Towards a Hybrid Arithmetic Smell Agent Optimization Technique for Stability Control of Microgrid System

The persistent need to control load disturbances and instability on power systems to various loads, especially in developing countries like Nigeria, is overwhelming. In an autonomous microgrid, load variation induces disturbance with a magnitude capable of causing power flow instability. These instabilities can result in power equipment damage and subsequently power outage in some designated consumer loads. To address these challenges, a hybrid optimization algorithm strategy is proposed to maintain the microgrid stability and sustain power quality deployment. A hybrid of arithmetic optimization algorithm and smell agent optimization, called the Arithmetic Smell Agent Optimization (ASAO), is conceptualized in this study. The improved algorithm can be used to design an efficient control strategy for coordinating the operation of microgrid systems.