**5. Conclusions**

PV generation is playing a more significant role in the future energy landscape. Meanwhile, accurate PV models can support the PV systems' accurate assessment, efficiency improvement, fault analysis, and simulation. Thus, this paper reviewed different metaheuristics employed in the PV model parameters extraction. In our work, (a) the PV models and problem formulations were explained; (b) different metaheuristics and their developments and applications were summarized; (c) the algorithmic parameter settings, various evaluation indicators, independent running numbers, and computational resources (TNFES) were assembled; (d) the final results of various algorithms were compared, and especially RMSE and SIAE were ranked; (e) the unknown parameters and RMSE variation patterns in different environments were analyzed; and (f) a comprehensive analysis of the challenges encountered by metaheuristics in solving the studied issue was presented, and some ideas for future research were outlined.

This study can assist beginners in gaining an introductory and systematic perspective on the issue. It may also provide a reference direction for further research when unfamiliar researchers understand the application of metaheuristics to this engineering problem.

**Author Contributions:** Conceptualization, Z.G. and G.X.; methodology, G.X.; formal analysis, G.X. and X.F.; writing—original draft preparation, Z.G.; writing—review and editing, G.X. and X.F.; supervision, G.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52167007, the Natural Science Foundation of Guizhou Province, grant number QiankeheBasic-ZK [2022] General121, the Innovation Foundation of Guizhou University Institute of Engineering Investigation and Design Co., Ltd., grant number GuiDaKanCha [2022]03, and the Modern Power System and Its Digital Technology Engineering Research Center, grant number QianJiaoJi [2022]043.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
