An Improved Fuzzy Multi-Criteria Algorithm for Optimizing CSP Hybrid Systems Using Pythagorean Fuzzy Sets

 

1. Introduction

This research explores a novel fuzzy-based optimization framework for CSP hybridized systems, focusing on decision-making under uncertainty. By leveraging Pythagorean Fuzzy Sets, the study enhances conventional multi-criteria algorithms to better capture ambiguity in renewable energy performance assessment, offering a robust foundation for sustainable energy planning.

2. Pythagorean Fuzzy Set Theory in Energy Systems

Pythagorean Fuzzy Sets provide greater flexibility than traditional fuzzy and intuitionistic fuzzy models by allowing a wider representation of uncertainty. This topic discusses how PFS improves the evaluation of complex energy parameters such as efficiency, reliability, and environmental impact in CSP hybrid systems.

3. Multi-Criteria Decision-Making Framework

This section highlights the development of an improved fuzzy multi-criteria algorithm capable of balancing technical, economic, and environmental criteria. The approach enables comprehensive ranking and selection of optimal CSP hybrid configurations under real-world constraints.

4. Optimization of CSP Hybridized Systems

The study demonstrates how the proposed algorithm enhances system optimization by integrating CSP with complementary energy sources. It improves overall system performance, energy stability, and operational efficiency while reducing uncertainty-driven decision errors.

5. Sustainability and Energy Performance Evaluation

This topic examines sustainability indicators and performance metrics within the fuzzy decision framework. The proposed method supports long-term energy planning by incorporating environmental impact, resource utilization, and cost effectiveness.

6. Future Research Directions and Applications

The final topic outlines future research potential, including the integration of artificial intelligence, real-time data analytics, and large-scale energy systems. The framework can be extended to smart grids, hybrid renewable networks, and policy-driven energy optimization models.

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