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