The development of the Energy Internet has improved the efficiency of energy utilization and promoted sustainable development of power and energy systems. The multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation control. Through the energy circuit theory

the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are extracted. Combined with dynamic characteristic parameters

the long short-term memory (LSTM) neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying (LTV) equations. Combined with the equations

an energy hub method is used to build a control model of industrial parks with multi-energy distribution system. Using the rolling optimal control strategy given in this paper

the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are obtained. Finally

the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline system. The rolling control model of the multi-energy system can improve the efficiency of energy utilization

exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.