다기간 자료포락분석을 이용한 전기차 충전소 효율성 변화 분석

• 영문명: Analysis on the Efficiency Change in Electric Vehicle Charging Stations Using Multi-Period Data Envelopment Analysis
• 발행기관: 한국산업경영시스템학회
• 저자명: 손동훈(Dong-Hoon Son) 강영수(Yeong-Su Gang) 김화중(Hwa-Joong Kim)
• 간행물 정보: 『산업경영시스템학회지』제44권 제2호, 1~14쪽, 전체 14쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.06.30

영문 초록

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.

목차

1. 서 론

2. 문헌연구

3. 연구방법

4. 효율성 분석결과

5. 결 론

키워드

Electric Vehicle Charging Station Efficiency Multi-Period Data Envelopment Analysis