A new intelligent charging strategy in a stationary hydrogen energy-based power plant for optimal demand side management of plug-in EVs

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dc.authorid0000-0002-5136-0829
dc.authorid0000-0002-8093-4078
dc.contributor.authorCakmak, Recep
dc.contributor.authorMeral, Hasan
dc.contributor.authorBayrak, Gokay
dc.date.accessioned2026-02-08T15:15:18Z
dc.date.available2026-02-08T15:15:18Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractStationary hydrogen energy-based power plants generating electricity to supply high-powered plug-in electric vehicles (PEVs) have recently become popular in renewable energy-based power plants. Besides, in a PEV charging station, various types of powered charge devices can be established such as DC fast chargers or 3.7 kW, 7.4 kW, 11 kW, and 22 kW AC chargers. This paper introduces a demand-side management-oriented optimal charging strategy that includes two stages for PEVs in a hydrogen energy-based microgrid. The paper focuses on two stages to execute an optimal charging of PEVs in compliance with their users' requests and satisfaction and considering the power system loading. It is assumed that there are three types of chargers in the PEV charging station and the users. In the first stage randomly created requests are classified by an ensemble learning classifier method that performs higher performance classification by combining the results from multiple classifiers in a machine learning classification. The second stage schedules the PEVs according to the classification results and users' requests. To test the proposed system, first random requests are created then they are sent to the classifier, and the results of classifiers are scheduled in each other. The demand-side management-oriented charge scheduling and managing strategy which includes the proposed two stages has been compared with nonmanaged cases. Case study results reveal that the proposed approach provides 52.1% peak load reduction and 72.3% valley filling improvement by the SOS algorithm. The results highlight the advantages of the proposed system in terms of peak reduction and valley filling.
dc.identifier.doi10.1016/j.ijhydene.2024.02.132
dc.identifier.endpage414
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.scopus2-s2.0-85186173328
dc.identifier.scopusqualityQ1
dc.identifier.startpage400
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2024.02.132
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5696
dc.identifier.volume75
dc.identifier.wosWOS:001298151900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectHydrogen energy
dc.subjectMicrogrid
dc.subjectDemand side management
dc.subjectPEVs
dc.subjectMachine learning
dc.subjectSymbiotic organisms search algorithm
dc.subjectRUSBoost
dc.titleA new intelligent charging strategy in a stationary hydrogen energy-based power plant for optimal demand side management of plug-in EVs
dc.typeArticle

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