Hybrid Intelligent Optimization Techniques for Grid Integration with Renewable Systems: Review

Abstract

Renewable energy sources are essential in fulfilling the increasing need for electricity. Researchers are actively exploring eco-friendly alternative energy sources and technologies, particularly in the form of micro grids or gridintegrated systems. A hybrid renewable energy system with battery storage in a small-scale industry was optimized using a blend of traditional and cutting-edge models, employing mixed integer linear programming techniques, as demonstrated in a recent study. The proposed optimization algorithm offers improved accuracy and reduced computational burdens. The model considers the intrinsic stochastic nature of hybrid energy systems and integrates fluctuations in load forecasting. By employing an intelligent computational optimization algorithm, the study focuses on optimizing the PV-Wind, Diesel, and battery storage hybrid system. The findings of this research shed light on the impact of load variations on component sizing in small-scale industries. This review holds significant value for researchers who aim to tackle the intricacies of algorithm analysis and power system design in order to drive future enhancements.