Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review

Abstract

Hybrid energy systems (HES) provide an effective solution to the growing global energy demand while addressing the limitations of conventional sources and environmental challenges. By integrating renewable and conventional energy sources, these systems enhance reliability, reduce costs, and improve efficiency. However, the variability of renewable resources such as solar and wind makes HES design more complex. This paper explores various design and sizing methods for HES, focusing on combining clean sources, including wind and solar, with conventional energy options. Through advanced optimization techniques, including artificial intelligence (AI), the study demonstrates how AI can identify optimal configurations to ensure system reliability while minimizing costs. The paper also highlights the crucial role of HES in providing energy to remote and underserved areas with limited access. This work serves as a comprehensive introduction for researchers and engineers interested in HES sizing, offering insights into technical challenges and optimization strategies, and contributing to the advancement of sustainable energy systems.

Keywords

• hybrid energy systems
• advanced optimization algorithms
• optimal sizing
• photovoltaic
• wind energy