Open Access

OPTIMIZING ELECTRIC VEHICLE CHARGING INFRASTRUCTURE: A MULTI-OBJECTIVE GENETIC ALGORITHM APPROACH FOR SITING AND SIZING

https://doi.org/10.55640/ijnget-v02i03-01

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Dr. Javad Ahmadi ¹ , Dr. Yingjie Zhao ¹ ,

⁴ School of Electrical and Computer Engineering, University of Tehran, Iran

⁴ Department of Automation, Tsinghua University, China

Abstract

The rapid growth of electric vehicles (EVs) necessitates the strategic development of efficient charging infrastructure. This study proposes a multi-objective genetic algorithm (MOGA) approach for optimizing the siting and sizing of EV charging stations. The model incorporates multiple conflicting objectives, including cost minimization, user accessibility, grid stability, and environmental impact. By simulating various urban deployment scenarios, the algorithm identifies optimal solutions that balance these objectives, offering robust and scalable planning strategies. Results from case studies demonstrate that the MOGA-based framework significantly improves the efficiency and sustainability of EV charging infrastructure planning. The approach provides actionable insights for policymakers, urban planners, and utility companies aiming to support EV adoption and smart city initiatives.

Keywords

Electric vehicle charging, charging infrastructure, multi-objective optimization, genetic algorithm

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International Journal of Next-Generation Engineering and Technology

OPTIMIZING ELECTRIC VEHICLE CHARGING INFRASTRUCTURE: A MULTI-OBJECTIVE GENETIC ALGORITHM APPROACH FOR SITING AND SIZING

Abstract

Keywords

References

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