Decision-Support Frameworks in Renewable Energy Planning: Comparative Insights and the Central Role of AHP

Abstract

The growing need for rapid decarbonization has greatly increased the complexity of renewable energy planning, which requires multi-criteria decision-making (MCDM) frameworks to balance multiple, and sometimes conflicting, decision objectives. Among the various MCDM techniques, the analytic hierarchy process (AHP) is one of the most widely used methods because of its capability to break down complex problems into hierarchical systems and to combine quantifiable information with qualitative judgments from experts. This paper synthesizes the literature related to the application of MCDM in renewable energy planning, specifically focusing on AHP's applications to technology selection and prioritization, site selection and infrastructure placement, and policy development. This paper compares and contrasts AHP with other MCDM families, identifies hybrids and new methodologies (including spatial analysis, uncertainty models, and participatory governance), and assesses current trends in MCDM research. Additionally, this paper uses examples from around the world to discuss some of the long-standing methodological challenges associated with using MCDM for renewable energy planning (expertise bias, rank reversal, and scale sensitivity), and offers suggestions to improve the rigor and transparency of MCDM use in renewable energy planning. Finally, this it argues that AHP and its hybridized versions can be critical components to achieve alignment between decision processes and the tenets of energy justice and the broad objectives of international sustainability frameworks (net-zero and climate resilience targets).