Research on Control Strategy of Energy Storage System Based on Day-ahead Energy Prediction

Abstract

As the number of new energy vehicles continues to rise, the demand for charging facilities is also increasing. The new energy production and consumption system represented by "optical storage and charging" will play an important role in achieving carbon neutrality. However, "optical storage and charging" integrated charging stations generally have problems such as poor photovoltaic energy absorption capacity, negative impact of load characteristics on the power grid, and low economic benefits. In view of the above phenomena, this paper takes the integrated charging station of "optical storage and charging" as the research object, and proposes a control strategy of energy storage system based on day-ahead energy prediction, Firstly, the day-ahead PV generation curve and EV charging curve are predicted, and then the model is modeled with the goal of minimizing the all-day electricity purchase cost and reducing the peak load as the constraint condition. The optimal solution is obtained by using the linear programming algorithm, and the feasibility and effectiveness of the strategy are verified by an example in the MATLAB simulation environment.