Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISAP.2007.4441641
Title: Evolving buyer's bidding strategies using game-theoretic co-evolutionary algorithm
Authors: Srinivasan, D. 
Chen, K.T. 
Wu, C.
Ah, C.L. 
Issue Date: 2007
Source: Srinivasan, D.,Chen, K.T.,Wu, C.,Ah, C.L. (2007). Evolving buyer's bidding strategies using game-theoretic co-evolutionary algorithm. 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ISAP.2007.4441641
Abstract: This paper presents a co-evolutionary algorithm for evolving bidding strategies for buyers in a reconstructed pool-type electrical power market. A demand-driven algorithm which aims to closely follow the individual demands while maintaining low locational marginal price has been implemented and analyzed in detail based on simulation results under different market scenarios. The algorithm has been tested on a simulated power market with 7 buyers and 20 sellers in IEEE 14 bus network. The simulation results suggest that the proposed demand-driven co-evolutionary algorithm is an effective learning algorithm which helps the buyers optimize their bidding strategy. A novel hybrid algorithm which combines the demand-driven algorithm with a game-like decision making process has also been implemented to improve the performance of this algorithm.
Source Title: 2007 International Conference on Intelligent Systems Applications to Power Systems, ISAP
URI: http://scholarbank.nus.edu.sg/handle/10635/70225
ISBN: 9860130868
DOI: 10.1109/ISAP.2007.4441641
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 13, 2017

Page view(s)

20
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.