A game-theoretic approach to understand transaction mode selection in electric markets: an evolutionary multi-agent artificial intelligent based algorithm

Since the implementation of the power system reform, the electricity market encouraged the establishment of a long-term and stable trading mechanism. In order to describe the trading behavior of micro agents in the power market, this paper used the simple reflection agent in artificial intelligence to simulate the market behavior of power plants and power users. Specifically, we analyzed the influence of the modes selection behavior of micro agents on power trading results. By comparing the equilibrium results of different market supply and demand relations, we obtained the conditions for improving power plants capacity utilization and market efficiency. The results show that the artificial intelligence agents can significantly improve the market efficiency in certain conditions.