| dc.contributor.author |
Kyriakarakos, G |
en |
| dc.contributor.author |
Piromalis, DD |
en |
| dc.contributor.author |
Dounis, AI |
en |
| dc.contributor.author |
Arvanitis, KG |
en |
| dc.contributor.author |
Papadakis, G |
en |
| dc.date.accessioned |
2014-06-06T06:52:39Z |
|
| dc.date.available |
2014-06-06T06:52:39Z |
|
| dc.date.issued |
2013 |
en |
| dc.identifier.issn |
03062619 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.apenergy.2012.10.011 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/6100 |
|
| dc.subject |
Demand side management |
en |
| dc.subject |
Grey predictor |
en |
| dc.subject |
Microgrids |
en |
| dc.subject |
Multi agent system |
en |
| dc.subject |
Polygeneration |
en |
| dc.subject |
Smartgrids |
en |
| dc.subject.other |
Demand side managements |
en |
| dc.subject.other |
Grey predictor |
en |
| dc.subject.other |
Micro grid |
en |
| dc.subject.other |
Poly-generation |
en |
| dc.subject.other |
Smartgrids |
en |
| dc.subject.other |
Algorithms |
en |
| dc.subject.other |
Desalination |
en |
| dc.subject.other |
Energy management |
en |
| dc.subject.other |
Fuzzy logic |
en |
| dc.subject.other |
Hydrogen |
en |
| dc.subject.other |
Management |
en |
| dc.subject.other |
Multi agent systems |
en |
| dc.subject.other |
Optimization |
en |
| dc.subject.other |
Potable water |
en |
| dc.subject.other |
Specifications |
en |
| dc.subject.other |
Topology |
en |
| dc.subject.other |
Water filtration |
en |
| dc.subject.other |
Electric inverters |
en |
| dc.subject.other |
demand-side management |
en |
| dc.subject.other |
desalination |
en |
| dc.subject.other |
drinking water |
en |
| dc.subject.other |
economic analysis |
en |
| dc.subject.other |
energy conservation |
en |
| dc.subject.other |
hydrogen |
en |
| dc.subject.other |
installation |
en |
| dc.subject.other |
transportation |
en |
| dc.title |
Intelligent demand side energy management system for autonomous polygeneration microgrids |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.apenergy.2012.10.011 |
en |
| heal.publicationDate |
2013 |
en |
| heal.abstract |
Autonomous polygeneration microgrids is a novel approach in addressing the needs of remote areas. These needs can include power, fuel for transportation in the form of hydrogen, potable water through desalination and space heating and cooling. This approach has been investigated technically and economically and has proved viable. Further research has taken place in the supervisory management of this topology using computational intelligence techniques like fuzzy logic, which has optimized the concept minimizing the sizes of the installed components. The optimal design of the system can meet, though, only the design principles and needs. In reality experience has shown that most autonomous power systems operate out of specifications very shortly after installation or after a couple of years new needs arise and it is not possible economic wise for the people to extend it. In these cases the microgrid would struggle to cover the increased needs and in the end fail, causing blackouts. A solution to this is partial load shedding in an intelligent manner. This paper presents a multi agent system for intelligent demand side management of the polygeneration microgrid topology which also includes grey prediction algorithms for better management. This approach can also be used for designing the optimal polygeneration microgrid for a given amount of an investment. The results show that the proposed intelligent demand side management system can address its design principles successfully and guaranty the most effective operation even in conditions near and over the limits of the design specification of the autonomous polygeneration microgrid. © 2012 Elsevier Ltd. |
en |
| heal.journalName |
Applied Energy |
en |
| dc.identifier.volume |
103 |
en |
| dc.identifier.doi |
10.1016/j.apenergy.2012.10.011 |
en |
| dc.identifier.spage |
39 |
en |
| dc.identifier.epage |
51 |
en |