## 5 Conclusions  
In this study, a new reconfiguration method is presented for a PV array with a dual-adaptive structure. In this method, an algorithm is used to ensure that the rows of the PV array are matched with the rows of the fixed section, so that the irradiance and therefore the short circuit currents are closest to each other. In the simulations made with different PSC scenarios on a 3 and 4-row PV array, the increase in the array efficiency with the dual-adaptive structure was clearly seen through the reconfiguration process. In addition, in another simulation, it was observed that a higher efficiency was achieved with the reconfiguration process performed with the dual-adaptive structure in the PV array with a single and dual-adaptive structure. It has also been shown to be scalable for different PV array sizes. With the GA-based optimization algorithm developed within the scope of this study, increasing connection possibilities and control complexity are minimized, making it applicable in real-time systems thanks to a feasible cycle time.  
The reconfiguration method offers an effective approach to enhancing energy efficiency while also having significant impacts on other performance metrics, such as reliability, cost, and scalability. These impacts have been optimized through the balance achieved by the integration of fixed and adaptive panels. However, the scalability of the system and the durability of hardware components should be the focus of future research.  
Simulation studies have shown that the developed optimization algorithm can determine a new connection order for reconfiguration within an acceptable time (18.685 s) for up to six rows. However, in a 6-row PV array, dual-adaptive configurations require optimization within an exceptionally large solution space. For instance, as indicated in Table 1, there are 4,705,096,570,216,277,114,880 (approximately 4.7 septillion) possible adaptive-fixed connection configurations, a 22-digit number. Despite this challenge, the proposed method successfully generated a solution and performed reconfiguration in approximately 18 s, an acceptable timeframe. These results clearly demonstrate the robustness of the proposed method, as it effectively tackles the complexities of such a vast solution space, making it applicable for PV arrays with up to 6 rows.  
Considering these factors, the proposed method not only enhances energy efficiency but also has a significant impact on performance metrics such as reliability, cost, and scalability.  
Dual-adaptive PV panel design: K.S.P.; Creation of the solution space resulting from the matching of adaptive and fixed parts: K.S.P. and O.K.; Determination of roles in Genetic Controller: K.S.P. and O.K.; Adaptation of the solution space for solving with the genetic controller: O.K.; Genetic algorithm design and coding: O.K.; Simulation design and application: K.S.P. and O.K.; Matlab simulation: K.S.P. All authors have read and agreed to the published version of the manuscript.  
This project is supported by Institution of Firat University Scientific Research Projects (FUBAP) through project number MF.24.99 and APC has been funded under the project.  
Not applicable.  
Data available statement: Data is contained within the article.  
The authors declare no conflicts of interest.