A Coordinated Charging Strategy for Plug-in Hybrid Electric Vehicles with V2G to Improve Reliability Indices in Smart Distribution Systems

Authors

  • Meysam Faraj Fahimi * Telecommunication Company of Iran (TCI), Tonekabon, Iran.

https://doi.org/10.48314/imes.vi.42

Abstract

This paper investigates the impact of managed charging of Plug-in Hybrid Electric Vehicles (PHEVs) with Vehicle-to-Grid (V2G) capability on the reliability performance of smart distribution networks. The increasing penetration of PHEVs introduces new challenges in load management and system reliability due to additional demand and voltage fluctuations. To address these challenges, a coordinated charging and discharging strategy is proposed to optimally control PHEV interactions with the grid. A Monte Carlo Simulation (MCS) framework is employed to evaluate the stochastic behavior of PHEV charging patterns and their impact on system performance. The standard IEEE distribution test system is utilized, and reliability is assessed using indices such as Loss of Energy Expectation (LOEE). The proposed strategy enables controlled charging during off-peak periods and supports the grid through V2G during peak demand conditions. Simulation results demonstrate that uncontrolled charging significantly degrades system reliability, whereas the proposed managed charging combined with V2G improves reliability indices, reduces peak load, and mitigates voltage deviations. The findings highlight the effectiveness of coordinated PHEV integration as a promising solution for enhancing the reliability and operational performance of smart distribution systems.   

Keywords:

Managed charging, Plug-in hybrid electric vehicles, Vehicle-to-Grid, Smart distribution network, Reliability indices

References

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Published

2025-07-12

Issue

Vol. 2 No. 3 (2025): Intelligence Modeling in Electromechanical Systems

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How to Cite

Faraj Fahimi, M. (2025). A Coordinated Charging Strategy for Plug-in Hybrid Electric Vehicles with V2G to Improve Reliability Indices in Smart Distribution Systems. Intelligence Modeling in Electromechanical Systems, 2(3), 157-168. https://doi.org/10.48314/imes.vi.42

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