Title page for etd-0522116-154340


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URN etd-0522116-154340
Author Cheng-yu Tsai
Author's Email Address No Public.
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Department Electrical Engineering
Year 2015
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Application of Artificial Intelligence and Game Theory for Bidding Analysis in Day-ahead Electricity Market
Date of Defense 2016-06-16
Page Count 104
Keyword
  • Artificial Intelligence Algorithm
  • Bidding Strategy
  • Electricity Market Deregulation
  • Game Theory
  • Taguchi’s Method
  • Abstract 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.
    Advisory Committee
  • Ta-Peng Tsao - chair
  • Chih-Ming Hung - co-chair
  • Ming-Tang Tsai - co-chair
  • Whei-Min Lin - advisor
  • Files
  • etd-0522116-154340.pdf
  • Indicate in-campus at 3 year and off-campus access at 3 year.
    Date of Submission 2016-06-28

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