An Improved NSGA-II Based Multi-Objective Optimization Model for Electric Vehicle Charging Station Selection

Facing the challenge of balancing electric vehicle (EV) user experience with distribution network security, this paper develops a multi-objective optimization model for charging station selection that simultaneously considers user-side costs and grid-side stability indicators, including voltage deviation and system power loss. To solve this complex problem, an improved NSGA-II algorithm with enhanced constraint handling is introduced. Case studies on the IEEE 33-bus system demonstrate that the proposed approach effectively limits maximum voltage deviation to 2% with only a minimal 3% increase in user cost and reduces network losses by 12%. This achieves an optimal balance between user satisfaction and grid security, providing quantitative support for coordinated charging management and infrastructure planning.

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Publication Details

Journal
Mathematics
Published
2025-12-01
DOI
https://doi.org/10.3390/math13233855
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.73
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article

An Improved NSGA-II Based Multi-Objective Optimization Model for Electric Vehicle Charging Station Selection

Jingxuan Li, Chengbin Liang, Zehao Li, Haiping Tang et al.
1 citations
Mathematics
Electric Vehicles and Infrastructure
0.73
article

An Improved NSGA-II Based Multi-Objective Optimization Model for Electric Vehicle Charging Station Selection

Jingxuan Li, Chengbin Liang, Zehao Li, Haiping Tang, Pengcheng Li
article en
1 citations

Abstract

Facing the challenge of balancing electric vehicle (EV) user experience with distribution network security, this paper develops a multi-objective optimization model for charging station selection that simultaneously considers user-side costs and grid-side stability indicators, including voltage deviation and system power loss. To solve this complex problem, an improved NSGA-II algorithm with enhanced constraint handling is introduced. Case studies on the IEEE 33-bus system demonstrate that the proposed approach effectively limits maximum voltage deviation to 2% with only a minimal 3% increase in user cost and reduces network losses by 12%. This achieves an optimal balance between user satisfaction and grid security, providing quantitative support for coordinated charging management and infrastructure planning.

MathematicsVol. 13(23)
Guizhou University (CN), Guizhou Electric Power Design and Research Institute (CN)
Openalex Percentile: Top 21%
Electric Vehicles and Infrastructure
0.73
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An Improved NSGA-II Based Multi-Objective Optimization Model for Electric Vehicle Charging Station Selection — Jingxuan Li, Chengbin Liang, et al. · Mathematics (2025) | TGRS Research Map | TGRS