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  5. A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks
 
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A Probabilistic Modeling of Photo Voltaic Modules and Wind Power Generation Impact on Distribution Networks

Author(s)
Soroudi, Alireza  
Aien, Morteza  
Ehsan, Mehdi  
Uri
http://hdl.handle.net/10197/6193
Date Issued
2011-01
Date Available
2014-11-24T16:58:58Z
Abstract
The rapid growth in use of renewable intermittent energy resources, like wind turbines (WTs) and solar panels, in distribution networks has increased the need for having an accurate and efficient method of handling the uncertainties associated with these technologies. In this paper, the unsymmetrical two point estimate method (US2PEM) is used to handle the uncertainties of renewable energy resources. The uncertainty of intermittent generation of WT, photo voltaic cells, and also electric loads, as input variables, are taken into account. The variation of active losses and imported power from the main grid are defined as output variables. The US2PEM is compared to symmetrical two point estimate method, Gram-Charlier method, and Latin hypercube sampling method, where Monte Carlo simulation is used as a basis for comparison. The validity of the proposed method is examined by applying it on a standard radial 9-node distribution network and a realistic 574-node distribution network.
Type of Material
Journal Article
Publisher
Institute of Electrical and Electronics Engineers
Journal
IEEE Systems Journal
Volume
6
Issue
2
Start Page
254
End Page
259
Copyright (Published Version)
2011 Institute of Electrical and Electronics Engineers
Subjects

Monte Carlo simulatio...

PV cells

Wind turbine

Active losses

Point estimate method...

DOI
10.1109/JSYST.2011.2162994
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Format

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Checksum (MD5)

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Owning collection
Electrical and Electronic Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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