수소 충전소 최적 위치 선정을 위한 기계 학습 기반 방법론

김수환; 류준형; Soo Hwan Kim; Jun-Hyung Ryu

Korean Chemical Engineering Research, Vol.58, No.4, 573-580, August, 2020

DOI10.9713/kcer.2020.58.4.573 Export Citation

수소 충전소 최적 위치 선정을 위한 기계 학습 기반 방법론

A Machine Learning based Methodology for Selecting Optimal Location of Hydrogen Refueling Stations

김수환, 류준형^†

동국대학교 에너지공학과, 38066 경상북도 경주시 동대로 123

Soo Hwan Kim, and Jun-Hyung Ryu^†

Division of Creative Convergence Engineering, Dongguk University, Gyeongju Campus, 123, Dongdae-ro, Gyeongju-si, Gyeongsangbuk-do, 38066, Korea

E-mail:

초록

최근 석유를 대체할 수송 에너지원으로 수소에 대한 관심이 커지고 있다. 수소의 장점을 극대화하기 위해서는 수소 충전소가 많이 보급되어야 한다. 본 논문은 수소 충전소를 보다 가깝게 이용 할 수 있는 최적 위치 선정 방법론을 제안하였다. 기존 에너지의 공급처인 주유소와 천연가스 충전소의 위치를 우선 참고하고, 인구, 등록 차량 수 등의 데이터를 추가 반영하여 수소자동차의 예상 충전 수요를 계산하였다. 기계 학습(machine learning) 기법 중 하나인 k-중심자 군집화(k-medoids Clustering)를 이용하여 예상 수요에 대응하는 최적 수소 충전소 위치를 계산하였다. 제안된 방법의 우수성은 서울의 사례를 통해 수치적으로 설명하였다. 본 방법론과 같은 데이터 기반 방법은 향후 수소의 보급 속도를 높여 환경친화적인 경제 체계를 구축하는데 기여할 수 있을 것이다.

Hydrogen emerged as a sustainable transport energy source. To increase hydrogen utilization, hydrogen refueling stations must be available in many places. However, this requires large-scale financial investment. This paper proposed a methodology for selecting the optimal location to maximize the use of hydrogen charging stations. The location of gas stations and natural gas charging stations, which are competing energy sources, was first considered, and the expected charging demand of hydrogen cars was calculated by further reflecting data such as population, number of registered vehicles, etc. Using k-medoids clustering, one of the machine learning techniques, the optimal location of hydrogen charging stations to meet demand was calculated. The applicability of the proposed method was illustrated in a numerical case of Seoul. Data-based methods, such as this methodology, could contribute to constructing efficient hydrogen economic systems by increasing the speed of hydrogen distribution in the future.

Keywords:Hydrogen refueling station;Machine learning;Location selecting;k-medoids clustering

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[1] Sharaf Z, Renew. Sust. Energ. Rev., 32, 810 (2014)

[2] Kim YP, J. Korean Soc. Atmos., 22(5), 535 (2006)

[3] Patnaik P, Wiley-Interscience, 402(2007).

[4] Pandian MS, 2010 Conference Proceedings IPEC, Singapore, 875-880(2010).

[5] H2Korea, “A Study on the Establishment of Hydrogen Fuel Price and Supply Management for Transportation,” MTI(2017).

[6] EM Solution Co., LTD. , “Review of Hydrogen Station Construction Regulations,” (2018).

[7] Garfinkel RS, Nemhauser GL, “Integer Programming,” Wiley(1972).

[8] Church R, Rsai, 32(1), 101 (1974)

[9] Kang JE, Recker W, Transp. Sci., 49(4) (2014)

[10] Hakimi SL, Oper. Res., 12(3), 450 (1964)

[11] Nicholas MA, Handy SL, Sperling D, Transp Res Rec, 1880(1), 126 (2004)

[12] Church R, Velle CR, Pap. Reg. Sci., 32, 101 (1974)

[13] Frade, et al., Transp Res Rec., 158, 6582 (2011)

[14] Hodgson, Geogr. Anal., 22(3), 270-279(1990).

[15] Hodgson et al., Eur. J. Oper. Res., 90(30), 427-443(1996).

[16] Lim K, Socio-Econ Plan Sci., 39(2), 125 (2005)

[17] Kuby M, Lines L, Schultz R, Xie ZX, Kim JG, Lim S, Int. J. Hydrog. Energy, 34(15), 6045 (2009)

[18] Melendez M, “A. Milbrandt, Geographically-based Hydrogen Demand and Infrastructure Deployment Scenario,” Nrel(2008).

[19] Steinhaus H, “Sur la Division Des Corp Materiels en Parties,” Bull. Acad. Polon. Sci. IV (C1.III), 801-804(1956).

[20] Kaufmann L, Rousseeuw P, Data Analysis based on the L1-Norm and Related Methods. 405-416(1987).

[21] Margaret HD, “Data Mining, Introductory and Advanced Topics,” Prentice Hall(2002).

[22] MTI, “Roadmap to the Revitalization of the Hydrogen Economy,” (2019).

[23] Park JW, Hun YS, Trans. Korean Hydrog. New Energy Soc., 28(6), 642 (2017)

[24] Transportation Safety Authority, “Driving Distance Statistics,” 2014-2018(2019).