## 2 Material and Methods
### Multi-Criteria Decision-Making: Foundations  
Multi-criteria decision-making (MCDM) techniques have emerged to aid decision-making processes involving numerous variables that cannot be easily considered simultaneously to find the optimal solution [34]. These techniques standardise decision-making through mathematical modelling, facilitating the resolution of problems with multiple objectives. Some notable MCDM techniques include PROMETHEE [35], ELECTRE [36], TOPSIS [37], and AHP [38]. * PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation)--This aids in identifying the most suitable solution when decision-makers have predetermined criteria and alternatives [39]. It prioritises alternatives based on pre-established criteria, providing decision-makers with a comprehensive view of the business and enabling multifunctional decision-making strategies. However, it may encounter ranking issues. * ELECTRE--This method constructs an over-classification relationship based on decision-makers' preferences towards available alternatives [40]. ELECTRE uses a binary over classification relationship to classify alternatives, employing either a pessimistic or optimistic approach. * TOPSIS (Technique for Order of Preference by Similarity)--This method is primarily used to rank alternatives based on preference [41]. It selects alternatives closest to the ideal positive solution and farthest from the ideal negative solution, formed using the best and worst values achieved by alternatives across evaluation criteria. Its advantages lie in its simplicity, ability to compare ideal and undesirable scenarios, and quick identification of the best alternative [37]. * AHP (Analytic Hierarchy Process)--This structured decision-making tool helps individuals and organisations solve complex problems by breaking them down into simpler, more manageable components [42]. AHP is especially valuable in scenarios where decisions involve multiple criteria, both qualitative and quantitative. AHP has been extensively utilised across different domains. Studies [43; 44] have applied AHP to develop collaborative supplier performance indices, select cleaning systems for parts, choose IoT platforms, assess disaster-response management systems, analyse interoperability, and prioritise software risks [45]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **External Issue** & **Impact on Power Generation** \\ \hline \multirow{5}{*}{Geographic position} & Temperature & 1–10\% \\  & Dust Deposition & 0–15\% \\  & Snow & Determined by the Local Installation \\  & Shading & Determined by the Local Installation \\  & Spectral distribution & 0–5\% \\ \hline \multirow{2}{*}{Constructive Parameters} & Lifetime & 0–5\% \\  & Uncertainty of construction parameters & 0–5\% \\ \hline \multirow{2}{*}{Installations Mistakes} & Cabling & 0–3\% \\ \cline{2-3}  & Installation angle & 1–5\% \\ \hline \hline \end{tabular} Source: Based on [33].  
\end{table} 
Table 3: Impact of external issues in PV. According to [41], the AHP and the TOPSIS are recognised as two of the most effective multi-criteria decision-making (MCDM) methods [46]. Both methods offer unique advantages that make them well-suited for complex decision-making scenarios involving multiple criteria and alternatives. In this context, AHP enables using qualitative or quantitative data for criterion analysis in various health, industrial, technical, and strategic applications [47; 48]. The first step of AHP involves decomposing the decision problem into a hierarchy with several levels, starting from the overall goal at the top, followed by criteria and sub-criteria, and finally, the alternatives at the bottom [38]. This hierarchical structure allows decision makers to focus on smaller, related sets of decision elements, simplifying the analysis. The core of AHP lies in making pairwise comparisons between elements at each level of the hierarchy.