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An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm

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dc.citedby64
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorMohamed A.en_US
dc.contributor.authorAmiruldin U.A.U.en_US
dc.contributor.authorAbidin I.B.Z.en_US
dc.contributor.authorUddin M.N.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57195440511en_US
dc.contributor.authorid26422804600en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid55663372800en_US
dc.date.accessioned2023-05-29T06:52:14Z
dc.date.available2023-05-29T06:52:14Z
dc.date.issued2018
dc.descriptionBins; Controllers; Fueling; Gas generators; Global optimization; Health; Integration; Learning algorithms; Optimization; Particle swarm optimization (PSO); Power generation; Reliability; Renewable energy resources; Scheduling; Wind; Backtracking search algorithms; Micro grid; Optimal scheduling; Scheduling controllers; Virtual power plants; Wind speed; Power controlen_US
dc.description.abstractThis paper presents a novel binary backtracking search algorithm (BBSA) for an optimal scheduling controller applied to the IEEE 14-bus test system for controlling distributed generators (DGs) in microgrids (MGs) in the form of virtual power plant (VPP) toward sustainable renewable energy sources integration. The VPP and MGs models are simulated and tested based on real parameters and loads data recorded in Perlis, Malaysia, employed on each bus of the system for 24 h. BBSA optimization algorithm provides the best binary fitness function, i.e., global minimum fitness for finding the best cell to generate the optimal schedule. The fitness function is generated based on real conditions such as solar irradiation and wind speed and preparation of battery charge/discharges, fuel states and demand of the specific hour. The obtained results show that the BBSA algorithm provides the best schedule to control DGs ON and OFF based on controller decision. Results obtained from the BBSA are compared with binary particle swarm optimization in terms of objective function and power saving to validate the developed controller. The developed BBSA optimization algorithm minimizes the power generation cost, reduces power losses, delivers reliable and high-quality power to the loads, and integrates priority-based sustainable MGs into the grid. Thus, VPP can enable efficient integration of DGs and MGs into the grid by balancing their variability. � 1972-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TIA.2018.2797121
dc.identifier.epage2844
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85040985677
dc.identifier.spage2834
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85040985677&doi=10.1109%2fTIA.2018.2797121&partnerID=40&md5=245e53026e24d9788bcb25a6a872142a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23832
dc.identifier.volume54
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.titleAn Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithmen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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