Thermal power is an important support for the new power system. It is of great significance to study the bidding strategy for thermal power generation companies and the influence of different clearing mechanisms to ensure their low-carbon and efficient operation. A bidding strategy model is constructed based on the multi-agent deep deterministic policy gradient algorithm to analyze the differential bidding strategies for different combinations of thermal power generation companies. The multi-agent price and quantity bidding strategy is optimized, and the market impact of different market clearing mechanisms is explored. The simulation results indicate that the proposed bidding strategy model can guide the thermal power generation companies to optimize their bidding methods and improve the market efficiency. When the penetration rate of new energy is low, the applicability of different clearing mechanisms varies for various types of units; with the increase of the penetration rate of new energy, the pay as bid mechanism can be used to enhance the economic and environmental efficiency of the electricity market; when the penetration rate of new energy reaches a high level, the random matching clearing mechanism can effectively address market volatility risks.