Optimal Infrastructure Planning for EVs Fast Charging Stations based on Prediction of User Behavior

Marjan Gjelaj*, Seyedmostafa Hashemi Toghroljerdi, Peter Bach Andersen, Chresten Træholt

*Corresponding author for this work

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Abstract

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.
Original languageEnglish
JournalI E T Electrical Systems in Transportation
Number of pages12
ISSN2042-9738
Publication statusAccepted/In press - 2019

Cite this

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title = "Optimal Infrastructure Planning for EVs Fast Charging Stations based on Prediction of User Behavior",
abstract = "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.",
author = "Marjan Gjelaj and Toghroljerdi, {Seyedmostafa Hashemi} and Andersen, {Peter Bach} and Chresten Tr{\ae}holt",
year = "2019",
language = "English",
journal = "I E T Electrical Systems in Transportation",
issn = "2042-9738",
publisher = "Institution of Engineering and Technology",

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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.

M3 - Journal article

JO - I E T Electrical Systems in Transportation

JF - I E T Electrical Systems in Transportation

SN - 2042-9738

ER -