## 4 Discussion
### Limitations/Trade-Offs with the Proposed Reconfiguration Method
#### 4.2.3 Long-Term Performance  
_Energy Efficiency_: The proposed reconfiguration technique increases the efficiency of the PV array by dynamically connecting adaptive panels to the fixed part of the array based on predetermined logic, even under varying shading scenarios.  
_Computation Time and Manageability_: Despite the large solution space sizes noted in Table 1, the genetic algorithm effectively scans the space and identifies the optimal configuration within reasonable timeframes. Figure 5 and Table 2 demonstrate the algorithm's efficiency in this process.  
Some Performance Metrics Other than Efficiency Other Performance Metrics: Reliability, Cost, and Scalability  
The proposed reconfiguration method has proven effective not only in terms of energy efficiency but also in the following performance metrics:  
_Reliability_: The electronic switching elements used in the SMC circuit of the proposed reconfiguration method ensure long-term safe operation of the system due to their ease of control, low conduction loss, and maintenance-free nature.  
_Cost_: With the significant reduction in the costs of electronic switching elements and control boards today, the initial and maintenance costs of the proposed reconfiguration method remain relatively low when considering the increase in efficiency gained from the PV array.  
_Scalability_: The exponential growth of the solution space, as shown in Table 1, is a key factor limiting the scalability of the system. Larger PV arrays require the genetic algorithm to search a more extensive solution space. However, the genetic algorithm is designed to operate effectively in such growing systems. In the simulation studies conducted, the convergence time of the developed optimization algorithm was found to be 7.354 s for a five-row PV array, 18.685 s for a 6-row PV array, and 331.249 s for a 7-row PV array.  
Therefore, considering the average frequency of shading condition changes in a real-time system, it can be concluded that the developed method is applicable for PV arrays with up to six rows.  
Considering these factors, the proposed method not only improves energy efficiency but also has a significant impact on performance metrics such as reliability, cost, and scalability.  
The proposed dual-adaptive configuration method differs from other methods in the literature by enhancing energy efficiency and adapting to dynamic shading scenarios. Additionally, Table 11 compares the general characteristics of single and dual-adaptive configurations, providing a qualitative comparison with fixed and single-adaptive structures. While the dual-adaptive method demonstrates superior performance in terms of energy efficiency and reconfiguration capacity, it also involves trade-offs, such as higher initial costs and increased implementation complexity.  
Table 11 provides a comprehensive comparison of different PV panel configuration methods in terms of energy efficiency, implementation complexity, number of components, dynamic reconfiguration capability, shading impact management, scalability, initial cost, and long-term performance. The proposed dual-adaptive method offers very high energy efficiency due to its optimized dual-adaptive structure. This method demonstrates superior performance in managing shading effects and dynamic reconfiguration while enhancing scalability by efficiently optimizing the large solution space through the use of a GA.  
Overall, despite factors such as initial cost and implementation complexity, the proposed dual-adaptive method provides a superior solution compared to other methods, thanks to its high long-term energy gains and ability to adapt to dynamic conditions.