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<p>Electrifying the transportation industry may play a major role in the transition of smart cities. With more EVs on the road, it is especially desirable to implement a well-planned and effective charging infrastructure. Comparing level 3 chargers to level 1 and level 2 charging stations, the former are far faster in charging EVs. However, level 3 chargers can break critical system standards and have high-power consumption; thus, their widespread use is not economically or technically justified. This manuscript describes the best possible combination of all three EV charger options to effectively manage the EV demand while lowering distribution transformer loads, installation costs, and losses. The consequences of PV (photovoltaic system) generation are also considered in the analysis. Due to the unpredictability of vehicle users, EV load is considered using a stochastic approach. In addition, an intelligent search technique that combines the Mexican Axolotl optimization is improved by utilizing the Dingo optimization algorithm, which is named the improved Mexican Axolotl optimization. The IBESO is used to solve the constrained nonlinear stochastic problem. Finally, the proposed IBESO methodology’s performance is analyzed using MATLAB and provides thorough comparisons with various existing methods.</p>

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Hybrid IMAO–IBESO optimization approach for the best location of EV charging stations in active distribution networks

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • G. Elumalai,
  • M. Lakshmanan,
  • B. R. Tapas Bapu,
  • K. Srilakshmi

摘要

Electrifying the transportation industry may play a major role in the transition of smart cities. With more EVs on the road, it is especially desirable to implement a well-planned and effective charging infrastructure. Comparing level 3 chargers to level 1 and level 2 charging stations, the former are far faster in charging EVs. However, level 3 chargers can break critical system standards and have high-power consumption; thus, their widespread use is not economically or technically justified. This manuscript describes the best possible combination of all three EV charger options to effectively manage the EV demand while lowering distribution transformer loads, installation costs, and losses. The consequences of PV (photovoltaic system) generation are also considered in the analysis. Due to the unpredictability of vehicle users, EV load is considered using a stochastic approach. In addition, an intelligent search technique that combines the Mexican Axolotl optimization is improved by utilizing the Dingo optimization algorithm, which is named the improved Mexican Axolotl optimization. The IBESO is used to solve the constrained nonlinear stochastic problem. Finally, the proposed IBESO methodology’s performance is analyzed using MATLAB and provides thorough comparisons with various existing methods.