Regional Phase Change Storage Station Optimal Operation Considering Dynamic Temperature Estimation Based on BP Neural Network

At present, most of the electro-thermal system optimization and control do not consider the matching relationship between the rapid fluctuation characteristics of renewable energy and the large inertia of the thermal system. The system's analysis and optimal control method of the electric-thermal combined system with phase-change thermal storage station are still at a blank stage. In view of the above problems, this paper analyzes the principle of energy transfer, conversion and storage in the electric-thermal system. A method based on BP neural network for dynamic estimation is proposed to optimize the operation control considering the model of phase change thermal storage system. Further, the phase change heat storage system is used to solve the wind power curtailment problem, so as to lay out the foundation for the optimal regulation in the electric-thermal storage system.