ArchivIA Università degli Studi di Catania
 

ArchivIA - Archivio istituzionale dell'Universita' di Catania >
Tesi >
Tesi di dottorato >
Area 09 - Ingegneria industriale e dell'informazione >

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10761/1338

Data: 22-feb-2013
Autori: Alsayed, Mohammed
Titolo: Optimal Sizing of Power Generation Systems Based on Multi Criteria Decision Making (MCDM)
Abstract: Power Generation Systems (PGSs) based on Hybrid Renewable Energy (HRE) are one of the promising solutions for future distributed generation systems. Among different configurations, Hybrid Photovoltaic - Wind turbine (PV-WT) grid connected PGSs are the most adopted for their good performance. However, due to the complexity of the system caused by wind power variability and solar radiation intermittency, the optimal balance between these two energy sources requires particular attention to achieve a good engineering solution. This thesis deals with the optimal sharing and sizing of PV-WT by adopting different Multi Criteria Decision Making (MCDM) optimization approaches. Different approaches have been developed using Multi attribute decision Making (MADM) and Multi Objective Decision Making (MODM). Moreover, sensitivity and uncertainty of MCDM algorithms have been analyzed, by considering different weighting criteria techniques with different fluctuation scenarios of wind speed and solar radiation profiles, and by considering stochastic analysis to solar radiation, wind speed, and load demand input data , thus highlighting advantages and drawbacks of the proposed optimal sizing approaches. The developed approaches could be assumed as a powerful roadmap for decision makers, analysts, and policy makers.
InArea 09 - Ingegneria industriale e dell'informazione

Full text:

File Descrizione DimensioniFormatoConsultabilità
LSYMMM81H21Z218V-LSYMMM81H21Z218V-Alsayed thesis.pdfLSYMMM81H21Z218V-Alsayed thesis2,46 MBAdobe PDFVisualizza/apri


Tutti i documenti archiviati in ArchivIA sono protetti da copyright. Tutti i diritti riservati.


Segnala questo record su
Del.icio.us

Citeulike

Connotea

Facebook

Stumble it!

reddit


 

  Browser supportati Firefox 3+, Internet Explorer 7+, Google Chrome, Safari

ICT Support, development & maintenance are provided by the AePIC team @ CILEA. Powered on DSpace Software.