Research On Hybrid Energy Storage System For Stabilizing Wind Power Output Fluctuation

With more and more wind turbines put into use,the irregularity of wind speed fluctuation makes the controllability of wind power output worse,and produces a huge amount of waste air volume,which restricts the development of wind power industry.In order to solve this problem,the hybrid energy storage device is introduced into the wind farm;at the same time,in order to improve the overall economy of the system,the capacity of the hybrid energy storage system is further optimized.The main research contents are as follows:Firstly,a hybrid energy storage system is introduced into the wind farm,which is composed of lead-acid battery and super capacitor.The structure of the hybrid energy storage system is studied and its connection with the power system is determined.At the same time,the DC/DC power converter used is selected as the full bridge structure.Then,this paper analyzes and studies the unit combination scheduling problem of wind storage fire hybrid system,and establishes the optimal scheduling model of minimum total waste air volume without hybrid energy storage system and with hybrid energy storage system with a scheduling cycle of 24 hours.At the same time,heuristic search is used to optimize the start and stop sequence of thermal power units,and the improved particle swarm optimization algorithm is used to allocate the power of units.The simulation results show that the introduction of hybrid energy storage device can not only reduce the generation of waste air volume,but also reduce the total power generation cost of thermal power units.Finally,the capacity of the hybrid energy storage system participating in the wind light complementary power generation system is configured,aiming at the minimum life cycle cost,and three particle swarm optimization algorithms are used to solve it at the same time.The simulation results show that this model can reasonably configure the capacity of the hybrid energy storage system,and the minimum life cycle cost of the two improved algorithms is smaller than that of the basic particle swarm optimization algorithm.