Abstract: In electrochemical energy storage systems, traditional power allocation strategies have problems such as poor adaptability and insufficient real-time performance. Therefore, this paper designs a electrochemical energy storage power allocation strategy that integrates improved pelican optimization algorithm and variational mode decomposition. This article introduces adaptive weight factors and dynamic learning factors into the traditional pelican algorithm, enhancing the algorithm’s global exploration and local development capabilities. At the same time, the variational mode decomposition method is used to adaptively decompose the load curve, achieving accurate capture of load characteristics at different time scales and providing refined demand forecasting support for power allocation. The experimental results show that the strategy proposed in this article reduces the cost of electricity purchase for users, delays the degradation rate of batteries, and lays an important foundation for the intelligent operation and economic optimization of electrochemical energy storage.