A machine-learning-based approach for adaptive integration of virtual power plant resources

Virtual power plants integrate various distributed resources, such as energy storage systems, distributed power sources, adjustable loads, etc., to form a unified power system. However, how to efficiently utilize these resources and reduce operating costs is an important issue facing virtual power plants. Therefore, a machine learning based adaptive integration method for virtual power plant resources is proposed. That is to say, machine learning is used to design the adaptive integration identification program of virtual power plant resources, generate the adaptive integration data dictionary of virtual power plant resources, and thus complete the adaptive integration of virtual power plant resources. The experimental results show that the designed adaptive integration method of virtual power plant resources has good integration indicators, reliability and certain application value, and has made certain contributions to realizing resource sharing and improving the operation and maintenance efficiency of virtual power plants.