dc.contributor | Universidade Estadual Paulista (UNESP) | |
dc.creator | Franco, John F. | |
dc.creator | Rider, Marcos J. | |
dc.creator | Lavorato, Marina | |
dc.creator | Romero, Rubén | |
dc.date | 2014-05-27T11:28:11Z | |
dc.date | 2016-10-25T18:42:48Z | |
dc.date | 2014-05-27T11:28:11Z | |
dc.date | 2016-10-25T18:42:48Z | |
dc.date | 2013-01-21 | |
dc.date.accessioned | 2017-04-06T02:10:28Z | |
dc.date.available | 2017-04-06T02:10:28Z | |
dc.identifier | Electric Power Systems Research, v. 97, p. 51-60. | |
dc.identifier | 0378-7796 | |
dc.identifier | http://hdl.handle.net/11449/74403 | |
dc.identifier | http://acervodigital.unesp.br/handle/11449/74403 | |
dc.identifier | 10.1016/j.epsr.2012.12.005 | |
dc.identifier | WOS:000315934800007 | |
dc.identifier | 2-s2.0-84872305854 | |
dc.identifier | http://dx.doi.org/10.1016/j.epsr.2012.12.005 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/895169 | |
dc.description | The problem of reconfiguration of distribution systems considering the presence of distributed generation is modeled as a mixed-integer linear programming (MILP) problem in this paper. The demands of the electric distribution system are modeled through linear approximations in terms of real and imaginary parts of the voltage, taking into account typical operating conditions of the electric distribution system. The use of an MILP formulation has the following benefits: (a) a robust mathematical model that is equivalent to the mixed-integer non-linear programming model; (b) an efficient computational behavior with exiting MILP solvers; and (c) guarantees convergence to optimality using classical optimization techniques. Results from one test system and two real systems show the excellent performance of the proposed methodology compared with conventional methods. © 2012 Published by Elsevier B.V. All rights reserved. | |
dc.language | eng | |
dc.relation | Electric Power Systems Research | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Active power losses reduction | |
dc.subject | Distributed generation | |
dc.subject | Mixed-integer linear programming | |
dc.subject | Reconfiguration of electric distribution systems | |
dc.subject | Active power loss | |
dc.subject | Classical optimization | |
dc.subject | Conventional methods | |
dc.subject | Distribution systems | |
dc.subject | Electric distribution systems | |
dc.subject | Imaginary parts | |
dc.subject | Linear approximations | |
dc.subject | MILP formulation | |
dc.subject | Mixed integer linear programming | |
dc.subject | Mixed-integer | |
dc.subject | Mixed-integer nonlinear programming | |
dc.subject | Operating condition | |
dc.subject | Optimality | |
dc.subject | Real systems | |
dc.subject | Test systems | |
dc.subject | Distributed power generation | |
dc.subject | Mathematical models | |
dc.subject | Optimization | |
dc.subject | Integer programming | |
dc.title | A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation | |
dc.type | Otro | |