DocumentCode :
1485309
Title :
Bidding strategy based on artificial intelligence for a competitive electric market
Author :
Hong, Y.-Y. ; Tsai, S.-W. ; Weng, M.T.
Author_Institution :
Dept. of Electr. Eng., Chung Yuan Christian Univ., Chung Li, Taiwan
Volume :
148
Issue :
2
fYear :
2001
fDate :
3/1/2001 12:00:00 AM
Firstpage :
159
Lastpage :
164
Abstract :
A bidding strategy using a fuzzy-c-mean (FCM) algorithm and the artificial neural network (ANN) was developed for competitive electric markets. The nodal price information was assumed to be released into the market. The FCM was used, first, to classify the daily load pattern into peak, medium-peak and off-peak levels and, secondly, to classify the competitive generation companies (gencos) into less-menacing, possible-menacing and menacing gencos. The back-propagation ANN was used for determining the bidding price for a genco. The FCM results aided in lessening the training data and reducing the ANN input nodes. The IEEE 30-busbar system was used for illustrating the applicability of the proposed method
Keywords :
backpropagation; costing; electricity supply industry; fuzzy set theory; neural nets; power system analysis computing; power system economics; ANN input nodes reduction; IEEE 30-busbar system; artificial intelligence; artificial neural network; back-propagation ANN; bidding price determination; bidding strategy; competitive electric market; competitive generation companies; daily load pattern; fuzzy-c-mean algorithm; medium-peak levels; nodal price information; off-peak levels; training data reduction;
fLanguage :
English
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
Publisher :
iet
ISSN :
1350-2360
Type :
jour
DOI :
10.1049/ip-gtd:20010124
Filename :
920895
Link To Document :
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