Research on Dynamic Intelligent Dispatching Model of Distributed Photovoltaic Energy Storage System Considering Volatility

This project aims at the coordinated optimization of photovoltaic and storage, and establishes a dynamic intelligent scheduling model for distributed photovoltaic energy storage systems that considers the impact of fluctuations, with the goal of improving energy utilization and grid stability. This project intends to combine machine learning and optimization algorithms to establish a data-driven dynamic scheduling model for photovoltaic power generation, to achieve real-time prediction of photovoltaic power and charge and discharge control of the energy storage system. The experimental results verify the robustness and efficiency of the model in complex environments, significantly reducing energy loss and improving the overall economic benefits of the system. This dynamic scheduling method not only enhances the stability of distributed photovoltaic energy storage systems, but also reduces dependence on traditional power grids and promotes the large-scale application of renewable energy. The research results of the project will provide new theoretical basis and technical support for the intelligent scheduling of distributed photovoltaic storage systems, and have great practical significance for promoting the efficient development of new energy and the sustainable development of power grids.