|Author's Email Address
||This thesis had been viewed 5559 times. Download 21 times.|
|Type of Document
||Application of Artificial Intelligence and Game Theory for Bidding Analysis in Day-ahead Electricity Market|
|Date of Defense
||Artificial Intelligence Algorithm
Electricity Market Deregulation
||With the electricity market deregulation was introduced, several issues need to be addressed by power engineers. The major subjects are the market-based bidding strategies and the development of models for bid matching in electricity markets. In this context, this thesis is focused on the analysis of bidding strategy.|
Taguchi’s method is used to reduce the number of experiments for the bidding simulation and a game theory based payoff matrix is constructed. Based on the Nash equilibrium theory, the set of equilibrium mixed strategy can be calculated or approached by using the gene-type self-adaptation enhanced bee swarm optimization algorithm.
Aimed to improve the convergence, enhanced mathematical formulas from artificial bee colony are used in the algorithm. In addition, two mechanisms are implemented; the first one is based on the concept of genetic algorithm which makes better solution space to search the global optima solution without distortion, and the second mechanism is for adjusting the parameters itself to improve the performance.
||Ta-Peng Tsao - chair|
Chih-Ming Hung - co-chair
Ming-Tang Tsai - co-chair
Whei-Min Lin - advisor
Indicate in-campus at 3 year and off-campus access at 3 year.|
|Date of Submission