Optimal allocation of renewable energy resources in a smart grid under neutrosophic credibility-based uncertainty

Modern research in renewable energy systems faces many challenges in dealing with uncertain data and inconsistent expert evaluations, particularly in the context of smart grid systems. Traditional multi-attribute decision-making (MADM) methods are incapable of representing the accumulated effect of truth, ambiguity and credibility, which are important factors when it comes to decision-making in energy planning. To address these limitations, this study presents a novel concept of neutrosophic credibility numbers (NCNs) as a new modeling tool that uses truth, indeterminacy and falsity components with credibility measurement for uncertainty representation. Based on NCNs, we introduce a novel aggregation operator termed as generalized neutrosophic fuzzy credibility numbers weighted averaging (GNFCNWA) operator which enhances aggregation of uncertain information and improves the credibility of decisions. Furthermore, we also provide an algorithm that extends the utilization of NCNs with the GNFCNWA operator in a complete approach to decision-making. In order to validate the effectiveness of our proposed approach, the method is applied to a case study in an optimal renewable energy resource allocation in sustainable energy systems in a smart grid. The analysis focuses on evaluating four major alternatives such as solar farm, wind park, battery storage system and hybrid energy systems on the basis of four key attributes including energy demand matching, transmission efficiency along with environmental impact and integration feasibility. The alternative solar farm stands out as an optimal alternative. A sensitivity analysis is performed using different set of value of the operator's parameter to confirm the robustness of the ranking outcomes. Furthermore, a comparative analysis with the existing methods confirms the efficacy of the proposed approach. Overall, the fusion of neutrosophic theory with credibility provides groundbreaking approach to sustainable energy systems and other engineering domains.