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Fast and accurate solution for the SCUC problem in large-scale power systems using adapted binary programming and enhanced dual neural network

Title
Fast and accurate solution for the SCUC problem in large-scale power systems using adapted binary programming and enhanced dual neural network
Type
Article in International Scientific Journal
Year
2014
Authors
Shafie khah, M
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Moghaddam, MP
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Sheikh El Eslami, MK
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Journal
Vol. 78
Pages: 477-485
ISSN: 0196-8904
Publisher: Elsevier
Other information
Authenticus ID: P-008-JNX
Abstract (EN): This paper presents a novel hybrid method for solving the security constrained unit commitment (SCUC) problem. The proposed formulation requires much less computation time in comparison with other methods while assuring the accuracy of the results. Furthermore, the framework provided here allows including an accurate description of warmth-dependent startup costs, valve point effects, multiple fuel costs, forbidden zones of operation, and AC load flow bounds. To solve the nonconvex problem, an adapted binary programming method and enhanced dual neural network model are utilized as optimization tools, and a procedure for AC power flow modeling is developed for including contingency/security issues, as new contributions to earlier studies. Unlike classical SCUC methods, the proposed method allows to simultaneously solve the unit commitment problem and comply with the network limits. In addition to conventional test systems, a real-world large-scale power system with 493 units has been used to fully validate the effectiveness of the novel hybrid method proposed.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 9
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