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Priority-based vehicle-to-grid scheduling for minimization of power grid load variance

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dc.citedby10
dc.contributor.authorHashim M.S.en_US
dc.contributor.authorYong J.Y.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorTan K.M.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorTariq M.en_US
dc.contributor.authorid57216688148en_US
dc.contributor.authorid56119339200en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid56119108600en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid57220656842en_US
dc.date.accessioned2023-05-29T09:06:55Z
dc.date.available2023-05-29T09:06:55Z
dc.date.issued2021
dc.descriptionBattery management systems; Charging (batteries); Commercial vehicles; Electric load flow; Electric power transmission networks; Optimization; Scheduling; Secondary batteries; Vehicle-to-grid; Bidirectional power flow; Energy; Grid scheduling; Minimisation; Optimisations; Peak load; Peak loading; Power grids; Priority-based; Vehicle to grids; Electric vehiclesen_US
dc.description.abstractElectric vehicles are considered as additional loads to the power grid and may pose possible threats to the power grid reliability by overloading the grid equipment, disturbing the grid voltage stability and injecting harmonics into the power grid. Nonetheless, electric vehicles can also provide supports to the power grid through Vehicle-to-Grid application by discharging battery energy into the power grid. This paper presents an optimal priority-based Vehicle-to-Grid scheduling with the objective to minimize the grid load variance. An optimal strategy was developed to optimize the amount of charging/discharging power based on the electric vehicle battery's state-of-charge. This study desires to find a central point which can benefit power utility and electric vehicle users. The algorithm can operate in three modes, which are valley filling, peak load shaving and priority charging. The charging of the electric vehicle can be performed in all three modes, as long as the charging of electric vehicle is required. Meanwhile, discharging of the electric vehicle only occurs during peak load shaving mode. To ensure the practicability of this study, numerous constraints were considered and the study was conducted in a commercial-residential area with electric vehicle mobility of 1300. The results indicated that the algorithm was able to minimize the grid load variance while prioritizing the electric vehicle with a low percentage of state-of-charge. The original maximum variation between peak loading and off-peak loading measured at 5 MW was effectively reduced to 1.5 MW following the deployment of the proposed algorithm. � 2021 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102607
dc.identifier.doi10.1016/j.est.2021.102607
dc.identifier.scopus2-s2.0-85105306297
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105306297&doi=10.1016%2fj.est.2021.102607&partnerID=40&md5=f6035fa9512d13e3098e0914778aeb1d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26117
dc.identifier.volume39
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.titlePriority-based vehicle-to-grid scheduling for minimization of power grid load varianceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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