Energy routing strategy of cold and heat networks in integrated energy stations

With the access of large-scale renewable energy sources and distributed power sources, the importance of building an energy internet with internet technology as the core to optimally coordinate and control the distributed renewable energy infrastructure settings through an energy management system in order to optimally complement multiple energy sources, such as electricity, cooling, heat and water, continues to be highlighted. This paper proposes an energy routing strategy based on the interoperability of the cold and heat pipeline networks of distributed energy stations, firstly using a neural network method to make short-term predictions of the cooling and heating loads, and carrying out optimal scheduling before the integrated energy station day according to the prediction results; then carrying out a heat and pressure balance analysis of the cooling and heating system, adding a pipeline network loss constraint on the basis of the scheduling model; finally using a message queue telemetry transmission communication Finally, a message queue telemetry communication protocol is used to achieve efficient information security transfer and ensure smooth internal calls and external access to the edge server.