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Academic Journal of Computing & Information Science, 2024, 7(5); doi: 10.25236/AJCIS.2024.070530.

Design Optimization Model for Fixed-Sun Mirror Field Based on Monte Carlo Simulation and Particle Swarm Algorithm


Weiyi Zhang

Corresponding Author:
Weiyi Zhang

School of Mathematics and Physics, Southwest University of Science and Technology, Mianyang, China


This study focuses on the optimal design of a circular heliostat mirror field, aiming to improve the energy utilization efficiency of a tower solar thermal power plant and help China achieve its carbon emission reduction goals. Parameter analysis and optimization are carried out by tools such as Python and MATLAB, and Monte Carlo simulation and other methods are used to calculate the relevant indexes and establish a constrained optimization model to solve the design problems. For different constraints, the particle swarm algorithm is used to optimize the parameters such as the size of the heliostat mirror and the installation height, so as to maximize the annual average output power per unit mirror area. Ultimately, the optimization model proposed in this study achieves a significant increase in the rated annual output power under different scenarios, which provides an important reference for promoting the application of new energy and achieving the goal of carbon emission reduction.


Monte Carlo Simulation Methods, Affine Matrix, Particle Swarm Algorithm

Cite This Paper

Weiyi Zhang. Design Optimization Model for Fixed-Sun Mirror Field Based on Monte Carlo Simulation and Particle Swarm Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 229-235. https://doi.org/10.25236/AJCIS.2024.070530.


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