Research on Dynamic Optimization Operation of Optical Storage Based on LSTM Load Forecasting

Abstract

Affected by weather, geography and other factors, PV output has fluctuations and intermittentness. It is not suitable to supply power to the load independently. It needs to be used together with the energy storage device. At the same time, the power load has a great influence on the energy storage configuration. Therefore, accurate load forecasting has Helps improve the stability and reliability of the power system, saves electricity costs, and facilitates dynamic optimization of optical storage. In this regard, this paper proposes a dynamic optimization strategy for optical storage based on long-term and short-term memory networks. Firstly, the long-short-term memory neural network method is used to predict the power load. Secondly, according to the power difference adjustment and the photovoltaic output result, the daily-time load is divided into four sections, and the different sections respectively correspond to specific control strategies. Finally, according to each, the interval control strategy optimizes the energy storage and discharge power in real time. The method stores excess photovoltaic energy and releases it in the form of electric energy when the user's power grid needs it. This can effectively suppress the power fluctuation of photovoltaic power generation, improve the utilization rate of photovoltaics, and perform peak clipping for the user grid Valley regulation.