Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Analysis on the Efficiency Change in Electric Vehicle Charging Stations Using Multi-Period Data Envelopment Analysis

다기간 자료포락분석을 이용한 전기차 충전소 효율성 변화 분석

  • Son, Dong-Hoon (Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology) ;
  • Gang, Yeong-Su (Asia Pacific School of Logistics, Inha University) ;
  • Kim, Hwa-Joong (Asia Pacific School of Logistics, Inha University)
  • 손동훈 (홍콩과학기술대학교 토목환경공학과) ;
  • 강영수 (인하대학교 아태물류학부) ;
  • 김화중 (인하대학교 아태물류학부)
  • Received : 2021.01.07
  • Accepted : 2021.04.27
  • Published : 2021.06.30
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Abstract

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology(No. NRF-2020R1F1A1075866).

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