A Q-Learning-Based Supplier Bidding Strategy in Electricity Auction Market

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 one 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 and all suppliers winning the market are paid based on their own bid prices. The dynamics and the incomplete information of the market are considered. The impact of suppliers’ strategic bidding on the market price is analyzed. Agent-based simulations are presented. The simulation results show the feasibility of the proposed bidding strategy.