Artificial Intelligence-Driven Virtual Power Plant Resource Scheduling and Electricity Trading

With the widespread popularization of photovoltaic power generation, electric vehicles, new energy charging stations, etc., the load profile of the power system has become more complex, and at the same time, it also puts forward higher requirements for the management and scheduling of the power system. This study establishes a virtual power plant architecture based on artificial intelligence technology, with a three-layer architecture capable of intelligently integrating the resources of all parties and accurately distributing the interests of all parties; it designs the application process of artificial intelligence technology in key aspects such as aggregation management, resource scheduling and power trading, and it designs an intelligent resource scheduling process and an intelligent trade settlement process based on reinforcement learning and supervised learning. Through the application of artificial intelligence technology, it is possible to quickly schedule the demand response in order to realize the intelligent demand response of the virtual power plant. Through the application of Artificial Intelligence-Agent, utility-based learning intelligent body and active reinforcement learning-based intelligent body are constructed to intelligently match the supply and demand sides and realize the intelligent scheduling of virtual power plant.