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AuthorMehrjerdi, Hasan
AuthorHemmati, Reza
Available date2022-11-14T10:49:13Z
Publication Date2020
Publication NameSustainable Energy Technologies and Assessments
ResourceScopus
Resource2-s2.0-85074714054
URIhttp://dx.doi.org/10.1016/j.seta.2019.100577
URIhttp://hdl.handle.net/10576/36369
AbstractThis paper designs optimal charging facility and capacity for electric vehicle charging station. The charging facility is modeled containing fast, intermediate, and slow speed chargers. The nominal powers of these chargers are determined. The charging station is linked to the utility grid and it is supplied by wind energy and the energy storage devices. The optimal sizing and operation of storage system are optimized. The electrical grid is strengthened by line reinforcement. The uncertainty of wind power is included and dealt by stochastic programming. The model is expressed as stochastic mixed integer linear programming and solved by GAMS toolbox. The results demonstrate that the rated powers of quick, intermediate, and slow speed chargers are optimized on 116, 84, and 52 kW, respectively. The power of quick charger is 27% more than the intermediate one and the intermediate charger needs about 38% larger power facility compared to the slow speed system. The storage system is designed with rated power equal to 133 kW and it can discharge 85% of its energy during one hour. The lines are reinforced by 183% to supply the energy demand of the charging station. The energy, network reinforcement, and charging facility cover about 70%, 15%, and 12% of total cost. The network without storage system needs about 2% more reinforcement. Reduction of line reinforcement by 30% increases the battery power about 4 times. 2019 Elsevier Ltd
Languageen
PublisherElsevier
SubjectBattery energy storage system
Electric distribution network
Electric vehicle charging station
Multiple charging infrastructure
Wind power
TitleStochastic model for electric vehicle charging station integrated with wind energy
TypeArticle
Volume Number37
dc.accessType Abstract Only


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