Year: 2025
Type: article
Abstract: This paper presents an evaluation of five renewable energy systems—Solar PV, Wind Energy, Hydropower, Biomass, and Geothermal Energy—using the Complex Proportional Assessment (COPRAS) method. The analysis considers four key criteria: Energy Efficiency, Technology Maturity, Resource Availability, and Land Requirement. Each criterion is assigned equal weight, ensuring a balanced evaluation across technical, environmental, and spatial factors. The decision-making process involves normalizing the data and applying weighted scores to rank the systems based on both beneficial and non-beneficial criteria. Geothermal Energy emerges as the top-ranked renewable energy option, achieving the highest utility score (100%) due to its excellent balance of high energy efficiency, technology maturity, and minimal land use. Hydropower, while leading in energy efficiency and technological maturity, ranks second due to its high land requirement. Wind Energy follows in third place, providing moderate energy efficiency and land use but offering reliable technological performance. Solar PV ranks fourth, hindered by lower energy efficiency but benefitting from advanced technology and lower land use. Biomass ranks last, despite its high resource availability, due to its moderate energy efficiency and significant land requirement. This study highlights the trade-offs between different renewable energy systems, offering insights into how factors like land availability and technological maturity impact the feasibility of renewable energy integration. The results can guide decision-makers in selecting the most suitable energy systems for their specific needs, balancing both environmental and economic objectives. The application of the COPRAS method provides a structured and comprehensive approach to evaluating the performance of diverse renewable energy options, making it a valuable tool for policymakers and energy planners looking to optimize renewable energy integration strategies.
DOI: 10.46632/jacp/3/3/3
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409867447