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A fuzzy cognitive maps-petri nets energy management system for autonomous polygeneration microgrids

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dc.contributor.author Kyriakarakos, G en
dc.contributor.author Dounis, AI en
dc.contributor.author Arvanitis, KG en
dc.contributor.author Papadakis, G en
dc.date.accessioned 2014-06-06T06:51:34Z
dc.date.available 2014-06-06T06:51:34Z
dc.date.issued 2012 en
dc.identifier.issn 15684946 en
dc.identifier.uri http://dx.doi.org/10.1016/j.asoc.2012.01.024 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/5576
dc.subject Fuzzy cognitive maps en
dc.subject Microgrids en
dc.subject Particle swarm optimization en
dc.subject Petri nets en
dc.subject Polygeneration en
dc.subject.other Design and operations en
dc.subject.other Energy flow en
dc.subject.other Energy management system en
dc.subject.other Fuzzy cognitive map en
dc.subject.other Integrated approach en
dc.subject.other Interactive optimization en
dc.subject.other Management systems en
dc.subject.other Micro grid en
dc.subject.other Part load en
dc.subject.other Poly-generation en
dc.subject.other Remote areas en
dc.subject.other Simulation and optimization en
dc.subject.other Software platforms en
dc.subject.other Economics en
dc.subject.other Fuzzy rules en
dc.subject.other Fuzzy systems en
dc.subject.other Investments en
dc.subject.other Large scale systems en
dc.subject.other Management en
dc.subject.other Particle swarm optimization (PSO) en
dc.subject.other People movers en
dc.subject.other Petri nets en
dc.subject.other Potable water en
dc.subject.other Energy management en
dc.title A fuzzy cognitive maps-petri nets energy management system for autonomous polygeneration microgrids en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.asoc.2012.01.024 en
heal.publicationDate 2012 en
heal.abstract Autonomous polygeneration microgrids (APM) are a relatively new approach in covering specific needs like power, potable water and fuel for transportation, in remote areas. This approach has been proved to be technically feasible nowadays and even present itself as an economically viable investment. The initial management system built for this approach is a simple ON/OFF supervisor which can make the APM operate, but not in an optimal way. The devices cannot be operated in part load and as a consequence there is little room for optimization. A combined fuzzy cognitive maps (FCMs)-petri nets (PN) approach has been developed for the energy management of such a system. The PN is used as an activator in the fuzzy cognitive map structure so as to enable different FCMs to be activated depending on the state of the microgrid. This combination forms an integrated approach to the energy management of the microgrid. Using this approach considerable optimization in the design and operation of the microgrid is possible. A methodology for simultaneous and interactive optimization of the energy management system along with the sizing of the various devices of the actual microgrid is implemented. A software platform consisting of TRNSYS, TRNOPT and GenOPT software packages was used for simulation and optimization. Particle swarm optimization is applied both for the sizing of the system and the optimization of the FCM weights and PN parameters. Two microgrids were designed, one based on the FCM-PN energy management system (FPEMS) and one on the ON/OFF approach. The results show that FPEMS manages the energy flows more effectively throughout the year which leads to a considerable decrease in the sizing of the various components of the microgrid. © 2012 Elsevier B.V. All rights reserved. en
heal.journalName Applied Soft Computing Journal en
dc.identifier.issue 12 en
dc.identifier.volume 12 en
dc.identifier.doi 10.1016/j.asoc.2012.01.024 en
dc.identifier.spage 3785 en
dc.identifier.epage 3797 en


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