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Optimal Allocation for Electric Vehicle Charging Stations

Author

Listed:
  • Jiwon Lee

    (Department of Big Data Science, Halla University, Wonju 26404, Korea)

  • Midam An

    (Department of Big Data Science, Halla University, Wonju 26404, Korea)

  • Yongku Kim

    (Department of Statistics, Kyungpook National University, Daegu 41566, Korea)

  • Jung-In Seo

    (Department of Information Statistics, Andong National University, Andong 36729, Korea)

Abstract

Currently, more than 30 % of the fine dust generated in the Seoul metropolitan area is a pollutant emitted from automobiles such as diesel vehicles, and air pollution caused by this is becoming increasingly serious. In addition, the importance of electric vehicle distribution is increasing due to the strengthening of international environmental regulations on automobile exhaust gas and increasing the possibility of depletion of petroleum resources. This manuscript proposes a method for selecting an optimal electric vehicle charging station location in expanding charging facilities to activate electric vehicle distribution. For the sake of illustration, directions will be provided on how to select the best location for electric vehicle charging stations using data from Seoul, which has the best access. As the features, the number of living population and work force people and the number of guest facilities, which are determined to affect demand for quick charging, are considered. The missing values of the observed data are imputed based on the kriging technique from spatial correlation, and by segmenting the data through clustering, a representative technique of unsupervised learning, the characteristics of each cluster are examined and the characteristics of the clusters are identified. In addition, machine learning techniques such as the elastic net, random forest, support vector machine, and extreme gradient boosting are applied to examine the influence of the features used in predicting classes of data. In clustering analysis, the optimal number of clusters was determined to be 3 based on the heuristic and information-theoretic methods, and all the machine learning techniques considered showed that the number of work force population is the most important feature in predicting classes of data. All things considered from our results, it is reasonable to install quick electric vehicle charging stations in the places with the highest concentration of work force population and guest facility.

Suggested Citation

  • Jiwon Lee & Midam An & Yongku Kim & Jung-In Seo, 2021. "Optimal Allocation for Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(18), pages 1-10, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5781-:d:634965
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    References listed on IDEAS

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    1. Chung, Sung Hoon & Kwon, Changhyun, 2015. "Multi-period planning for electric car charging station locations: A case of Korean Expressways," European Journal of Operational Research, Elsevier, vol. 242(2), pages 677-687.
    2. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    3. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    4. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    5. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    6. Milad Akbari & Morris Brenna & Michela Longo, 2018. "Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
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    Cited by:

    1. Mansur Arief & Yan Akhra & Iwan Vanany, 2023. "A Robust and Efficient Optimization Model for Electric Vehicle Charging Stations in Developing Countries under Electricity Uncertainty," Papers 2307.05470, arXiv.org.
    2. Sagar Hossain & Md. Rokonuzzaman & Kazi Sajedur Rahman & A. K. M. Ahasan Habib & Wen-Shan Tan & Md Mahmud & Shahariar Chowdhury & Sittiporn Channumsin, 2023. "Grid-Vehicle-Grid (G2V2G) Efficient Power Transmission: An Overview of Concept, Operations, Benefits, Concerns, and Future Challenges," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    3. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    4. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    5. Manzolli, Jônatas Augusto & Trovão, João Pedro F. & Henggeler Antunes, Carlos, 2022. "Electric bus coordinated charging strategy considering V2G and battery degradation," Energy, Elsevier, vol. 254(PA).
    6. Ahmadi Jirdehi, Mehdi & Sohrabi Tabar, Vahid, 2023. "Risk-aware energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources high penetration, crypto-currency miners and responsive loads," Energy, Elsevier, vol. 263(PA).

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