Eagle arithmetic optimization algorithm for renewable energy-based load frequency stabilization of power systems

Abstract

Power systems' efficient management and planning are crucial in renewable energy-based systems. As the global electricity demand continues to rise, there is a growing need for alternative energy sources such as solar, wind, and hydropower. Consequently, numerous research studies have focused on maintaining load balancing within the renewable energy system and improving the forecasting of renewable energy resources. This paper presents the Eagle Arithmetic Optimization Algorithm (EAOA) as a novel approach to address these challenges. By utilizing a fuzzy-based dragonfly optimization algorithm (fuzzy-DFOA), the proposed method enhances the accuracy of load-balancing analysis in renewable energy resources. Through its innovative techniques, the EAOA demonstrates its potential to significantly improve the efficiency and effectiveness of managing renewable energy systems, paving the way for a more sustainable and reliable power grid. The accuracy rate of both wind and solar datasets is given. For the wind dataset, our proposed work got 92.63%, SVR got 75.89%, CNN got 87.54%, and QODA got 83.16%. For the solar dataset presented work of fuzzy-based DFOA got 92.59%, SVR got 69.16%, CNN got 86.25%, and QODA got 82.37%.