Research on the Optimal Allocation Method of Hybrid Energy Storage Capacity Based on NGO-Enhanced VMD

With the increasing penetration of wind power, the adverse effects of wind power fluctuations on the grid are becoming more and more apparent. How to reduce the wind power prediction error and reduce the wind abandonment rate has become one of the research hotspots. In this paper, a hybrid supercapacitor-battery energy storage system is used to balance the deviation between wind power and planned wind farm output. An optimum hybrid energy storage power allocation strategy supported Northern hawk Optimization-enhanced Variational Mode Decomposition (NGO-enhanced VMD) rule is planned. Firstly, the Northern Goshawk algorithm is used to optimize the number of VMD parameter modes $K$ and the quadratic penalty term $\alpha$ . Then, the constructed NGO-enhanced VMD model is used to decompose the wind power imbalance into high-frequency and low-frequency components, and the super capacitor and battery are used to level off the high-frequency and low-frequency components respectively. Last but not least, a hybrid energy storage system is created taking into account the investment cost and actual power output data from wind farms, and the calculation results confirm the effectiveness of the suggested strategy.