Abstract
| Original language | English |
|---|---|
| Journal | I E T Electrical Systems in Transportation |
| Number of pages | 12 |
| ISSN | 2042-9738 |
| DOIs | |
| Publication status | Accepted/In press - 2019 |
Cite this
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Optimal Infrastructure Planning for EVs Fast Charging Stations based on Prediction of User Behavior. / Gjelaj, Marjan; Toghroljerdi, Seyedmostafa Hashemi; Andersen, Peter Bach; Træholt, Chresten.
In: I E T Electrical Systems in Transportation, 2019.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Optimal Infrastructure Planning for EVs Fast Charging Stations based on Prediction of User Behavior
AU - Gjelaj, Marjan
AU - Toghroljerdi, Seyedmostafa Hashemi
AU - Andersen, Peter Bach
AU - Træholt, Chresten
PY - 2019
Y1 - 2019
N2 - Electric vehicles (EVs) appear to offer a promising solution to support sustainable transportation and the reduction of CO2 emissions in the metropolitan areas. To satisfy the EV load demand of the new EV models with larger battery capacities, public direct-current fast-charging stations (DCFCSs) are essential to recharge EVs rapidly. A stochastic planning method of the DCFCSs is presented considering user behaviour and the probabilistic driving patterns in order to predict EVs charging demand. According to the stochastic method, a coordinated charging demand and storage charging demand are proposed with the objective of minimising peak load from EVs and charging-infrastructure costs. The proposed planning method can prevent additional grid reinforcement costs due to EV demand during the peak hours. In the coordinated charging demand, the peak load from EVs is managed by controlling the DCFCSs. Instead, in the battery energy storage (BES) charging demand, an optimal BES is proposed as an alternative solution to reduce the peak demand of EVs as well as DCFCSs operational costs. Finally, an economic analysis is carried out to evaluate the technical and economic aspects related to DCFCSs, the BES lifecycle costs as well as the financial performance of BES costs versus grid reinforcement costs.
AB - Electric vehicles (EVs) appear to offer a promising solution to support sustainable transportation and the reduction of CO2 emissions in the metropolitan areas. To satisfy the EV load demand of the new EV models with larger battery capacities, public direct-current fast-charging stations (DCFCSs) are essential to recharge EVs rapidly. A stochastic planning method of the DCFCSs is presented considering user behaviour and the probabilistic driving patterns in order to predict EVs charging demand. According to the stochastic method, a coordinated charging demand and storage charging demand are proposed with the objective of minimising peak load from EVs and charging-infrastructure costs. The proposed planning method can prevent additional grid reinforcement costs due to EV demand during the peak hours. In the coordinated charging demand, the peak load from EVs is managed by controlling the DCFCSs. Instead, in the battery energy storage (BES) charging demand, an optimal BES is proposed as an alternative solution to reduce the peak demand of EVs as well as DCFCSs operational costs. Finally, an economic analysis is carried out to evaluate the technical and economic aspects related to DCFCSs, the BES lifecycle costs as well as the financial performance of BES costs versus grid reinforcement costs.
U2 - 10.1049/iet-est.2018.5080
DO - 10.1049/iet-est.2018.5080
M3 - Journal article
JO - I E T Electrical Systems in Transportation
JF - I E T Electrical Systems in Transportation
SN - 2042-9738
ER -