Renewable Energy Systems Design with Soft Computing and Evolutionary Algorithms

Renewable energy systems play a vital role in addressing global energy demands and reducing environmental impacts through the use of sustainable resources like solar and wind. However, their efficiency and reliability are often challenged by the variability and uncertainty of these resources, leading to difficulties in system optimization and decision-making. This research addresses these limitations by employing fuzzy logic to manage uncertainties and evolutionary algorithms to optimize system configurations and performance. Fuzzy logic provides a robust framework for handling imprecise data, enabling adaptive decision-making, while evolutionary algorithms efficiently explore design spaces to identify optimal solutions. The proposed methodology is validated through case studies involving solar and hybrid energy systems, showcasing significant improvements in energy efficiency, reliability, and costeffectiveness. This work offers a scalable and systematic approach to overcoming the challenges in renewable energy system design, contributing to the development of sustainable and resilient energy solutions.