With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

Shill bidding, the use of non-genuine bids to inflate prices, undermines auction market integrity. Exploiting China’s online judicial housing auctions as a laboratory, we identify 2% of participants as suspected shill bidders, affecting 8% of auctions. They raise price premium by 14.3%, causing an annual deadweight loss of ¥570 million for homebuyers. Mechanism analysis reveals they create bidding momentum and intensify competition. We establish causality using a difference-in-differences analysis leveraging a 2017 regulatory intervention and an instrumental variable approach using dishonest judgment debtors. These findings offer actionable insights for policymakers and auction platforms to combat fraud in online high-stake auctions.