期刊
APPLIED ENERGY
卷 195, 期 -, 页码 738-753出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2017.02.021
关键词
Electric vehicle charging-power demand; Markov-chain traffic model; Charging patterns; Real-time closed-circuit television data; Urban area
资金
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea [20161210200560]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20161210200560] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents a time-spatial electric vehicle (EV) charging-power demand forecast model at fast charging stations located in urban areas. Most previous studies have considered private charging locations and a fixed charging-start time to predict the EV charging-power demand. Few studies have considered predicting the EV charging-power demand in urban areas with time-spatial model analyses. The approaches used in previous studies also may not be applicable to predicting the EV charging-power demand in urban areas because of the complicated urban road network. To possibly forecast the actual EV charging-power demand in an urban area, real-time closed-circuit television (CCTV) data from an actual urban road network are considered. In this study, a road network inside the metropolitan area of Seoul, South Korea was used to formulate the EV charging-power demand model using two steps. First, the arrival rate of EVs at the charging stations located near road segments of the urban road network is determined by a Markov-chain traffic model and a teleportation approach. Then, the EV charging power demand at the public fast-charging stations is determined using the information from the first step. Numerical examples for the EV charging-power demand during three time ranges (i.e., morning, afternoon, and evening) are presented to predict the charging-power demand profiles at the public fast-charging stations in urban areas. The proposed time-spatial model can also contribute to investment and operation plans for adaptive EV charging infrastructures with renewable resources and energy storage depending on the EV charging-power demand in urban areas. (C) 2017 Elsevier Ltd. All rights reserved.
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