ENERGY STORAGE OPTIMIZATION OF POWER GRID COMBINING EMPIRICAL MODE DECOMPOSITION AND FUZZY CONTROL

To enhance the stability and system economy of wind power grid connection, this study proposes a power allocation strategy for grid energy storage that combines empirical mode decomposition and fuzzy control.This strategy improves the accuracy of empirical mode decomposition by introducing adaptive noise, extracts signal time-frequency characteristics using Hilbert transform, and designs a fuzzy controller to dynamically adjust the SOC of energy storage devices.On this basis, a comprehensive consideration of operation and maintenance costs and battery life loss is taken into account to construct an energy storage optimization model for the power grid.The experiment showed that the power allocation method combining empirical mode decomposition and fuzzy control increased the fluctuation shift rate by 17.86%.The total cost of the energy storage optimization model has been reduced by 14.26% compared to other optimization models, and the wind power prediction error, response time, and charge discharge loss rate of this model have been reduced by 26.42%, 7.34%, and 13.59%.Meanwhile, the energy utilization rate and equipment lifespan improvement rate of this model were 94.45% and 26.51%, showing significant advantages.This indicates that the optimization strategy can effectively suppress power fluctuations, achieve multi-objective optimization, enhance the adaptability and economy of energy storage systems, and provide more stable support for wind and solar power grid integration.