A Deep Q Network Approach for Optimizing Offering Strategies in Electricity Markets

Bi-level optimization constitutes the most common mathematical methodology for modeling the decision-making process of strategic generation companies in deregulated electricity markets. However, previous models neglect the physical non-convex operating characteristics of generation units, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in the context of markets with complex biding and unit commitment clearing mechanisms. Aiming at addressing this fundamental limitation, this paper delves into deep reinforcement learning-based approaches by proposing a novel deep Q network (DQN) method and enabling explicit incorporation of these non-convexities into the bi-level optimization model. Case studies demonstrate the superior performance of the proposed method over the conventional Q-learning method in devising more profitable offering strategies.