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

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dc.contributor.author Babu, P. Vijay
dc.contributor.author Singh, S.P.
dc.date.accessioned 2020-02-19T09:40:22Z
dc.date.available 2020-02-19T09:40:22Z
dc.date.issued 2016-12
dc.identifier.issn 18766102
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/629
dc.description.abstract A 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.sponsorship Bio Logic,MAGEEP et al.,Ministry of New and Renewable Energy,Goverment of India,SEMYUNG,ZEISS en_US
dc.language.iso en_US en_US
dc.publisher Elsevier Ltd en_US
dc.subject Distributed Generator en_US
dc.subject GAMS en_US
dc.subject Radial/mesh distribution system en_US
dc.title Optimal Placement of DG in Distribution network for Power loss minimization using NLP & PLS Technique en_US
dc.type Article en_US


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