Abstract
This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.
| Original language | English |
|---|---|
| Pages (from-to) | 647-659 |
| Number of pages | 13 |
| Journal | Applied Energy |
| Volume | 178 |
| Early online date | 28 Jun 2016 |
| DOIs | |
| Publication status | Published - 15 Sep 2016 |
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Keywords
- Electric vehicle charging stations
- Load capability
- Load profile templates
- Planning
- Traffic flow
Cite this
Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates. / Xiang, Yue; Liu, Junyong; Li, Ran; Li, Furong; Gu, Chenghong; Tang, Shuoya.
In: Applied Energy, Vol. 178, 15.09.2016, p. 647-659.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates
AU - Xiang, Yue
AU - Liu, Junyong
AU - Li, Ran
AU - Li, Furong
AU - Gu, Chenghong
AU - Tang, Shuoya
PY - 2016/9/15
Y1 - 2016/9/15
N2 - This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.
AB - This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.
KW - Electric vehicle charging stations
KW - Load capability
KW - Load profile templates
KW - Planning
KW - Traffic flow
UR - http://www.scopus.com/inward/record.url?scp=84976351329&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1016/j.apenergy.2016.06.021
U2 - 10.1016/j.apenergy.2016.06.021
DO - 10.1016/j.apenergy.2016.06.021
M3 - Article
VL - 178
SP - 647
EP - 659
JO - Applied Energy
JF - Applied Energy
SN - 0306-2619
ER -