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Optimal Placement of DG in Distribution network for Power loss minimization using NLP & PLS Technique

dc.contributor.authorBabu, P. Vijay
dc.contributor.authorSingh, S.P.
dc.date.accessioned2020-02-19T09:40:22Z
dc.date.available2020-02-19T09:40:22Z
dc.date.issued2016-12
dc.description.abstractA novel application of General Algebraic Modelling System (GAMS) software has been reported in this paper to determine the load flow solution for both radial and mesh distribution network. With interfacing of GAMS and MATLAB, optimal sitting and sizing of Distributed Generator (DG) in radial/mesh distribution systems is efficiently done in order to reduce power loss and improvement of voltage profile in distribution systems. The main contributions of the paper are as follows: Load flow solution by GAMS software for both radial and mesh distribution networks. Planning of optimal sitting and sizing of DG has been carried out in two phases. In first phase, optimal locations of DG based on the Power Loss Sensitivity (PLS) and in second phase, optimal size of DG has been determined by CONOPT solver of GAMS, solves Non-Linear programming (NLP). Two types of DGs have been considered for analysis i.e. Type 1 (DG operating at unity power factor) and Type 3 (DG operating at lagging power factor). The results are obtained on IEEE 33-bus and IEEE 69-bus radial distribution systems and also compared with other existing methods. The test results demonstrate that the proposed method produced superior results in respect of loss reduction, improvement in voltage profile and computational time.en_US
dc.description.sponsorshipBio Logic,MAGEEP et al.,Ministry of New and Renewable Energy,Goverment of India,SEMYUNG,ZEISSen_US
dc.identifier.issn18766102
dc.identifier.urihttps://idr-sdlib.iitbhu.ac.in/handle/123456789/629
dc.language.isoen_USen_US
dc.publisherElsevier Ltden_US
dc.subjectDistributed Generatoren_US
dc.subjectGAMSen_US
dc.subjectRadial/mesh distribution systemen_US
dc.titleOptimal Placement of DG in Distribution network for Power loss minimization using NLP & PLS Techniqueen_US
dc.typeArticleen_US

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