Abstract: As the installed capacity of new energy gradually increases and China's market-oriented reform matures, new energy sources such as wind power will gradually participate in the electricity spot market through quantity bidding. In response to the issue that the uncertainty of new energy output may bring bias penalties to its market behavior, this paper proposes a wind power day ahead step curve pricing strategy that considers prediction errors. Firstly, based on the centralized electricity market, a wind power tiered pricing strategy considering prediction errors is proposed according to the probability density distribution of prediction errors. Secondly, based on probability distribution, prediction error scenarios were extracted, and a double-layer bidding model for wind power participation in the day ahead market was established. The upper layer is the bidding model that maximizes the profit of wind farm stations participating in the day-ahead market, and the lower layer is the day-ahead market clearing model that minimizes electricity purchase costs. Finally, a combination of the lemur algorithm and programming solution was used to efficiently solve the two-layer model. The case analysis showed that the proposed quotation method could alleviate the risks brought by uncertainty, reduce deviation penalties, improve profits, and provide certain support for the system's regulatory ability.