Optimized Operation of Distributed Photovoltaic Energy Storage

This paper takes a certain enterprise in the park as the research object, collects its historical load data as well as the parameters of related PV and energy storage equipment, and aims to explore the economic superiority of distributed PV energy storage system. The study first utilizes the neural network method to forecast the load data. By constructing a load prediction model based on long short-term memory (LSTM) neural network, the temporal characteristics of the load data can be effectively captured, so as to realize the accurate prediction of typical daily load. On this basis, an empirical computational formula is used to physically model the PV system, and the corresponding PV output prediction is obtained by combining the historical meteorological data with the physical characteristics of the PV modules. Further, with the help of the optimization procedure of the programming software, the load prediction results are combined with the PV output prediction to realize the economically optimized operation on a typical day. By optimizing the charging and discharging strategies of the PV energy storage system, the system operating cost is reduced and the energy utilization efficiency is improved. The results of the study show that distributed PV energy storage systems have significant economic superiority at the level of engineering practice, and can effectively reduce the cost of electricity for enterprises, while providing flexibility support for grid operation.