The random nature of wind energy is an important reason for the low energy utilization rate of wind farms. The use of a compressed air energy storage system (CAES) can help reduce the random characteristics of wind power generation while also increasing the utilization rate of wind energy. However, the unreasonable capacity allocation of the CAES system results in high capital investment and a long payback period. In order to improve the economic benefits of energy storage, this paper studies the capacity configuration of compressed air energy storage systems under the condition of wind energy uncertainty. First, the typical hourly power distribution of wind power generation was obtained using historical data. Factors such as user load demand, time-of-use price of the power grid, system investment cost, power shortage cost, and power sales revenue were considered. Then, a model was built with the charging and discharging power and gas storage capacity of the CAES system as constraints, and the maximum return on investment and the minimum volume of the gas storage tank as targets. NSGA-II and TOPSIS optimal selection methods were used to solve the problem. Finally, the model was used to optimize a power operation case. The results show that in the case of an hourly load power demand of a factory using 3.2 MW, a wind farm would need to keep four wind turbines running every day, and a compressed air energy storage system with a rated power of 1 MW and a rated capacity of 7 MW would ensure the best project benefit. In this mode, 1.24 × 103 MWh of wind abandoning power could be reduced annually, 2.6 × 104 kg of carbon emissions could be reduced by increasing energy storage within the operation cycle, and the payback period of investment would only be 4.8 years.