Prediction of electric vehicle charging-power demand in realistic urban traffic networks
- Authors
- Arias, Mariz B.; Kim, Myungchin; Bae, Sungwoo
- Issue Date
- Jun-2017
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Electric vehicle charging-power demand; Markov-chain traffic model; Charging patterns; Real-time closed-circuit television data; Urban area
- Citation
- APPLIED ENERGY, v.195, pp.738 - 753
- Indexed
- SCIE
SCOPUS
- Journal Title
- APPLIED ENERGY
- Volume
- 195
- Start Page
- 738
- End Page
- 753
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152242
- DOI
- 10.1016/j.apenergy.2017.02.021
- ISSN
- 0306-2619
- 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.
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