<p>Wind-solar hybrid power generation systems are widely used in areas rich in wind and solar energy. However, because of the instability, intermittent and volatile of wind and light, it is hoped to install energy storage system to guarantee its stable operation. Battery energy storage (BES) has short cycle life, complex maintenance, and long power response time, while superconducting magnetic energy storage (SMES) has the features of high conversion efficiency, fast speed of response, and long service life. Thus, combining SMES with battery energy storage could enhance the load adaptability and reliability of power supply in energy storage systems. In this study, a composite energy storage capacity configuration model is built with the objective of minimizing life cycle cost and solved using improved quantum genetic algorithm. Comparing with the traditional particle swarm optimization arithmetic (PSO) and improved PSO algorithm, the method used in this paper has the lowest configuration cost, lower voltage node fluctuations and requires fewer iterations. The validity of the puts forward approach has been proved in example analysis.</p>