An electricity supplier bidding strategy through Q-Learning

One of the most important issues for power suppliers in the deregulated electric industry is how to bid into the electricity auction market to satisfy their profit-maximizing goals. Based on the Q-Learning algorithm, this paper presents a novel supplier bidding strategy to maximize supplier's profit in a long term. A perfectly competitive day-ahead electricity auction market, where no supplier possess the market power and all suppliers winning the market are paid on their own bids, is assumed here. The dynamics and the incomplete information of the market are emphasized. The impact of suppliers' strategic biddings on the market price is analyzed. Agent-based simulations are presented in this paper. The simulation results show the feasibility of the proposed bidding strategy.