Wind and solar power stations face challenges in maintaining energy balance and managing output fluctuations when connected to the grid due to their uncertain nature.Here,an optimization model for day-ahead bidding of wind-solar-storage systems based on kernel density estimation and Copula functions is proposed in accordance with the varying wind and solar output characteristics and error distributions across different regions.Firstly,the kernel density estimation method is used to calculate the probability density functions of wind and solar power outputs,and the Archimedean Copula function is introduced to solve their joint distribution function.Then,Monte Carlo sampling and K-means clustering methods are used to generate typical output scenarios.Finally,the optimization model is established,considering peak-valley arbitrage of energy storage.The results show that the proposed model improves the accuracy of describing the output characteristics of wind-solar power stations,realizes a better grid connection and energy storage strategies,and verifies the effectiveness of increasing revenue and improving accuracy,enabling the wind-solar power plant clusters to better cope with output fluctuation and improve their earnings.