Abstract
BackgroundAccording to sustainable development goals (SDGs), societies should have access to affordable, reliable, and sustainable energy. Deregulated electricity markets have been established to provide affordable electricity for end-users through advertising competition. Although these liberalized markets are expected to serve this purpose, they are far from perfect and are prone to threats, such as collusion. Tacit collusion is a condition, in which power generating companies (GenCos) disrupt the competition by exploiting their market power. MethodsIn this manuscript, a novel deep Q-network (DQN) model is developed, which GenCos can use to determine the bidding strategies to maximize average long-term payoffs using available information. In the presence of collusive equilibria, the results are compared with a conventional Q-learning model that solely relies on past outcomes. With that, this manuscript aims to investigate the impact of emerging DQN models on the establishment of collusive equilibrium in markets with repetitive interactions among players. Results and ConclusionsThe outcomes show that GenCos may be able to collude unintentionally while trying to ameliorate long-term profits. Collusive strategies can lead to exorbitant electric bills for end-users, which is one of the influential factors in energy poverty. Thus, policymakers and market designers should be vigilant regarding the combined effect of information disclosure and autonomous pricing, as new models exploit information more effectively.
Renewable Energy Bidding Strategies Using Multiagent Q-Learning in Double-Sided Auctions