Novel linguistic Pythagorean fuzzy dynamic Dombi aggregation operators and their application in leveraging sustainable renewable energy sources for electricity generation

Renewable energy sources (RESs) are vital for meeting global energy demands sustainably, offering cost-effective, long-term solutions with minimal environmental impact. With rising electricity needs and limited fossil fuel supplies, RESs play a crucial role, especially in emerging nations and rural areas. Aggregation operators (AOs) are crucial for effective data analysis as they combine substantial amounts of data into a single concise value. However, real-world scenarios often involve time-varying data acquisition. In this case, dynamic AOs improve decision-making by integrating altering information over time, enabling flexibility, precision, and consideration of preferences, priorities, and constraints. This study explores electricity generation through RESs using dynamic Dombi AOs within linguistic Pythagorean fuzzy (LPF) knowledge. This research establishes the Dombi operation laws for LPF numbers and develops a novel ranking system for these numbers to solve the multiple attribute decision-making (MADM) problems. In addition, we introduce two novel dynamic AOs, namely the linguistic Pythagorean fuzzy Dynamic Dombi ordered weighted averaging (LPFDyDOWA) operator and the linguistic Pythagorean fuzzy Dynamic Dombi ordered weighted geometric (LPFDyDOWG) operator and investigate the fundamental structural properties of these operators. Moreover, we develop a systematic mathematical mechanism to tackle the complexities of ambiguous data within the framework of decision-making in the LPF Dynamic Dombi aggregation scenario. In order to highlight the efficacy and superiority of our proposed methodologies, we apply these techniques to address the MADM problem of leveraging RES for electricity generation. Furthermore, we provide a comparative assessment to illustrate the authenticity and practical usefulness of the newly developed methods compared to previously presented procedures.