A genetic algorithm based method for deregulated electricity market dispatch

Wong, Kinn Onn, Dong, Zhao Yang and Saha, T. K. (2006). A genetic algorithm based method for deregulated electricity market dispatch. In: Proceedings of the 7th International Conference on Advances in Power System Control, Operation and Management. 7th International Conference on Advances in Power System Control, Operation and Management, Hong Kong, (). 30 October - 2 November, 2006. doi:10.1049/cp:20062244


Author Wong, Kinn Onn
Dong, Zhao Yang
Saha, T. K.
Title of paper A genetic algorithm based method for deregulated electricity market dispatch
Conference name 7th International Conference on Advances in Power System Control, Operation and Management
Conference location Hong Kong
Conference dates 30 October - 2 November, 2006
Proceedings title Proceedings of the 7th International Conference on Advances in Power System Control, Operation and Management
Place of Publication Hong Kong
Publisher The Institution of Engineering and Technology Hong Kong
Publication Year 2006
Sub-type Fully published paper
DOI 10.1049/cp:20062244
ISBN 0863412467
9780863412462
Volume 523 CP
Total pages 7
Collection year 2006
Abstract/Summary In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.
Subjects E1
290901 Electrical Engineering
660304 Energy systems analysis
Q-Index Code E1
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Access Statistics: 76 Abstract Views  -  Detailed Statistics
Created: Thu, 23 Aug 2007, 22:11:50 EST