A Hybrid Arithmetic Smell Agent Optimization Algorithm for Stability Control of Micro-Grid Systems

Résumé

Microgrid systems play a vital role in meeting local energy demands, particularly in the presence of unpredictable load variations. However, maintaining system stability under such conditions remains a key challenge. Despite several works implementing optimized control strategies, they are limited in their ability to offer robust solutions for addressing the complex interplay of power quality, stability, efficiency, and load variations in microgrids. This study addresses that limitation by presenting a hybrid Arithmetic Smell Agent Optimization (ASAO) algorithm, designed to optimize microgrid control performance by effectively tuning Proportional–Integral–Derivative (PID) parameters. A Simulink model was developed, incorporating solar Photovoltaic (PV) generation, battery energy storage, and grid interconnection, to simulate microgrid behavior. The control system adopted a two-level hierarchy, with a primary controller for rapid disturbance correction and a secondary controller for long-term stability. The primary and secondary controllers demonstrated rapid stabilization, with rise times of 4 ms and 50 ms, and settling times of 4.1 ms and 75 ms, respectively. This work provides a reliable foundation for the design of intelligent control systems in modern microgrids, offering scalable and efficient solutions for sustainable power delivery.

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