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Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling

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dc.citedby24
dc.contributor.authorAbdolrasol M.en_US
dc.contributor.authorMohamed R.en_US
dc.contributor.authorHannan M.en_US
dc.contributor.authorAl-Shetwi A.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorBlaabjerg F.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid7005169066en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57004922700en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid7004992352en_US
dc.date.accessioned2023-05-29T09:05:39Z
dc.date.available2023-05-29T09:05:39Z
dc.date.issued2021
dc.descriptionElectromagnetic wave emission; Meteorology; Microgrids; Particle swarm optimization (PSO); Scheduling; Solar energy; Solar power generation; Wind; Battery status; BPSO algorithms; Learning rates; Optimal energy; Optimal values; Solar irradiation; Sustainable resources; Virtual power plants (VPP); Neural networksen_US
dc.description.abstractThis letter proposes an enhancement for artificial neural network (ANN) using particle swarm optimization (PSO) to manage renewable energy resources (RESs) in a virtual power plant (VPP) system. This letter highlights the comparison of the ANN-based binary particle swarm optimization (BPSO) algorithm with the original BPSO algorithm. The comparison has been made upon searching the optimal value of the number of nodes in the hidden layers and the learning rate. These parameter values are used in ANN training for microgrid (MG) optimal energy scheduling. The proposed approach has been tested in the VPP system covering MGs involving RESs to minimize the power and giving priority to sustainable resources to participate instead of buying power from the utility grid. This model is tested using real load demand recorded for 24 h in Perlis state, the northern part of Malaysia. Besides, real weather condition data are recorded by Tenaga Nasional Berhad Research solar energy meteorology for a 1-h average (e.g., solar irradiation, wind speed, battery status data, and fuel level). The results show that ANN-PSO gives precise decision compared with BPSO algorithm, which in turn prove that the enhancement for the neural net reaches the optimum level of energy scheduling. � 1986-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9411682
dc.identifier.doi10.1109/TPEL.2021.3074964
dc.identifier.epage12157
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85104625333
dc.identifier.spage12151
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85104625333&doi=10.1109%2fTPEL.2021.3074964&partnerID=40&md5=2b686c8ae86b94cb5c4b3df074b9e455
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25940
dc.identifier.volume36
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Power Electronics
dc.titleArtificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Schedulingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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