IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5781-d634965.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5781/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5781/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    2. Prof.as.dr.Sonela Stillo & PhD.Cand. Gentisa Furxhi & Doc.Dr. Marinela Teneqexhi, 2017. "Innovation in International Business," European Journal of Multidisciplinary Studies Articles, Revistia Research and Publishing, vol. 2, ejms_v2_i.
    3. Schreier, Alayna & Stenersen, Madeline R. & Strambler, Michael J. & Marshall, Tim & Bracey, Jeana & Kaufman, Joy S., 2023. "Needs of caregivers of youth enrolled in a statewide system of care: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 147(C).
    4. William Magee & Sébastien St-Arnaud, 2012. "Models of the Joint Structure of Domain-Related and Global Distress: Implications for the Reconciliation of Quality of Life and Mental Health Perspectives," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(1), pages 161-185, January.
    5. repec:jss:jstsof:06:i02 is not listed on IDEAS
    6. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    7. Moira Mckniff & Stephanie M. Simone & Tania Giovannetti, 2023. "Age, Loneliness, and Social Media Use in Adults during COVID-19: A Latent Profile Analysis," IJERPH, MDPI, vol. 20(11), pages 1-12, May.
    8. Deslauriers-Varin, Nadine & Beauregard, Eric, 2014. "Consistency in crime site selection: An investigation of crime sites used by serial sex offenders across crime series," Journal of Criminal Justice, Elsevier, vol. 42(2), pages 123-133.
    9. Jennifer M. Boldero & Richard C. Bell & Susan M. Moore, 2010. "Do gambling activity patterns predict gambling problems? A latent class analysis of gambling forms among Australian youth," International Gambling Studies, Taylor & Francis Journals, vol. 10(2), pages 151-163, August.
    10. Caili Liu & Yong Wei & Yu Ling & E. Scott Huebner & Yifang Zeng & Qin Yang, 2020. "Identifying Trajectories of Chinese High School Students’ Depressive Symptoms: an Application of Latent Growth Mixture Modeling," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 15(3), pages 775-789, July.
    11. Seamus Fleming & Mark Shevlin & Jamie Murphy & Stephen Joseph, 2014. "Psychosis within dimensional and categorical models of mental illness," Psychosis, Taylor & Francis Journals, vol. 6(1), pages 4-15, January.
    12. Hanna Lee & Jeong-Won Han, 2021. "Analysis of Parenting Attitude Types and Influencing Factors of Korean Parents by Using Latent Transition Analysis," IJERPH, MDPI, vol. 18(14), pages 1-13, July.
    13. Thao Thi Phuong Nguyen & Ha Ngoc Do & Thao Bich Thi Vu & Khanh Long Vu & Hiep Duy Nguyen & Dung Tuan Nguyen & Hoang Minh Do & Nga Thi Thu Nguyen & Ly Thi Bac La & Linh Phuong Doan & Tham Thi Nguyen & , 2023. "Association of Individual and Neighborhood Characteristics to Problematic Internet Use among Youths and Adolescents: Evidence from Vietnam," IJERPH, MDPI, vol. 20(3), pages 1-14, January.
    14. Ssu-Kuang Chen & Sunny Lin, 2014. "The Latent Profiles of Life Domain Importance and Satisfaction in a Quality of Life Scale," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 116(2), pages 429-445, April.
    15. Frauke Kreuter & Ting Yan & Roger Tourangeau, 2008. "Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 723-738, June.
    16. Luo, Li & Qiu, Yuxi, 2024. "Parent−child relationship and social competence in Chinese preschoolers: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 163(C).
    17. Cappelli, Christopher & Pike, James Russell & Riggs, Nathaniel R. & Warren, Christopher M. & Pentz, Mary Ann, 2019. "Executive function and probabilities of engaging in long-term sedentary and high calorie/low nutrition eating behaviors in early adolescence," Social Science & Medicine, Elsevier, vol. 237(C), pages 1-1.
    18. Xiangyu Luo & Rui Qiu, 2020. "Electric Vehicle Charging Station Location towards Sustainable Cities," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
    19. Terry Elrod & Gerald Häubl & Steven Tipps, 2012. "Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 358-387, April.
    20. Yu Ling & E. Scott Huebner & Hongmei Yuan & Zhihua Li & Wenli Liu, 2016. "Subtyping of Strengths and Difficulties in a Chinese Adolescent Sample: a Latent Class Analysis," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 9(4), pages 933-948, December.
    21. Hye-Gyeong Son & Kyu-Hyoung Jeong & Heeran J. Cho & Minuk Lee, 2021. "Changes in Types of Drinking Behavior in Korean Adults: Differences in Demographics, Depression, and Suicidal Thoughts," IJERPH, MDPI, vol. 18(14), pages 1-8, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5781-:d:634965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.