Hourly level configuration and optimization of solar heating system with seasonal thermal energy storage

Solar seasonal thermal energy storage technology is an effective solution for the seasonal imbalance between solar energy resources and heating load. It is also a key technological pathway for promoting high-solar fraction solar district heating development. However, owing to the high initial investment required for the construction of a solar heating system with seasonal thermal energy storage (SHSSTS), its scientific and rational configuration is crucial for improving the energy utilization efficiency and economic feasibility. To address this issue, this study built a system model using Python that includes modules such as solar collector, seasonal thermal energy storage, auxiliary heat sources, energy transmission, and control system. A dynamic operation simulation platform was established for an SHSSTS, an hourly level configuration method based on the gradient descent algorithm was designed, and a corresponding configuration tool was developed. This tool effectively optimizes the system configuration accuracy and improves the system performance. In addition, a levelized cost of heat economic model was introduced, thereby enabling full-scenario analysis and collaborative evaluation of the effects of solar fraction and ratio of the collector area and thermal storage volume on the economic cost of the system. This provides a reliable platform for engineering design and project decision-making of SHSSTSs and important references for the development of next-generation high-precision configuration tools.