DocumentCode
834617
Title
Enhanced augmented Lagrangian Hopfield network for unit commitment
Author
Dieu, V.N. ; Ongsakul, W.
Author_Institution
Energy Field of Study, Asian Inst. of Technol., Pathumthani
Volume
153
Issue
6
fYear
2006
fDate
11/1/2006 12:00:00 AM
Firstpage
624
Lastpage
632
Abstract
An enhanced augmented Lagrangian Hopfield network (EALHN) for unit commitment (UC) is proposed. The EALHN is an augmented Lagrangian Hopfield network (ALHN) enhanced by unit classification to allow decommitment of excess spinning reserve units caused by the minimum up and down time constraints. First, the ALHN is used to solve the UC problem when neglecting the minimum up and down time constraints. Then, a heuristic-based algorithm is applied to satisfy the minimum up and down time constraints and decommit excess spinning reserve units. Finally, the economic dispatch problem is solved using an augmented Lagrangian-relaxation-based continuous Hopfield network (ALRHN). The EALHN is tested on several systems ranging from 10 to 100 units and compared to several methods. The total production costs from the proposed method are smaller those obtained using existing methods, especially for the large systems. Moreover, the computational times of the proposed method are also much faster than these for existing methods and slightly increase with the system size, which is very favourable for large-scale implementation
Keywords
Hopfield neural nets; power generation dispatch; power generation economics; power system analysis computing; Lagrangian-relaxation; economic dispatch problem; enhanced augmented Lagrangian Hopfield neural network; heuristic-based algorithm; production costs; spinning reserve unit; unit commitment;
fLanguage
English
Journal_Title
Generation, Transmission and Distribution, IEE Proceedings-
Publisher
iet
ISSN
1350-2360
Type
jour
DOI
10.1049/ip-gtd:20050460
Filename
4015884
Link To Document