Research On Information Transmission Optimization Technology For Smart Grid Supply And Demand Interaction Business

As more and more intelligent electrical terminals are connected to the electric power communication network and participate in the demand response interaction,a large number of demand response service data streams will be generated and network congestion and delay will be caused,which will make the transmission quality of communication deteriorate.In order to reduce the end-to-end transmission delay and delay jitter,reduce the packet loss rate in the process of data transmission,and effectively solve the problems such as link congestion and high transmission reliability in the process of demand response communication.In this paper,the optimization technology of information transmission for supply and demand interaction business of smart grid is studied.Firstly,the conceptual model of smart grid and the physical architecture model of demand response under smart grid are analyzed.The type and business of demand response are studied,and the business of demand response is divided into four levels.Several typical demand response information interaction scenarios are summarized.It provides the basic support for the optimization strategy that can improve the transmission quality of demand response communication and solve the problems of link congestion,packet loss and large delay in the process of demand response communication.Secondly,it analyzes the causes of queuing delay of data in the communication network during the process of demand response communication.In order to ensure the service quality of demand response business,it is necessary to analyze different demand response business data streams.An improved demand response scheduling algorithm based on dynamic weighted fair queue is proposed.In addition to ensuring the transmission quality of high-priority demand response services,low-priority services can be fairly scheduled.Reduce end-to-end transmission delay and delay jitter,and reduce the packet loss rate during data transmission.Finally,a demand response service transmission optimization algorithm based on long and short term memory artificial neural network is proposed.The SNR of the channel is predicted by the artificial neural network of long and short term memory,and the system model is trained.The optimal parameters of the model are obtained according to the root-mean-square error value,and the parameters are adjusted to obtain the optimal predicted value.According to the predicted signal-to-noise ratio,adaptive coding technology with feedback mechanism is adopted to improve the transmission quality of demand response communication.