<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2025000200008</article-id>
<article-id pub-id-type="doi">10.14483/23448393.23474</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Energy Management System using Particle Swarm Optimization for Operating Costs Reduction in AC Microgrids with Battery Storage during Grid-Connected and Islanded Operation]]></article-title>
<article-title xml:lang="es"><![CDATA[Sistema de gestión de energía mediante optimización por enjambre de partículas para la reducción de costos operativos en microrredes de CAconalmacenamiento en baterías durante la operación interconectada y aislada]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Figueroa-Saavedra]]></surname>
<given-names><![CDATA[Hugo Alessandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Grisales Noreña]]></surname>
<given-names><![CDATA[Luis Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cortés Caicedo]]></surname>
<given-names><![CDATA[Brandon]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Talca  ]]></institution>
<addr-line><![CDATA[Curicó ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Institución Universitaria Pascual Bravo  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<volume>30</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2025000200008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2025000200008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2025000200008&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context:  This paper proposes an energy management system (EMS) for battery energy storage systems (BESS) to reduce operating costs in AC microgrids (MGs) operating in grid-connected (GON) and islanded (GOFF) mode, considering energy purchase, conventional generation, and maintenance costs while accounting for all the operational constraints of the system and its components.  Method:  A master-slave methodology based on particle swarm optimization (PSO) andanhourlypowerflowbasedonthesuccessiveapproximations method (SAM)is used as a smart BESS operation strategy. This proposal is validated in a 33-bus AC MGoperating in GONandGOFFmodes,incomparisonwithtwomethodsutilizing the vortex search algorithm (VSA) and conitnuos version of the Chu &amp; Beasley ge netic algorithm (CBGA) and the same power flow.  Results:  The PSO-based EMSachievedthelowestcostsi.e., 6897.59 USD/day (GON) and 17 527.42 USD/day (GOFF), with cost reductions of 1.45 and 0.13%, and low standard deviation values (0.067 and 0.014%), which confirms its efficiency, robust ness, and constraint compliance.  Conclusions:  The EMS based on PSO/SAM delivers superior solution quality and processing times in both modes of operation. In GON mode, it reduces the mean costs by0.0287%comparedtotheVSAand0.2252%vs.the CBGA,where as,inGOFF mode, the reductions are 0.0191 and 0.0355%, respectively. These results reflect a more effective cost reduction than exact methods, which constitutes this paper&#8217;s main contribution.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto:  En este artículo se propone un sistema de gestión energética (EMS) para sistemas de almacenamiento de energía en baterías (BESS) orientado a la reducción de costos operativos en microrredes (MG) AC que operan conectados a la red (GON) y en modo isla (GOFF), considerando los costos de compra de energía, generación convencional y mantenimiento a la vez que se cumple con todas las restricciones operativas del sistema y sus componentes.  Método:  Comoestrategia de operación inteligente del sistema de almacenamiento en baterías, se utiliza una metodología maestro-esclavo basada en optimización por enjambre de partículas y un flujo de potencia horario basado en el método de aproximaciones sucesivas (SAM). Esta propuesta se valida en una MG-AC de 33 nodos, operando en modos GON y GOFF, en comparación con dos métodos alternativos que utilizan el algoritmo de búsqueda por vórtices (VSA) y el algoritmo genético continuo de Chu&amp;Beasley (CBGA) y el mismo método de flujo de potencia. Resultados: El EMS basado en PSOalcanzó los menores costos, i.e., 6897.59 USD/día (GON) y 17 527.42 USD/día (GOFF), con reducciones en costos del 1.45 y el 0.13% respectivamente y bajas desviaciones estándar (0.067 y 0.014%), lo que confirma su eficiencia, robustez y cumplimiento de restricciones.  Conclusiones:  El EMS basado en PSO/SAM ofrece una calidad de solución y un tiempo de procesamiento superiores en ambos modos de operación. En el modo GON, reduce los costos promedio en un 0.0287% en comparación con el VSA y en un 0.2252% con respecto al CBGA, mientras que, en el modo GOFF, las reducciones son de 0.0191 y 0.0355% respectivamente. Estos resultados reflejan una reducción de costos más efectiva que la obtenida por métodos exactos, lo que constitye la principal contribución de este artículo.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[energy management system]]></kwd>
<kwd lng="en"><![CDATA[battery energy storage]]></kwd>
<kwd lng="en"><![CDATA[microgrids]]></kwd>
<kwd lng="en"><![CDATA[swarm optimization]]></kwd>
<kwd lng="en"><![CDATA[grid-on]]></kwd>
<kwd lng="en"><![CDATA[grid-off]]></kwd>
<kwd lng="es"><![CDATA[sistema de gestión de energía]]></kwd>
<kwd lng="es"><![CDATA[almacenamiento en baterías]]></kwd>
<kwd lng="es"><![CDATA[microrredes]]></kwd>
<kwd lng="es"><![CDATA[optimización por enjambre de partículas]]></kwd>
<kwd lng="es"><![CDATA[conectado a la red]]></kwd>
<kwd lng="es"><![CDATA[aislado de la red]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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