Multi-criteria evaluation with gradual-weighting and aggregation of normalised distance matrices: a case study in renewable energy grid selection

Moving towards a sustainable future, optimising energy systems and resources involves increasingly sophisticated decision-making processes that require balancing technical efficiency with societal, economic and environmental constraints. However, traditional decision-making methods often rely on unidimensional cost metrics and are prone to rank reversal and sensitive to normalisation, leading to unstable or biased outcomes. Driven by these methodological and domain-specific research gaps, this article proposes a novel algorithm, “Multi-Criteria Evaluation via Gradual-Weighting and Aggregation of Normalised Distance Matrices (MEGAN).” Rather than using fixed-weight vectors as in conventional methods, MEGAN adjusts the weight vectors gradually to aggregate rankings across scenarios. Moreover, it utilises dynamic thresholds based on dispersion measures to improve decision precision. The proposed algorithm was verified using a synthetic dataset of ten grid alternatives evaluated across fourteen criteria (economic, societal, environmental, and technical) based on real-world expert priorities. Experimental analyses demonstrated that MEGAN achieves superior ranking stability, remaining insensitive to variations in normalisation techniques compared to conventional decision-making methods. In addition, according to the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) complexity protocol, it achieved a lower computational complexity score than complex outranking methods, thereby offering scalability for large-scale problems. In summary, MEGAN provides a robust, insensitive, and computationally efficient decision-support tool capable of managing the multidimensional trade-offs essential to sustainable energy planning.