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

Dandan Hu (), Xiongkai Li, Chen Liu and Zhi-Wei Liu
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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

Sustainability, 2024, vol. 16, issue 3, 1-17

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.

Keywords: electric vehicle; location; sizing; quantum genetic algorithm; simulated annealing algorithm; carbon emission (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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