Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor	Institut de Recerca en Energía de Catalunya
dc.contributor	Universitat Politècnica de Catalunya. CITCEA - Centre d'Innovació Tecnològica en Convertidors Estàtics i Accionaments
dc.contributor.author	Marzband, Mousa
dc.contributor.author	Yousefnejad, Ebrahim
dc.contributor.author	Sumper, Andreas
dc.contributor.author	Domínguez García, José Luis
dc.date	2016-02-01
dc.identifier.citation	Marzband, M., Yousefnejad, E., Sumper, A., Domínguez-Garcia, J.L. Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization. "International journal of electrical power and energy systems", 01 Febrer 2016, vol. 75, p. 265-274.
dc.identifier.citation	0142-0615
dc.identifier.citation	10.1016/j.ijepes.2015.09.010
dc.identifier.uri	http://hdl.handle.net/2117/83182
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Energies
dc.subject	Electric engineering
dc.subject	Real time EMS
dc.subject	Short term scheduling
dc.subject	Very short term scheduling
dc.subject	Microgrid
dc.subject	Ant colony optimization
dc.subject	Optimal operation
dc.subject	ALGORITHM
dc.subject	STORAGE
dc.subject	Energia elèctrica
dc.title	Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/article
dc.description.abstract	In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too. (C) 2015 Elsevier Ltd. All rights reserved.

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