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  5. Taxonomy of Uncertainty Modeling Techniques in Renewable Energy System Studies
 
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Taxonomy of Uncertainty Modeling Techniques in Renewable Energy System Studies

Author(s)
Soroudi, Alireza  
Uri
http://hdl.handle.net/10197/11238
Date Issued
2014-01-28
Date Available
2019-12-10T10:13:41Z
Abstract
With the introduction of new concepts in operation and planning of power systems, decision making is becoming more critical than ever before. These concepts include restructuring, smart grids, and the importance of environmental concerns. The art of decision making is defined as choosing the best action among available choices considering the constraints and input data of the problem. Decision making is usually a complex task which becomes more sophisticated when the input data of the problem are subject to uncertainty. This chapter presents a critical review of the state-of-the-art uncertainty in handling tools for renewable energy studies. Different uncertainty modeling tools are first introduced and then the appropriate ones for renewable energies are identified. Then, each method is implemented on a simple two-bus case study.
Type of Material
Book Chapter
Publisher
Springer
Series
Green Energy and Technology book series (GREEN)
Copyright (Published Version)
2014 Springer
Subjects

Uncertainty

Monte Carlo simulatio...

Point estimate method...

Scenario based uncert...

Scenario based uncert...

DOI
10.1007/978-981-4585-30-9_1
Language
English
Status of Item
Peer reviewed
Journal
Hossain, J., Mahmud, A. (eds.). Large Scale Renewable Power Generation: Advances in Technologies for Generation, Transmission and Storage
ISBN
978-981-4585-30-9
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

chapter1.docx

Size

4.58 MB

Format

Unknown

Checksum (MD5)

640a6139002afbf24d7a46b978e2592f

Owning collection
Electrical and Electronic Engineering Research Collection
Mapped collections
Climate Change 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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