Novel Wind Power Station Site Selection Framework Based on Multipolar Fuzzy Schweizer-Sklar Aggregation Operators

Nowadays, wind power stations play a significant role in eco-friendly energy production by efficiently harnessing wind energy to produce electricity. A crucial factor in constructing a wind power station is the site selection process, which identifies ideal locations for wind turbines to optimize energy generation, minimize costs, and reduce environmental impact. This complex decision-making involves multipolar attributes, including technical and environmental categories. An m-polar fuzzy (mPF) set model is an effective tool for addressing such uncertain problems involving multi-dimensional parameters. The main goal of this study is to integrate Schweizer-Sklar operations with mPF information to determine the aggregated results in a more generalized environment. We develop some novel mPF-geometric and mPF-averaging aggregation operators (AgOs), including the mPF Schweizer-Sklar weighted averaging (mPFSSWA), mPF Schweizer-Sklar ordered weighted averaging (mPFSSOWA), mPF Schweizer-Sklar hybrid averaging (mPFSSHA), mPF Schweizer-Sklar weighted geometric (mPFSSWG), mPF Schweizer-Sklar ordered weighted geometric (mPFSSOWG), and mPF Schweizer-Sklar hybrid geometric (mPFSSHG) operators. We support these AgOs by presenting numerical examples and some fundamental properties, like monotonicity, boundedness, idempotency, and commutativity. Further, we propose an algorithm for both mPFSSWA and mPFSSWG operators to minimize uncertainty in various MCDM problems. Next, we investigate a case study of Sindh province in Pakistan (i.e., choosing the best site for a wind power station) by implementing the suggested algorithm. Finally, we compare the developed mPF Schweizer-Sklar AgOs with the preexisting mPF-Yagar, mPF-Dombi, mPF-Aczel-Alsina AgOs, mPF-AHP (Analytical Hierarchy Process), mPF-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and mPF-ELECTRE-I (ELimination and Choice Expressing REality)-I methods.