**5. Conclusions**

This work attempts to study and discuss two MPPT techniques based on two metaheuristic optimization algorithms, i.e., PSO and GWO. These new techniques of MPPT overcome the problems of classic MPPT strategies (e.g., perturb and observe and incremental) when tracking the maximum power point, even in the presence of sudden changes of irradiation and shadows on the photovoltaic modules. The MPPT techniques studied show good behavior and better performance. A comparative study of simulation results for a different type of shading shows PSO-MPPT effectiveness compared to GWO-MPPT from the point of view of speed and oscillation during the transient state. In addition, a simulation test shows the efficiency of PSO MPPT versus GWO MPPT in terms of storage charge in the battery under uniform irradiation. As a future work, we would like to analyze how to adapt the algorithm's parameters (which are now constant) to the dynamic lighting conditions.

Moreover, one of the future endeavors of this work is to compare more algorithms performances and search the best combination that can be used for such an optimization problem. Therefore, incremental algorithm, perturb and observe algorithm, the fuzzy solution and other population-based metaheuristic algorithms as bio-inspired algorithms, evolutionary algorithm and physics-based algorithm will be studied, tested and evaluated. **Author Contributions:** Conceptualization, H.K. and F.A.; Data curation, H.K.; Formal analysis, H.K. and F.A.; Funding acquisition, M.A.; Investigation, H.K. and L.Y.; Methodology, H.K. and F.A.; Project administration, H.K., A.T. and S.S.M.G.; Resources, H.K.; Software, H.K. and L.Y.; Supervision, H.K. and M.A.; Validation, H.K. and F.A.; Visualization, H.K., F.A., L.Y., M.A. and S.S.M.G.; Writing original draft, H.K., F.A., L.Y., A.T., M.A. and S.S.M.G.; Writing—review & editing, H.K., F.A., L.Y., A.T., M.A. and S.S.M.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Taif University Researchers Supporting Project, grant number "TURSP-2020/122" and by II Plan Propio Smart Campus, project "Smart and Secure EV Urban Lab II" by the University of Málaga (Spain).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/122), Taif University, Taif, Saudi Arabia and the financial support received from II Plan Propio Smart Campus, project "Smart and Secure EV Urban Lab II" by the University of Málaga (Spain).

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

### **References**

