Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89998
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dc.contributor.authorLee, Xuecongen
dc.contributor.authorYan, Mengxuanen
dc.contributor.authorXu, Fang Yuanen
dc.contributor.authorWang, Yueen
dc.contributor.authorFan, Yiliangen
dc.contributor.authorLee, Zekaien
dc.contributor.authorWen, Yonggangen
dc.contributor.authorMohammad Shahidehpouren
dc.contributor.authorLai, Loi Leien
dc.date.accessioned2019-07-15T04:45:28Zen
dc.date.accessioned2019-12-06T17:38:21Z-
dc.date.available2019-07-15T04:45:28Zen
dc.date.available2019-12-06T17:38:21Z-
dc.date.issued2019en
dc.identifier.citationLee, X., Yan, M., Xu, F. Y., Wang, Y., Fan, Y., Lee, Z., . . . Lai, L. L. (2019). Virtual storage-based DSM with error-driven prediction modulation for microgrids. IEEE Access, 7, 71109-71118. doi:10.1109/ACCESS.2019.2913898en
dc.identifier.urihttps://hdl.handle.net/10356/89998-
dc.description.abstractMicrogrids consider adjustable loads in demand-side management (DSM), which respond to dynamic market prices. A reliable DSM strategy relies on load forecasting techniques in day-ahead (DA) scheduling. This paper applies an error-driven prediction modulation to evaluate these differences. In addition, this paper creates two new DSM methods with an evaluation environment to utilize this modulation. The first method adds this modulation directly to traditional microgrid DSM with electrical storage. The second method creates two virtual sub-storages for behavior adjustment in both DA and real-time (RT) markets. The results of numerical studies indicate that the new DSM methods can reduce microgrid operation costs.en
dc.format.extent10 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE Accessen
dc.rightsArticles accepted before 12 June 2019 were published under a CC BY 3.0 or the IEEE Open Access Publishing Agreement license. Questions about copyright policies or reuse rights may be directed to the IEEE Intellectual Property Rights Office at +1-732-562-3966 or copyrights@ieee.org.en
dc.subjectStorageen
dc.subjectMicrogriden
dc.subjectEngineering::Computer science and engineeringen
dc.titleVirtual storage-based DSM with error-driven prediction modulation for microgridsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.identifier.doi10.1109/ACCESS.2019.2913898en
dc.description.versionPublished versionen
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