Fuzzy TOPSIS‐Based Multi‐Criteria Selection of Renewable Energy Sources for Smart Grids

Transitioning to renewable energy sources (RES) is essential for achieving sustainable energy systems and reducing environmental impacts. Integrating RES into smart grids presents a complex decision‐making challenge, as it involves evaluating multiple, often conflicting, criteria such as cost, energy efficiency, environmental impact, reliability, and scalability. This study applies the Fuzzy TOPSIS (FTOPSIS) method to assess and rank four common RES: solar, wind, biomass, and hydropower. The evaluation was based on expert opinions from 25 industry professionals, taking into account various factors that impact smart grid integration. The final ranking, determined by the FTOPSIS method, showed that wind energy ranked highest with a closeness coefficient ( C i ) of 0.72, followed by solar ( C i = 0.65), hydropower ( C i = 0.59), and biomass ( C i = 0.48). These findings highlight the strengths and trade‐offs of each energy source, providing valuable insights for energy planners to optimize smart grid integration. This study demonstrates the effectiveness of FTOPSIS in managing uncertainty and providing a structured decision‐making framework for renewable energy selection, while offering new insights into smart grid integration by emphasizing criteria such as scalability, adaptability, and real‐world expert input. Further research could explore the inclusion of additional criteria and real‐time data to refine the decision‐making process.