## 2 Literature Review
### Existing MCDM Approaches Adopted for Similar Problems  
To understand the effectiveness of MCDM in the context of the present research, this subsection will discuss their implementation in the renewable energy sector by also providing a specific focus on the solar energy field. [PERSON] and [PERSON] [19] reviewed various applications of MCDM methods, examining advancements, strengths, and limitations. Their work provided a comprehensive understanding of the complexity of decision-making processes and guidance on selecting appropriate MCDM techniques according to the specificity of the real scenario, driving sustainable development across different domains. [PERSON] et al. [PERSON] et al. (2018) discussed the importance of adopting modern technologies in the manufacturing field by identifying barriers along with their respective solutions. On the basis of experts' consultation, the authors first ranked the various identified barriers using the Best-Worst Method (BWM) and second evaluated solutions using the Combined Compromise Solution (CoCoSo) method. Various modeling techniques such as Interpretive Structural Modeling (ISM), Total Interpretive Structural Modeling (TISM), and DEMATEL were also implemented to determine the interrelations among the identified barriers, developing a thorough analysis of the challenges and opportunities related to modern technology adoption in manufacturing. [PERSON] et al. [PERSON] et al. (2018) explored various MCDM methods applied within Sustainable Supply-Chain Management (SSCM). Key methods discussed include DEMATEL and its variants, such as Fuzzy and Grey DEMATEL, the AHP and its fuzzy counterpart for complex decision-making, as well as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS. Additionally, methods like ELECTRE and the Analytical Network Process (ANP) were highlighted for their roles in addressing conflicting criteria and interdependencies within supply chains. In the renewable energy sector, [PERSON] et al. [PERSON] et al. (2018) presented 16 different configurations for energy self-consumption in Renewable Energy Communities (RECs) containing different industrial factories. A mathematical model for each configuration was proposed, and various MCDM-based approaches were employed, namely Weighted Sum (WS), Weighted Product (WP), TOPSIS, and Evaluation based on Distance from Average Solution (EDAS). This study showed the effectiveness of configurations that combine both individual and collective energy self-consumption in RECs. [PERSON] et al. [PERSON] et al. (2018) deployed a new hybrid fuzzy MCDM model aimed at selecting the best renewable energy projects at a sub-national level. The proposed model combined such methods as VIKOR (an acronym for \"Vlsekriterijumska KOmpromisono Rangiernje\" in Serbian, which translates to \"Multi-criteria Optimization and Compromise Solution\" in English), distance from average solution, and additive ratio assessment. The results identified solar energy as the most suitable renewable energy option, with Jajarm identified as the optimal location for project implementation. In addition, sensitivity analysis demonstrated the robustness of the model. [PERSON] (2018) integrated the TOPSIS methodology with triangular neutrosophic sets to rank and select the best source of renewable energy in Egypt. [PERSON] et al. [PERSON] et al. (2018) assessed the spatial suitability of solar and wind farm locations in India from technical, economic, and socio-environmental perspectives. They integrated the Geographical Information System (GIS) with MCDM approaches. The proposed model served as a tool for policymakers regarding renewable energy resources and assessed the suitability of existing projects. [PERSON] et al. [PERSON] et al. (2018) integrated the Fuzzy AHP and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II to assess the suitability of sites for implementing solar PV projects in Saudi Arabia.