A Supplier Bidding Strategy Through Q-Learning Algorithm in Electricity Auction Markets

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 the long run. In this approach, the supplier bidding strategy is viewed as a kind of stochastic optimal control problem and each supplier can learn from experience. A competitive day-ahead electricity auction market with hourly bids is assumed here, where no supplier possesses the market power. The dynamics and the incomplete information of the market are considered. The impacts of suppliers’ strategic bidding on the market price are analyzed under uniform pricing rule and discriminatory pricing rule. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.