In response to the energy crisis and the global push for sustainability, modern power grids are increasingly integrating renewable energy, plug-in electric vehicles, and energy storage systems. This evolution demands an advanced energy management system capable of handling the variability of renewable resources, uncertainties in electric vehicle performance, fluctuating electricity prices, and dynamic load conditions. To address these challenges, our study introduces a novel decision-making framework that leverages a new score function for comparing q-rung orthopair multi-fuzzy soft numbers. This approach employs the Criteria Importance Through Inter-criteria Correlation (CRITIC) method to determine objective weights while simultaneously incorporating subjective preferences through an integrated weighting scheme. The framework is further enhanced by applying the Combined Compromise Solution (CoCoSo) method within the Lq* q-rung orthopair multi-fuzzy soft decision-making structure to select optimal energy management policies. Extensive sensitivity analysis confirms the robustness and effectiveness of the proposed methodology, offering a promising solution for efficient energy management in modern power systems.

Imprecision, uncertainty, and conflicting criteria often complicate the process of identifying the optimal conclusions in real-world decision-making scenarios. This paper suggests a novel multi-criteria decision-making (MCDM) framework that combines a hybrid logarithmic precursor chain-driven objective weighting–preference ranking organization method for enrichment of evaluations technique with linear Diophantine fuzzy sets to address uncertain frameworks. The selection of the location of a Sustainable Emergency Service Station and the selection of an investment portfolio are two real-world and socially significant decision-making challenges where traditional MCDM approaches fail to address the uncertainties. Our suggested fuzzy-based paradigm demonstrates the adaptability of both infrastructure design and financial decision-making. The results provide the optimal solutions based on our requirements, even under unpredictable conditions. The outcomes of sensitivity analysis and comparative analysis demonstrate how well the suggested approach handles ambiguous and imprecise data, particularly when expert opinions are presented in a linguistically or incompletely articulated manner. This work provides a solid, scalable, and precise method for resolving MCDM issues in the face of ambiguity, offering improved support to decision-makers in a range of fields.