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  5. Information Gap Decision Theory based OPF with HVDC Connected Wind Farms
 
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Information Gap Decision Theory based OPF with HVDC Connected Wind Farms

Author(s)
Rabiee, Abbas  
Soroudi, Alireza  
Keane, Andrew  
Uri
http://hdl.handle.net/10197/6267
Date Issued
2014-12
Date Available
2014-12-22T10:23:41Z
Abstract
A method for solving the optimal power flow (OPF) problem including HVDC connected offshore wind farms is presented in this paper. Different factors have been considered in the proposed method, namely, voltage source converter (VSC-HVDC) and line-commutated converter high-voltage DC (LCC-HVDC) link constraints, doubly fed induction generators' (DFIGs) capability curve as well as the uncertainties of wind power generation. Information gap decision theory (IGDT) is utilized for handling the uncertainties associated with the volatility of wind power generation. It is computationally efficient and does not require the probability density function of wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the considered uncertainties. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus system. The obtained results validate the applicability of the proposed IGDT-based OPF model for optimal operation of AC/DC power systems with high penetration of offshore wind farms.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Institute of Electrical and Electronics Engineers
Journal
IEEE Transactions on Power Systems
Volume
PP
Issue
99
Start Page
1
End Page
11
Subjects

Wind turbine

Uncertainty modeling

Energy systems

Decision making under...

Risk modeling

DOI
10.1109/TPWRS.2014.2377201
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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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/.
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