Abstract

One of the strategies that can be implemented to achieve sustainable energy self-sufficiency is to increase the use of renewable energy sources. Renewable energy sources such as solar, wind, hydropower, and geothermal have the potential to provide clean, reliable, and affordable energy. Countries that are rich in renewable energy resources can leverage these resources to reduce their reliance on fossil fuels and achieve energy self-sufficiency. Based on the background and problem formulation that has been conveyed, this study aims to develop a decision-making framework to identify and recommend optimal renewable energy solutions for Southwest Sumba, considering local conditions, resource potential, and sustainability criteria. This research employs a systematic approach to develop a decision-making framework for identifying and recommending optimal renewable energy solutions in Southwest Sumba. The study will focus on creating a structured process that considers local conditions, resource potential, and sustainability criteria. The development of a comprehensive and adaptive decision-making framework is crucial for determining optimal renewable energy solutions in remote areas such as Southwest Sumba. A multi-stage approach that integrates Multi-Criteria Decision Making (MCDM) methods such as TOPSIS, considering the criteria of Consolidated, Controllable, Continuous, Clean, and Cheap (5C), and combining expert evaluation, local knowledge, and community-based approaches, can be a key solution to addressing the challenges of renewable energy implementation. The findings of this study indicate that the developed decision-making framework has significant implications in overcoming the challenges of renewable energy implementation in remote areas like Southwest Sumba. This framework not only provides guidance for policymakers and energy planners in developing and implementing renewable energy projects but also emphasizes the importance of a thorough business feasibility study after selecting the best alternative model.