Optimal Configuration of Standalone Wind–Solar–Storage Complementary Generation System Based on the GA-PSO Algorithm

  • Qian SUN State Grid Henan Electric Power Company
  • Jianwei MA State Grid Henan Electric Power Company
  • Yanjie SHE State Grid Henan Electric Power Company
  • Jingchao ZHANG State Grid Henan Electric Power Company
  • Bo GU dNorth China University of Water Resources and Electric Power
  • Zichao ZHANG Texas Tech University


The capacity configuration of the standalone wind–solar–storage complementary power generation system (SWS system) is affected by environmental, climate condition, load and other stochastic factors. This makes the capacity configuration of the SWS system problematic when the capacity configuration method of traditional power generation is used. An optimal configuration method of the SWS system based on the hybrid genetic algorithm and particle swarm optimization (GA-PSO) algorithm is proposed in this study to improve the stability and economy of the SWS system. The constituent elements of investment, maintenance cost and various reliability constraints of the SWS system were also discussed. The optimal configuration of the SWS system based on GA-PSO was explored to achieve the optimization objective, which was to minimize investment and maintenance costs of the SWS system while maintaining power supply reliability. The investment and maintenance costs of the SWS system under different configuration methods were calculated and analyzed on the bases of the monthly mean wind speed, solar radiation and load data of Xiaoertai Village in Zhangbei County of Hebei Province in the last 10 years. Results show that the optimal configuration method based on the GA-PSO algorithm could effectively improve the economy of the system and meet the requirements of system stability. The proposed method shows great potential for guiding the optimal configuration of the SWS system in remote areas.


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How to Cite
SUN, Qian et al. Optimal Configuration of Standalone Wind–Solar–Storage Complementary Generation System Based on the GA-PSO Algorithm. Journal of Power Technologies, [S.l.], v. 99, n. 4, p. 231–236, dec. 2019. ISSN 2083-4195. Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1617>. Date accessed: 29 july 2021.
Energy Engineering and Technology

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