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Received July 18, 2014
Accepted December 26, 2014
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도로운송부문용 에너지 공급 시스템 설계 및 경제성평가

Scenario-based Design and Life Cycle Cost Analysis of Energy Supply System for Transportation Sector

인천대학교 에너지화학공학과, 406-772 인천광역시 연수구 아카데미로 119
Department of Energy & Chemical Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 406-772, Korea
Korean Chemical Engineering Research, April 2015, 53(2), 164-173(10), 10.9713/kcer.2015.53.2.164 Epub 30 March 2015
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Abstract

본 연구에서는 다양한 도로운송부문용 에너지 공급 시스템을 구축하고 각 시나리오의 최적 비용을 비교분석하였다. 에너지 공급 시스템의 구성요소로써 기존의 정유공정, 부생수소 시스템, 신재생 에너지 자원 기반의 전력 생산공정, 전력운송을 위한 전력망을 설정하였으며, 내연기관자동차, 전기자동차, 연료전지자동차 등 세 가지의 도로운송부문용 자동차를 포함하였다. 이러한 구성요소를 포함한 다양한 에너지 공급 시스템 시나리오를 기반으로 최적 생애주기비용을 규명할 수 있는 에너지 시스템 평가모델을 개발하였다. 본 연구에서 개발한 최적화 모델을 제주도 지역에 적용함으로써 모델의 성능을 검증하였고 또한 제주도 지역의 에너지 시스템 구축에 관한 다양한 시나리오의 경제성을 분석하였다. 제주도 도로운송부문용 에너지 공급 시스템의 생애주기비용 분석 결과, 전력망을 이용하여 전기를 공급하는 전기자동차 시나리오가 상대적으로 가장 높은 경제성을 보였으며, 신재생 에너지 자원을 이용하여 수소를 공_x000D_ 급하는 연료전지자동차 시나리오가 가장 낮은 경제성을 보였다. 또한 연료비용, 차량비용, 인프라비용, 유지비용 등 주요 비용 관련 변수들에 관한 민감도분석을 수행함으로써 생애주기비용의 변화에 주요한 구성요소들을 규명하였다.
This study aims to design energy supply systems from various energy sources for transportation sectors and comparatively analyze the life cycle cost of different scenario-based systems. For components of the proposed energy supply system, we consider a typical oil refinery, byproduct hydrogen system, renewable energy source (RES)-based electric generation system and existing electricity grid. We also include three types of vehicles in transportation sector such as internal combustion engine vehicle (ICEV), electric vehicle (EV), fuel cell vehicle (FCV). We then develop various energy supply scenarios which consist of such components and evaluate the economic performance of different systems from the viewpoint of life cycle cost. Finally we illustrate the applicability of the proposed framework by conducting the design problem of energy supply systems of Jeju, Korea. As the results of life cycle cost analysis, EV fueled by electricity_x000D_ from grid is the most economically feasible. In addition, we identify key parameters to contribute the total life cycle cost such as fuel cost, vehicle cost, infra cost and maintenance cost using sensitivity analysis.

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