An Agent-based Electricity Market Simulator
Spring 2016, Volume7 - Number1 علمی-پژوهشی (وزارت علوم)/ISC (12 صفحه - از 23 تا 34)
In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of themarket clearing price. In this paper, a new market simulator is introduced for a joint energy and spinningreserve market, in which market participants’ learning process is modeled using Q-learning algorithm.The main feature of this simulator is simulating a real market, in which market participants makedecisions based on incomplete information of the market. Using the proposed simulator, the clearingprice for each submarket is computed considering the participants’ behavior, under different load levelsand/or contingency conditions. The results show that Q-learning approach can modify the agent’s strategyunder different market situations.
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