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Electric vehicle charging infrastructure planning model with energy management strategies considering EV parking behavior

Author

Listed:
  • Habib, Salman
  • Ahmad, Fareed
  • Gulzar, Muhammad Majid
  • Ahmed, Emad M.
  • Bilal, Mohd

Abstract

The automotive industry is experiencing a surge in electric vehicle (EV) popularity due to their environmental benefits, low carbon emissions, affordable maintenance, and cost-effective operation. However, this rise in EV numbers impacts distribution networks, affecting energy loss, bus voltage, grid stability, reliability, and harmonic distortion due to charging requirements. The increased peak demand on the grid from charging stations (CSs) poses a significant challenge. The location and capacity of CSs are influenced by investor decisions and EV user behavior. A multi-objective problem is formulated, considering the installation cost of charging infrastructure, energy loss in the electrical network, and EV users’ travel costs for charging. To mitigate grid demand, photovoltaic (PV) sources are proposed at distribution network nodes with CSs. Energy management strategies (EMSs) are implemented to efficiently control the discharging and charging of battery energy storage systems (BESS), aiming to minimize peak power demand and optimize PV source utilization. The proposed solution is tested on the IEEE-33 distribution network, with simulations running over 24 h. The Monte Carlo simulation (MCS) technique addresses uncertainties in PV generation and EV charging needs. Results show that incorporating EMSs at optimal CS locations reduces average energy loss by up to 15% and peak power demand from the grid by up to 20%. This approach effectively enhances the stability and efficiency of the distribution network while accommodating the growing presence of electric vehicles

Suggested Citation

  • Habib, Salman & Ahmad, Fareed & Gulzar, Muhammad Majid & Ahmed, Emad M. & Bilal, Mohd, 2025. "Electric vehicle charging infrastructure planning model with energy management strategies considering EV parking behavior," Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:energy:v:316:y:2025:i:c:s0360544225000635
    DOI: 10.1016/j.energy.2025.134421
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