Design of Heliostat Field Based on Genetic Optimization Algorithm

Authors

  • Yunfei Zhang
  • Zhengzhi Kuang
  • Haoran Liu

DOI:

https://doi.org/10.54097/jceim.v11i2.12405

Keywords:

Tower solar, Photovoltaic power generation, Ray tracing method, Genetic algorithm

Abstract

This paper mainly focuses on the in-depth study of the application of tower solar photovoltaic power generation technology in low carbon and environmental protection. The optimal design of the heliostat mirror field is carried out by using the ray tracing method and the genetic optimization algorithm. Firstly, the coordinate transformation of the reflective points of the heliostat mirror was realized by establishing the light cone coordinate system, mirror coordinate system and ground coordinate system, and the related annual average optical efficiency and thermal power were calculated; secondly, under the satisfaction of several constraints, the genetic algorithm was applied to optimize the parameters of the heliostat mirror field in order to maximize the annual average output thermal power per unit of mirror area. Finally, a comparison with the existing mainstream schemes proves the effectiveness and rationality of the model.

References

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Fangfang. Research on power load forecasting based on Improved BP neural network [D]. Harbin Institute of Technology, 2011.

Amjady N. Short-term hourly load forecasting using time series modeling with peak load estimation capability [J]. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.

SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model [J]. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.

Ma Kunlong. Short term distributed load forecasting method based on big data [D]. Changsha: Hunan University, 2014.

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Published

05-10-2023

Issue

Section

Articles

How to Cite

Zhang, Y., Kuang, Z., & Liu, H. (2023). Design of Heliostat Field Based on Genetic Optimization Algorithm. Journal of Computing and Electronic Information Management, 11(2), 39-44. https://doi.org/10.54097/jceim.v11i2.12405

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