Optimal Infrastructure Planning of EVs Fast Charging Stations based on Prediction of Users’ Behaviour

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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Electric Vehicles (EVs) appear to offer a promising solution to support sustainable transportation and reduction of CO2 emissions in the metropolitan areas. To satisfy the EV load demand of the new EV models with larger battery capacities, public DC Fast-Charging Stations (DCFCSs) are essential to recharge EVs rapidly. A stochastic planning method of the DCFCSs is presented considering users’ behaviour and the probabilistic driving patterns in order to predict EVs charging demand. According to the stochastic method, a coordinated charging demand and battery energy storage (BES) charging demand are proposed with the objective of minimising EVs peak load and the charging infrastructure costs. The proposed planning method shows the ability to avoid additional grid reinforcement costs caused by the EVs demand during the peak hours. In the coordinated charging demand, the EVs peak load is managed by using DCFCSs controllability modes. Instead, in the BES charging demand, an optimal BES is proposed as an alternative solution to reduce DCFCSs operational costs as well as EVs peak demand. Finally, an economic analysis is carried out to evaluate the technical and economic issues of DCFCSs, the BES life-cycle 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 pages13
ISSN2042-9738
Publication statusAccepted/In press - 2019
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