An Overview of Current Optimization Approaches for Hybrid Energy Systems Combining Solar Photovoltaic and Wind Technologies

Abstract

This study reviews recent developments in optimization techniques for hybrid solar photovoltaic and wind energy systems, particularly those using artificial intelligence (AI) and hybrid algorithms. Due to the global need for sustainable energy, the study compares both traditional and modern optimization techniques. It shows that hybrid algorithms, like, Gray Wolf–Cuckoo Search Optimization (GWCSO), can speed up convergence and reduce costs by up to 25% compared with other conventional methods, such as linear programming. The study groups optimization techniques into traditional, software-based, AI-driven, and hybrid approaches; assessing how well they improve system efficiency, reliability, and cost. It also outlines sizing methods and their economic, technical, and environmental effects, with results showing that AI-driven methods can lower the levelized cost of energy by 10%–15% in complex microgrids (MGs). The study further provides a structured way to size MGs, addressing a gap in optimization methods for independent hybrid systems in remote locations. Greater flexibility of hybrid algorithms in handling complex optimization problems was emphasized. Ultimately, this study offers new insights into combining AI with traditional methods, suggesting future research directions for both smart grid and MG design.