Research on the Application of Machine Learning in the Construction of Power Demand Response Model for Edge Computing

With the large quantity of access to distributed power sources and flexible loads, there will be many stakeholders in the traditional EPS, such as power grid companies, owners of distributed power sources and flexible loads. Edge computing technology can collect all kinds of state information inside the Internet of Things (IoT) in real time and can realize real-time monitoring and control of the power grid by using its own computing, storage and network communication capabilities and the interaction between nodes. In this article, the application of machine learning in edge calculation of power demand response is studied, and an optimal power dispatching model considering dynamic incentive compensation and expected outage loss is established to coordinate and solve the problem of unit commitment on the generation side and the interaction between supply and demand on the demand side, so as to realize the economy and safety of power grid operation. The simulation results show that the proposed power optimal dispatching model considering demand response and expected outage loss can reduce the operating cost of power grid and realize the economic and safe operation of Electric power system (EPS) in market environment under the premise of ensuring a certain level of reliability.