Developing Electricity Bidding Strategies using Evolutionary Algorithms and Game Theory

J. Yin and Y. Li (UK)


Oligopolistic power markets, Bidding Strategy, Artificial Intelligence, Evolutionary Algorithms, GameTheory.


This paper presents a new “intelligent” methodology to search for optimal trading strategies in the electrical power market under oligopoly conditions. In this research, an auction model to mimic real world power markets has been developed. An improved Game Theory method, “trigger price strategy”, is developed as power suppliers’ basic trading strategy. Genetic Algorithms are utilized to enhance the strategy and to optimize bidding parameters. Results show that the hybrid Game Theory and Evolutionary Computing is a powerful technique for searching optimal trading strategies in oligopolistic power markets.

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