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A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration

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  • Bahman Ahmadi

    (Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands)

  • Elham Shirazi

    (Department of Engineering Technology, University of Twente, 7522 NB Enschede, The Netherlands)

Abstract

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

Suggested Citation

  • Bahman Ahmadi & Elham Shirazi, 2023. "A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration," Energies, MDPI, vol. 16(19), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6959-:d:1254102
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