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• W2600172076 abstract "Abstract 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." @default.
• W2600172076 created "2017-04-07" @default.
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• W2600172076 date "2017-06-01" @default.
• W2600172076 modified "2023-10-17" @default.
• W2600172076 title "Prediction of electric vehicle charging-power demand in realistic urban traffic networks" @default.
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• W2600172076 doi "https://doi.org/10.1016/j.apenergy.2017.02.021" @default.
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