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Photovoltaic power control algorithms using bess
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dc.contributor.authorKim, Jun Sung-
dc.contributor.authorNa, Ui Kyun-
dc.contributor.authorSong, Jae Ju-
dc.contributor.authorJung, Jae Sung-
dc.date.issued2021-08-01-
dc.identifier.issn2287-4364-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/32217-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113319558&origin=inward-
dc.description.abstractIn the case of renewable energy, power generation is affected by factors such as the external climate environment. In order to efficiently storage and use of renewable energy, Battery Energy Storage Systems (BESS) are being used. However, BESS faced issues such as fire accident and stability due to lack of optimization operating system. In this paper, we propose a predict system of solar power generation using the ANN(Artificial Neural Network) and an BESS operation scheduling algorithm for BESS optimization. In this paper, we verified the proposed algorithm for real-time output compensation service and BESS operation stability, and we expect to address the safety issues of BESS.-
dc.description.sponsorshipThis work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20183010141100)-
dc.language.isoeng-
dc.publisherKorean Institute of Electrical Engineers-
dc.subject.meshANN (artificial neural network)-
dc.subject.meshBattery energy storage systems-
dc.subject.meshFire accident-
dc.subject.meshOperation scheduling-
dc.subject.meshOperation stability-
dc.subject.meshPhotovoltaic power-
dc.subject.meshRenewable energies-
dc.subject.meshUse of renewable energies-
dc.titlePhotovoltaic power control algorithms using bess-
dc.typeArticle-
dc.citation.endPage1172-
dc.citation.number8-
dc.citation.startPage1167-
dc.citation.titleTransactions of the Korean Institute of Electrical Engineers-
dc.citation.volume70-
dc.identifier.bibliographicCitationTransactions of the Korean Institute of Electrical Engineers, Vol.70 No.8, pp.1167-1172-
dc.identifier.doi10.5370/kiee.2021.70.8.1167-
dc.identifier.scopusid2-s2.0-85113319558-
dc.identifier.urlhttp://journal.auric.kr/kiee/XmlViewer/f407661-
dc.subject.keywordArtificial Neural Network-
dc.subject.keywordBattery Energy Storage System-
dc.subject.keywordOperation Scheduling-
dc.subject.keywordPhotovoltaic-
dc.type.otherArticle-
dc.identifier.pissn1975-8359-
dc.description.isoafalse-
dc.subject.subareaElectrical and Electronic Engineering-
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