000118809 001__ 118809
000118809 005__ 20230914083511.0
000118809 0247_ $$2doi$$a10.24084/repqj20.436
000118809 0248_ $$2sideral$$a130003
000118809 037__ $$aART-2022-130003
000118809 041__ $$aeng
000118809 100__ $$aOviedo-Carranza, S.
000118809 245__ $$aOptimal Operation of a Distributed Generation Microgrid based on the Multi-Objective Genetic Algorithms
000118809 260__ $$c2022
000118809 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118809 5203_ $$aThis document describes the application of multi-objective genetic algorithms as techniques and tools to optimize generation and distribution in small microgrids. In this way, genetic algorithms have been used for the allocation of distributed generation to reduce losses and improve the voltage profile. The IEEE14 network has been taken as a study and analysis model. This smart grid has 14 nodes and integrates several generation units, both conventional and renewable, transformers, and multiple loads. In this way, a multi-objective metaheuristic algorithm is proposed with the purpose of planning the power distribution grid based on a series of conditions such as the optimal generation configuration, the minimization of power losses in the lines, power transfer capacity, the reduction of CO2 emissions, and the optimization of the benefits obtained in renewable generation. The overall purpose is the development of an intelligent microgrid management system that is capable of determining the optimal configuration, by estimating demand, energy costs, and operating costs. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.
000118809 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T28_20R$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104711RB-100
000118809 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000118809 592__ $$a0.148$$b2022
000118809 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ4
000118809 593__ $$aRenewable Energy, Sustainability and the Environment$$c2022$$dQ4
000118809 593__ $$aEnergy Engineering and Power Technology$$c2022$$dQ4
000118809 594__ $$a0.6$$b2022
000118809 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118809 700__ $$0(orcid)0000-0001-7764-235X$$aArtal-Sevil, J.$$uUniversidad de Zaragoza
000118809 700__ $$0(orcid)0000-0002-4770-0069$$aDomínguez-Navarro, J. A.$$uUniversidad de Zaragoza
000118809 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000118809 773__ $$g20 (2022), 789-794$$pRenewable energy power qual. j.$$tRenewable Energy and Power Quality Journal$$x2172-038X
000118809 8564_ $$s2827685$$uhttps://zaguan.unizar.es/record/118809/files/texto_completo.pdf$$yVersión publicada
000118809 8564_ $$s2686478$$uhttps://zaguan.unizar.es/record/118809/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118809 909CO $$ooai:zaguan.unizar.es:118809$$particulos$$pdriver
000118809 951__ $$a2023-09-13-12:52:56
000118809 980__ $$aARTICLE