ArchivIA Università degli Studi di Catania

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

Please use this identifier to cite or link to this item:

Issue Date: 22-Feb-2013
Authors: Alsayed, Mohammed
Title: 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.
Appears in Collections:Area 09 - Ingegneria industriale e dell'informazione

Files in This Item:

File Description SizeFormatVisibility
LSYMMM81H21Z218V-LSYMMM81H21Z218V-Alsayed thesis.pdfLSYMMM81H21Z218V-Alsayed thesis2,46 MBAdobe PDFView/Open

Items in ArchivIA are protected by copyright, with all rights reserved, unless otherwise indicated.

Share this record




Stumble it!



  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.