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Energy storage technology is one of the effective means to promote the consumption of new energy. It has the advantages of improving the flexibility and stability of power grid. Energy storage plays an important role in improving the peaking and valley filling function of the load side of the power grid. Based on the two-stage topology of the energy storage system, this paper establishes the mirror model of the practical application engineering of the energy storage system, and uses the data-driven method to establish the energy storage battery model. On this basis, the multi-objective control strategy is adopted for the peak regulating power of the energy storage system and the load state balance of the battery. The support vector machine algorithm is used to predict the daily load data of the power grid, and the constant power algorithm is proposed to control the battery control node signal. Finally, taking the battery compartment of the energy storage system as the simulation object, the effectiveness of the proposed control strategy is verified, which provides a theoretical basis for the topic research.

The rational configuration of distributed energy storage can not only realize p peak-shaving and valley-filling during distribution network operation， but also play an important role in improving the consumption of renewable energy and ensuring the safe， reliable， and economic operation of the power grid. In view of the user-side friendly interaction of distributed energy storage， this paper studies the hierarchical optimal configuration of distributed energy storage in distribution networks with a high proportion of PV. In the upper layer， multi-objective models of investment and operation cost and maximized consumption of distributed PV are established from the perspective of both economy and environmental protection. In the lower layer， the friendly interaction of distributed energy storage devices on peak shaving and valley filling of distribution networks is considered， and the daily net load deviation per unit tie-line power is established. For the established non-linear， multi-objective two-layer optimization model， a weight method is used to convert the multi-objective model in the upper layer into a single-objective one.Moreover， an improved genetic algorithm is used for the optimal solution. The simulation is validated by an improved IEEE 33-node example. The results show that the established optimization model can reflect the investment benefits of energy storage and the impact on new energy consumption. The users’ friendly interaction impacts both distributed energy storage configuration and PV consumption in varying degrees.

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