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  5. Information gap decision theory approach to deal with wind power uncertainty in unit commitment
 
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Information gap decision theory approach to deal with wind power uncertainty in unit commitment

Author(s)
Soroudi, Alireza  
Rabiee, Abbas  
Keane, Andrew  
Uri
http://hdl.handle.net/10197/8285
Date Issued
2017-04
Embargo end date
2019-04-01
Abstract
The renewable energy sources (RES) integration in the electricity supply utilities can reduce the energy procurement costs as well as the environmental concerns. Wind power is the most popular form of RES which is vastly utilized worldwide. This paper proposes a robust model for unit commitment (UC) problem, minimizing the operating costs considering uncertainty of wind power generation. In order to handle the uncertainties arising from volatile nature of wind power, information gap decision theory (IGDT) is utilized, where risk averse (RA) and opportunity seeker (OS) strategies are developed. RA strategy gives a robust decision making tool for handling the severe uncertainty of wind power, whereas the OS strategy makes benefit of possible uncertainties by adjusting the decision variables in a right way. Besides, the impact of demand flexibility (or demand response) on the operation costs is also investigated. The proposed model is examined on the IEEE 118-bus test system, and its benefits over the existing stochastic programming technique is examined. The obtained results demonstrate the applicability of the proposed method to deal with the UC problem with uncertain wind power generation. It is also observed that demand flexibility has positive impacts in both RA and OS strategies.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Elsevier
Journal
Electric Power Systems Research
Volume
145
Start Page
137
End Page
148
Copyright (Published Version)
2017 Elsevier
Subjects

Power system

Wind

Uncertainty

Decision making

Risk analysis

Demand response

Transmission network

DOI
10.1016/j.epsr.2017.01.001
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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EPSR_-_R2.pdf

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5051c31c2f48a1c2e2f70540b5c351ce

Owning collection
Electrical and Electronic Engineering Research Collection
Mapped collections
Energy Institute 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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