주행거리 기반 충전 수요를 고려한 전기자동차 완속 충전기 최적 공급량 산출
Optimal Supply Calculation of Electric Vehicle Slow Chargers Considering Charging Demand Based on Driving Distance
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초록
교통부문 탄소중립을 위한 전기자동차로의 전환에 있어 충분한 충전 인프라의 구축은 중요한 선행요소이다. 특히, 거주지의 충전 인프라 구축은 필수적이다. 우리나라의 주거형태는 주로 공동주택이며, 다수의 거주민을 위한 공공 충전기가 공급되어야 한다. 정부는 충전시설과전기자동차 전용주차구역의 확보를 법적으로 규정하고 있으나, 주차면수만을 산출근거로 한다. 완속 충전기는 3.5kW 과금형 콘센트와 7kW 완속 충전기가 주를 이룬다. 전자가 충전기설치 및 이용에 유리하지만, 충전속도가 느려 두 가지 형태의 충전기는 양립이 필요하다. 본연구에서는 일일 주행거리를 기반으로 산정한 전기자동차의 충전 수요에 대응할 수 있는 충전기를 할당하는 최적화 모형을 제시하였다. 또한, 메타 휴리스틱 알고리즘인 Tabu Search를 사용하여 최적화 모형을 만족하는 것과 동시에 충전기 공급 및 충전 비용을 최소화할 수 있는완속 충전기 공급량을 산정하였다. 사례 분석을 위해 개인통행실태조사자료를 사용해 주행거리를 산정하였으며, 가상의 충전 시나리오 및 환경을 설정하여 100대의 전기자동차 충전 수요에 대응하는 22대의 3.5kW 과금형 콘센트를 최적 공급량으로 산정하였다.
Abstract
The transition to electric vehicles is a crucial step toward achieving carbon neutrality in the transportation sector. Adequate charging infrastructure at residential locations is essential. In South Korea, the predominant form of housing is multifamily dwellings, necessitating the provision of public charging stations for numerous residents. Although the government mandates the availability of charging facilities and designated parking areas for electric vehicles, it bases the supply of charging stations solely on the number of parking spaces. Slow chargers, mainly 3.5kW charging outlets and 7kW slow chargers, are commonly used. While the former is advantageous for installation and use, its slower charging speed necessitates the coexistence of both types of chargers. This study presents an optimization model that allocates chargers capable of meeting charging demands based on daily driving distances. Furthermore, using the metaheuristic algorithm Tabu Search, this model satisfies the optimization requirements and minimizes the costs associated with charger supply and usage. To conduct a case study, data from personal travel surveys were used to estimate the driving distances, and a hypothetical charging scenario and environment were set up to determine the optimal supply of 22 units of 3.5kW charging outlets for the charging demands of 100 BEVs.
참고문헌 (31)
- Abido, M. A., "Optimal power flow using tabu search algorithm", Electric Power Components and Systems, 30(5), pp.469~483 (2002)
- Andwari, A. M., "A review of battery electric vehicle technology and readiness levels", Renewable and Sustainable Energy Reviews, 78, pp.414~430 (2017)
- Baresch, M., "Allocation of e-car charging : Assessing the utilization of charging infrastructures by location", Transportation Research Part A : Policy and Practice, 124, pp.388~395 (2019)
- Bjerkan, K. Y., "Incentives for promoting battery electric vehicle(BEV)adoption in Norway", Transportation Research Part D : Transport and Environment, 43, pp.169~180 (2016)
- Bonges III, H. A., "Addressing electric vehicle(EV)sales and range anxiety through parking layout, policy and regulation", Transportation Research Part A : Policy and Practice, 83, pp.63~73 (2016)
- Cui, D., "Battery electric vehicle usage pattern analysis driven by massive real-world data", Energy, 250, pp.123837~ (2022)
- Learning from Norwegian battery electric and plug-in hybrid vehicle users, "Figenbaum, E.", TØI Transportøkonomisk institutt, ~ (2016)
- Gerossier, A., "Modeling and forecasting electric vehicle consumption profiles", Energies, 12(7), pp.1341~ (2019)
- Glover, F., "Future paths for integer programming and links to artificial intelligence", Computers & Operations Research, 13(5), pp.533~549 (1986)
- Gu, S., "A study on installation of optimal number of chargers for electric vehicles apartment buildings based on household supply types", pp.2023
- Hoke, A., "Accounting for lithium-ion battery degradation in electric vehicle charging optimization", IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(3), pp.691~700 (2014)
- Global EV outlook 2023, Catching up with climate ambitions, "International Energy Agency", 107~113 (2023)
- Jin, C., "Optimizing electric vehicle charging with energy storage in the electricity market", IEEE Transactions on Smart Grid, 4(1), pp.311~320 (2013)
- Jin, C., "Optimizing electric vehicle charging : A customer's perspective", IEEE Transactions on Vehicular Technology, 62(7), pp.2919~2927 (2013)
- Liu, Z., "Optimal day-ahead charging scheduling of electric vehicles through an aggregative game model", IEEE Transactions on Smart Grid, 9(5), pp.5173~5184 (2017)
- Michel, L., "A simple tabu search for warehouse location", European Journal of Operational Research, 157(3), pp.576~591 (2004)
- Mies, J. J., "Estimating the charging profile of individual charge sessions of Electric Vehicles in The Netherlands", World Electric Vehicle Journal, 9(2), pp.17~ (2018)
- Strategy to Expand Electric Vehicle Charging Infrastructure and Enhance Safety, "Ministry of Environment", 1~6 (2023)
- "Ministry of Environment" (2024)[URL]
- Neubauer, J., "The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility", Journal of Power Sources, 257, pp.12~20 (2014)
- Omar, N., "Lithium iron phosphate based battery–Assessment of the aging parameters and development of cycle life model", Applied Energy, 113, pp.1575~1585 (2014)
- Park, J., "Charging pattern of electric vehicle user and affecting factors : latent class analysis approach", The Transactions of the Korean Institute of Electric Engineers, 71(11), pp.1639~1645 (2022)
- Quirós-Tortós, J., "A statistical analysis of EV charging behavior in the UK", 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), pp.445~449 (2015)
- Saber, A. Y., "Optimization of vehicle-to-grid scheduling in constrained parking lots", 2009 IEEE Power & Energy Society General Meeting, pp.1~8 (2009)
- Song, H., "Evaluation of charging mileage of electric vehicle using battery module", Transactions of the Korean Society of Automotive Engineers, 27(8), pp.645~652 (2019)
- Statistics Korea, "Changes in Population", Household Structure and Residential Characteristics (1985~2020), 2023, pp.83~92
- Automobile Use Survey, "The Korea Transport Institute", 3~5 (2013)
- Xu, B., "Modeling of lithium-ion battery degradation for cell life assessment", IEEE Transactions on Smart Grid, 9(2), pp.1131~1140 (2016)
- Xu, Y., "Optimization based on tabu search algorithm for optimal sizing of hybrid PV", energy storage system : Effects of tabu search parameters, 2022, pp.53, 102662~
- Yoo, J., "Analysis of Electric Vehicle Use and Charging Behavior and Policy Implications : Powered by Real Time EV User Data", Journal of Korean Society of Transportation, 41(6), pp.704~723 (2023)
- Ziegler, D. U., "Methodology for integrating flexibility into realistic large-scale distribution network planning using Tabu search", International Journal of Electrical Power & Energy Systems, 152 (2023)