Abstract: With the increasing popularity of complementary energy storage power generation system, its coverage is also expanding. But the capacity optimization technology is still in the single energy stage. Therefore, a capacity optimization algorithm is designed for the solar and wind energy complementary storage and power generation system. By analyzing the power output of the power generation system, the state of annual output and supply is obtained, and the capacity optimization objectives and constraints are established according to the power balance criterion. Then, the differential evolution algorithm is used for mutation, crossover, selection and other operations, and the particle swarm optimization algorithm is used to update the orientation and velocity. The current optimal solution of parent body and population is obtained by the combination of the two, and the capacity optimization of energy storage system is realized through iterative cycle. In the experimental, through the data of component configuration quantity and load power shortage rate it is found that this algorithm greatly improves the economic benefits, and ensures the reliability of power supply, which shows that the algorithm has significant optimization advantages and great application potential.

Key words: solar energy； , wind energy； , capacity optimization； , differential evolution algorithm； , particle swarm optimization； , complementary energy storage and power generation system