Integrating Fuzzy AHP and GRA for Strategic Evaluation and Decision-Making in Renewable Energy Technologies
This paper presents a structured evaluation framework for renewable energy technologies (RETs) in project management, addressing the critical need to mitigate environmental impacts and reduce dependence on fossil fuels. By integrating decision-making techniques, the study aims to provide a systematic approach for selecting optimal RETs.
Subjective judgments and uncertainties often complicate RET selection. To overcome these challenges, this study develops a dual-method framework using Fuzzy Analytic Hierarchy Process (F-AHP) and Grey Relational Analysis (GRA), applied within the Moroccan Agency for Sustainable Energy (MASEN) project. The framework enhances objectivity in RET decision-making.
The research employs F-AHP to determine the relative importance of selection criteria, accounting for uncertainty in expert judgments. GRA then ranks RETs based on these weighted criteria. The methodology is applied to the MASEN project to validate its effectiveness empirically.
This study introduces a novel integration of F-AHP and GRA in RET evaluation, bridging qualitative and quantitative decision-making approaches. By refining selection methodologies, it advances the practical application of systematic assessment tools in sustainable energy projects.
The proposed F-AHP-GRA methodology demonstrates high effectiveness in RET evaluation. RET4, identified as the optimal choice, reflects the framework’s ability to enhance decision-making transparency and accuracy. Detailed analysis of criteria weights provides deeper insights into RET prioritization.
Energy project managers can adopt this framework to make data-driven and objective decisions in RET selection, improving project sustainability and efficiency.
Future studies should explore the adaptability of the F-AHP-GRA methodology across various energy project contexts, assessing its effectiveness in diverse geopolitical and technological settings. Additionally, refining selection criteria – such as socio-economic impacts and lifecycle costs – could enhance RET evaluation accuracy.
By providing a structured framework for RET selection, this research supports sustainable energy advancements, reducing environmental degradation, and aiding the transition toward global sustainability goals. The methodology promotes data-driven decision-making, fostering more efficient and responsible energy management.
Further investigation could focus on expanding the model to include dynamic, real-time data integration for improved responsiveness in RET evaluations. Additionally, exploring its applicability beyond RET selection, such as in broader sustainability initiatives, could unlock new opportunities for energy policy and project management.
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