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Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks

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dc.citedby17
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorSuhail Hussain S.M.en_US
dc.contributor.authorUstun T.S.en_US
dc.contributor.authorSarker M.R.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid22035146400en_US
dc.contributor.authorid43761679200en_US
dc.contributor.authorid57537703000en_US
dc.contributor.authorid37461740800en_US
dc.date.accessioned2023-05-29T09:05:47Z
dc.date.available2023-05-29T09:05:47Z
dc.date.issued2021
dc.descriptionControllers; Cost reduction; Electric load dispatching; Energy management; Microgrids; Neural networks; Control approach; Intelligent controllers; Microgrid; Multi micro-grids; Network-based; Performance; Power plant system; Scheduling control; System conditions; Virtual power plants; Schedulingen_US
dc.description.abstractThis study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algo-rithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers� robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN�s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6507
dc.identifier.doi10.3390/en14206507
dc.identifier.issue20
dc.identifier.scopus2-s2.0-85117415327
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85117415327&doi=10.3390%2fen14206507&partnerID=40&md5=6bb64f94db43fc6ea3a3275134058f92
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25959
dc.identifier.volume14
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleEnergy management scheduling for microgrids in the virtual power plant system using artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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