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Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm

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  • Zhou, Guangyou
  • Zhu, Zhiwei
  • Luo, Sumei

Abstract

Nowadays, the development of new energy science has gradually entered a good situation, and accordingly, electric vehicles have been recognized by people all over the world and rapidly popularized in various countries. However, the rapid growth of electric vehicles has caused a series of problems, such as insufficient number of charging stations, uneven distribution, and high cost, which are becoming increasingly serious. In order to solve the above problems, this paper takes Ireland as an example for research. Based on the collection of relevant information, this paper first tries to build a social total cost model, and calculates the total operating cost of charging stations under various distribution conditions. In this model, the total social cost is subdivided into comprehensive economic cost and environmental cost. Economic costs include construction costs and fees, while environmental costs include electricity consumption and carbon dioxide emissions. Secondly, this paper established a charging station location optimization model based on genetic algorithm, which simplified the Irish territory into a rectangle with a length of 350 km and a width of 200 km, subdivided it into small squares, and adjusted the correlation coefficient to meet the charging demand. This solution solves the minimization problem of operating cost under the constraints of depreciation period of charging station, power consumption per unit distance of charging station and vehicle charging probability. Finally, in order to determine the factors that may have a significant impact on the total cost of charging stations, we conducted sensitivity analysis and found that the total cost is very sensitive to the number of charging stations and the possibility of EV charging per day.

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  • Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003401
    DOI: 10.1016/j.energy.2022.123437
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    2. Clairand, Jean-Michel & González-Rodríguez, Mario & Kumar, Rajesh & Vyas, Shashank & Escrivá-Escrivá, Guillermo, 2022. "Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements," Energy, Elsevier, vol. 256(C).
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    5. Loni, Abdolah & Asadi, Somayeh, 2023. "Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco," Energy, Elsevier, vol. 282(C).
    6. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    7. Park, Junseok & Moon, Ilkyeong, 2023. "A facility location problem in a mixed duopoly on networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    8. 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.
    9. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    10. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    11. Hui Zhao & Jing Gao & Xian Cheng, 2023. "Electric Vehicle Solar Charging Station Siting Study Based on GIS and Multi-Criteria Decision-Making: A Case Study of China," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    12. Di Xu & Wenhui Pei & Qi Zhang, 2022. "Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience," Energies, MDPI, vol. 15(14), pages 1-16, July.
    13. Deveci, Muhammet & Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Cali, Umit, 2023. "A rough Dombi Bonferroni based approach for public charging station type selection," Applied Energy, Elsevier, vol. 345(C).
    14. Panagiotis Skaloumpakas & Evangelos Spiliotis & Elissaios Sarmas & Alexios Lekidis & George Stravodimos & Dimitris Sarigiannis & Ioanna Makarouni & Vangelis Marinakis & John Psarras, 2022. "A Multi-Criteria Approach for Optimizing the Placement of Electric Vehicle Charging Stations in Highways," Energies, MDPI, vol. 15(24), pages 1-13, December.
    15. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    16. Hamza El Hafdaoui & Hamza El Alaoui & Salma Mahidat & Zakaria El Harmouzi & Ahmed Khallaayoun, 2023. "Impact of Hot Arid Climate on Optimal Placement of Electric Vehicle Charging Stations," Energies, MDPI, vol. 16(2), pages 1-19, January.
    17. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).

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