Volume 4,Issue 3
Monte Carlo - Optimization Design of Concentrating Mirror Field for Clustering Algorithm
Tower solar thermal power is a high-efficiency clean energy source. Aiming at low optical efficiency and unreasonable layout in the heliostat field, this paper proposes an integrated optimization method combining Monte-Carlo ray tracing and DBSCAN clustering. The optical efficiency model is established to calculate the cosine, shading, atmospheric and truncation efficiency. On this basis, the tower position, heliostat size, height and layout are optimized. Results show that the proposed method significantly improves the annual average thermal power output per unit area. This design provides an effective solution for engineering applications. Due to the optimized layout allowing more heliostats to be deployed within the same footprint, the total power output has nearly doubled. The annual average output thermal power increases from 32.40 MW to 65.59 MW, and the annual average output thermal power per unit mirror area increases from 0.516 kW/m2 to 0.690 kW/m2.
[1] Yang G, 2022, Influence of Solar Shape and Optical Errors on Focused Energy Spillage in Tower Solar Thermal Power Plants, thesis, North China Electric Power University (Beijing).
[2] Liu R, Yu Y, Liu J, et al., 2025, Electric Vehicle Charging Load Prediction Based on Improved Monte Carlo Algorithm. Zhejiang Electric Power, 44(8): 15–23.
[3] Han S, Yang N, Yao Y, et al., 2025, Derivation of Rain Patterns in Jiangsu Province Based on Cluster Analysis. Journal of Hydrology, 46(1): 91–97.
[4] Qian W, Lu X, Yun T, et al., 2025, A Ray-Tracing Algorithm Based on Discrete Anisotropic Radiative Transfer Model. Journal of Northwest Forestry University, 40(1): 1–10.