Optimal Configuration of Hybrid Energy Storage Capacity Based on Improved Compression Factor Particle Swarm Optimization Algorithm

Wind power generation and solar thermal power generation are unstable and intermittent. The use of energy storage devices can suppress the power fluctuations caused by wind and solar power generation. In order to improve the economy of wind power-photothermal combined power generation energy storage system, the capacity configuration model of energy storage system is studied. Firstly, lithium battery and flywheel are used as energy storage devices of power generation system. The capacity optimization configuration model of hybrid energy storage system is established with the whole life cycle cost model as the objective function and the system load power shortage rate, lithium battery characteristics and flywheel energy storage characteristics as constraints. Secondly, based on the dynamic changes of inertia factor and acceleration factor, the compression factor is introduced to improve the particle swarm optimization algorithm, and the simulation is solved on Matlab. The results show that the improved compression factor particle swarm optimization algorithm has faster convergence speed and lower system cost.