The development of the wind energy industry is seriously restricted by grid connection issues and wind energy generation rejections introduced by the intermittent nature of wind energy sources. As a solution of these problems, a wind power system integrating with a thermal energy storage (TES) system for district heating (DH) is designed to make best use of the wind power in the present work. The operation and control of the system are described in detail. A one-dimensional system model of the system is developed based on a generic model library using the object-oriented language Modelica for system modeling. Validations of the main components of the TES module are conducted against experimental results and indicate that the models can be used to simulate the operation of the system. The daily performance of the integrated system is analyzed based on a seven-day operation. And the influences of system configurations on the performance of the integrated system are analyzed. The numerical results show that the integrated system can effectively improve the utilization of total wind energy under great wind power rejection.

The simulation and analysis of neutronics parameters in Molten Salt Reactors (MSRs) is fundamental for the design of the reactor core. However, high-fidelity neutron transport calculations of the MSR are time-consuming and require significant computational resources. Artificial neural networks (ANNs) have been used in various industries, and in recent years are increasingly introduced in the nuclear industry. Back-Propagation neural network (BPNN) is one type of ANN. A surrogate model based on BP neural network is developed to quickly predict two key neutronics parameters in reactor core: the effective multiplication factor ($k_{\mathrm{eff}}$) and the three-dimensional channel-by-channel neutron flux distribution. The dataset samples are generated by modeling and simulating different operation states of the Molten Salt Reactor Experiment (MSRE) using the Monte Carlo code. Hyper-parameters optimization is performed to obtain the optimal surrogate model. The numerical results on the test dataset show good agreement between the surrogate model and the Monte Carlo code. Additionally, the surrogate model significantly reduces computation time compared to the Monte Carlo code and greatly enhances efficiency. The feasibility and advantages of the proposed surrogate model is demonstrated, which has important significance for real-time prediction and design optimization of the reactor core.