Currently

carbon emission trading markets and green certificate trading markets have emerged as crucial mechanisms to promote carbon emission reduction in the power sector and incentivize renewable energy integration. These markets are closely coupled with electricity markets

forming a complex multi-market environment. Under this coupled market environment

how to analyze the bidding behavior of various market entities and the comprehensive operational market efficiency is an important research topic. Accordingly

this paper proposes a multi-agent deep reinforcement learning-based evolutionary game bidding model for multiple entities in coupled markets. First

a transaction model for the electricity-carbon-green certificate coupled market is constructed

accounting for the characteristics of each market. Second

in order to simulate the strategy learning and dynamic adjustment process of multiple market entities

a multiple entities evolutionary game bidding model in the electricity-carbon-green certificate coupled market is established

and a multi-agent cross-entropy twin delayed deep deterministic policy gradient algorithm is adopted to efficiently solve the model. Finally

case studies are conducted to analyze the impact of coupled market factors on bidding strategies and equilibrium outcomes

validating the effectiveness of the proposed model and the superiority of the algorithm.