An augmented fuzzy decision-making framework for evaluating renewable energy sources for hydrogen production: A case application to India

Abstract

Hydrogen is the cleanest fuel, and its production from renewable sources is crucial for addressing energy and climate challenges. India’s renewable energy capacity presents an opportunity to produce environmentally friendly hydrogen for the international market. However, large-scale hydrogen production requires a thorough assessment of renewable energy sources under multiple constraints. This study introduces a fuzzy logic-based multistage assessment model to evaluate the feasibility of renewable energy-based hydrogen generation in India. A novel approach using bipolar complex linear diophantine fuzzy sets (BCLDFS) is proposed to account for both positive and negative perspectives in human judgment. The integration of bipolarity into the complex-valued structure of linear diophantine fuzzy sets enhances the precision of decision-making. Additionally, a modified BCLDF-level based weight assessment (LBWA)- multi-normalization multi-distance assessment (TRUST) method is developed as a multi-attribute decision-making (MADM) tool. This approach ranks renewable energy alternatives based on technical, economic, social, political, and environmental factors, along with 14 sub-criteria. This approach ranks renewable energy alternatives based on technical, economic, social, political, and environmental factors, along with 14 sub-criteria. In the case study focused on India, the proposed model identified solar energy as the most suitable renewable energy source for hydrogen production, followed by wind and hydro power. These results align with India’s green hydrogen priorities. A comparative and sensitivity analysis further validates the robustness of the proposed methodology, ensuring its reliability in strategic decision-making for sustainable hydrogen generation.