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Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage

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dc.citedby55
dc.contributor.authorHuy P.D.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorYong J.Y.en_US
dc.contributor.authorTan K.M.en_US
dc.contributor.authorEkanayake J.B.en_US
dc.contributor.authorid57192416703en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid56119339200en_US
dc.contributor.authorid56119108600en_US
dc.contributor.authorid7003409510en_US
dc.date.accessioned2023-05-29T08:10:39Z
dc.date.available2023-05-29T08:10:39Z
dc.date.issued2020
dc.descriptionElectric power factor; Electric power transmission networks; Evolutionary algorithms; Optimization; Differential Evolution; Differential evolution algorithms; Distributed generation source; Multiple distributed generations; Optimal allocation; Optimisations; Power factorAbstract; Power system constraints; Distributed power generation; algorithm; distribution system; energy planning; operations technology; optimizationen_US
dc.description.abstractIn this paper, an optimised framework utilising a Differential Evolution algorithm is presented to optimally integrate multiple distributed generation sources simultaneously into the distribution grid. By considering the important power system constraints, the proposed algorithm optimises the location, sizing and power factor setting for each distributed generation source to minimise network losses and maximise distributed generation integration. Various case studies were conducted at constant or varying levels of load and generation in both the planning stage and the real-time operation stage. The results of all case studies revealed that the proposed Differential Evolution-based algorithm delivered better performance in terms of network loss reduction and maximised distributed generation compared to other existing methods. The network loss reduction of 95.71% was achieved when all three parameters of placement, sizing and power factor of distributed generation were optimised simultaneously. In addition, a practical framework with a varying optimal power factor for distributed generation was designed. The optimal power factor setting for each distributed generation source was dynamically adjusted during real-time power grid operation, resulting in further minimisation of the system loss reduction. The overall loss reduction achieved was 96.04% relative to the base case of no distributed generation connection. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo117011
dc.identifier.doi10.1016/j.energy.2020.117011
dc.identifier.scopus2-s2.0-85078273267
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078273267&doi=10.1016%2fj.energy.2020.117011&partnerID=40&md5=aeb234fe0bed033b898b3175f0e568ea
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25540
dc.identifier.volume195
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy
dc.titleOptimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stageen_US
dc.typeArticleen_US
dspace.entity.typePublication
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