sustainable Energies, and Compensators," Sustainability, MDPI, vol. 17(1), pages 1-32, January.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:376-:d:1561312
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    References listed on IDEAS

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    1. Riesz, Jenny & Sotiriadis, Claire & Ambach, Daisy & Donovan, Stuart, 2016. "Quantifying the costs of a rapid transition to electric vehicles," Applied Energy, Elsevier, vol. 180(C), pages 287-300.
    2. Petchrompo, Sanyapong & Wannakrairot, Anupong & Parlikad, Ajith Kumar, 2022. "Pruning Pareto optimal solutions for multi-objective portfolio asset management," European Journal of Operational Research, Elsevier, vol. 297(1), pages 203-220.
    3. Nandini K. Krishnamurthy & Jayalakshmi N. Sabhahit & Vinay Kumar Jadoun & Dattatraya Narayan Gaonkar & Ashish Shrivastava & Vidya S. Rao & Ganesh Kudva, 2023. "Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method," Energies, MDPI, vol. 16(4), pages 1-27, February.
    4. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    5. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    6. Francisco Haces-Fernandez, 2023. "Risk Assessment Framework for Electric Vehicle Charging Station Development in the United States as an Ancillary Service," Energies, MDPI, vol. 16(24), pages 1-17, December.
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