Mathematical Model for Economic Optimization of Tower-Type Solar Thermal Power Generation Systems via Coupled Monte Carlo Ray-Tracing and Multi-Mechanism Heat Loss Equations

With the global energy transition and decarbonization goals, tower-type solar thermal power generation is increasingly important for dispatchable clean energy due to its high efficiency, thermal storage capacity, and regulation performance. However, current research focuses on ideal conditions, ignoring real geographical constraints on heliostat layout and environmental impacts on receiver performance. More practical scene modeling and performance evaluation methods are urgently needed. To address these issues, we propose a heliostat field simulation algorithm based on heat loss mechanisms and real site characteristics. The algorithm includes optical performance evaluation (cosine efficiency, shading, truncation, atmospheric transmittance) and heat loss mechanisms (radiation, convection, conduction) for realistic net heat output estimation. Experiments revealed the following: (1) higher central towers improve optical efficiency by increasing solar elevation angle; (2) radiation losses dominate at high power and tower height, while convection losses dominate at low power and tower height. Using the Economic-Integrated Score (EIS) optimization algorithm, we achieved optimal tower and receiver configuration with 40.22% average improvement over other configurations (maximum 3.9× improvement). This provides a scientific design basis for improving tower-type solar thermal systems’ adaptability and economy in different geographical environments.