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Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm

Author

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  • Dandan Hu

    (School of Management, South-Central Minzu University, Wuhan 430074, China)

  • Xiongkai Li

    (School of Management, South-Central Minzu University, Wuhan 430074, China)

  • Chen Liu

    (School of Engineering, RMIT University, Melbourne, VIC 3001, Australia)

  • Zhi-Wei Liu

    (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

China’s pursuit of carbon peak and carbon neutrality relies heavily on the widespread adoption of electric vehicles (EVs), necessitating the optimal location and sizing of charging stations (CSs). This study proposes a model for minimizing the overall social cost by considering CS construction and operation costs, EV user charging time costs, and associated carbon emissions costs. An improved quantum genetic algorithm, integrating a dynamic rotation angle and simulated annealing elements, addresses the optimization problem. Performance evaluation employs test functions and a case study using electric taxi trajectory data from Shenzhen. Findings reveal that higher charging power does not always yield better outcomes; appropriate power selection effectively reduces costs. Increasing the number of CSs beyond a threshold fails to significantly reduce carbon emission costs but enhances demand coverage.

Suggested Citation

  • Dandan Hu & Xiongkai Li & Chen Liu & Zhi-Wei Liu, 2024. "Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm," Sustainability, MDPI, vol. 16(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1158-:d:1329485
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    References listed on IDEAS

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