Due to the uncertain characteristics of wind power generation, wind power stations are faced with the challenge of energy balance and output fluctuation when they are online. Aiming at the differential characteristics of wind power and error distribution in different regions, a wind storage day bidding optimization model based on kernel density and Copula function is proposed. Firstly, the kernel density estimation method is used to calculate the probability density function of the scenic power, and the Archimedes cluster Copula function is introduced to solve the joint distribution function of the scenic power. Then Monte Carlo sampling and K-means clustering method are used to generate the typical output scene. Finally, a pre-day bidding optimization model of wind power station equipped with energy storage considering energy storage peak-valley arbitrage is established. The results show that the proposed model improves the accuracy of describing the output characteristics of the wind-solar power station, realizes a better Internet access and energy storage strategy, verifies the effectiveness of increasing revenue and improving accuracy, and enables the wind-solar power plant cluster to better cope with the output fluctuation problem and improve revenue.