Enhancing Renewable Energy System Efficiency Through Optimization Techniques: A Scientometric Review

The research explores methods to improve renewable energy systems by enhancing performance, reducing costs, and supporting global energy sustainability. Rising demand for solar, wind, and biomass energy necessitates system optimization for output, environmental cost reduction, and efficiency. The research studies optimization strategies for Hybrid Renewable Energy Systems (HRES) to assess effects on productivity and economic advantages. This study used scientometric analysis on literature from the Scopus database from 2015 to 2025. VOS viewer and Bibliometrix tools examined keyword changes and co-authorship connections. Research shows energy output efficiency and system reliability improved using optimization methods like Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and Artificial Neural Networks (ANNs). Power systems using different renewable sources create synergies that boost production and stabilize intermittent power. The research demonstrated the essential role of energy storage systems for better stability. This study enhances knowledge about optimization technologies for renewable energy solutions for off-grid power generation. The outcomes emphasize the necessity of innovations and international teamwork to enhance renewable energy system practicality.