Study on Free Joint Bidding Mechanism in Multi-agent Environment Based on Two-stage Deep Reinforcement Learning Algorithm
In the early stages of power market development,the imperfect regulatory mechanism provides opportunities for power generators to secretly communicate and jointly bid.Detecting such collusive behaviors is challenging.In this paper,a bidding model considering the association among generators is established and a novel two-stage deep reinforcement learning algorithm is proposed to tackle the discrete-and-continuous decision problem of choosing collusion partners and bidding coefficients.The joint strategies of generators under different congestion scenarios are analyzed,and the effectiveness of the algorithm is validated in a large-scale case study.The simulation results demonstrate that the proposed method can effectively emulate the free association of generators and identify potential collusion.
two-stage deep reinforcement learningfree associationmulti-agent simulationjoint auction behaviors
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