The intermittency, volatility, and anti-peak shaving characteristics of wind power cause a large amount of waste of wind power, which affects its economic and environmental benefits. In view of the different characteristics of wind power, pumped storage and thermal power output, the internal and external two-layer model is used to solve the problem. A dual-objective model with the largest internal wind-storage combined operation income and the smallest fluctuation of wind power is established firslyt to determine the pumping power or generate power of the pumped-storage unit. Then, an outer target model that takes into account the wind power forecast errors of different confidence levels is bulit to maximize the combined benefits of wind-storage. Secondly, the cooperative operation of pumped storage and wind power is used to deal with the uncertainty of wind power. Then the chance-constrained programming is used to deal with the random variables in the model. Finally, the particle swarm optimization-genetic algorithm (PSO-GA) hybrid optimization algorithm is used to solve the model. The IEEE 30-bus system verifies that the model increases the economic benefits of the system and reduces the volatility of wind power output.