A Review of the Application of Swarm Intelligence-Based Algorithms for Optimization in Microgrids

The unique features of swarm intelligence algorithms have led to their use in solving complex and diverse problems in various fields. These algorithms are widely used as a powerful tool in artificial intelligence and computational science. In this review, the capabilities of swarm intelligence-based algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and fish swarm algorithm (FSA) for optimizing the performance of microgrids are examined. First, the concepts of microgrids and the introduction of each swarm intelligence-based algorithm are presented. Then, the advantages and disadvantages of the application of the algorithms are stated. The application of the algorithms in various topics such as energy management, protection, loss reduction, and virtual impedance in microgrids is stated. Finally, the existing challenges and future research directions are mentioned. This study can be a foundation for future research that uses the capabilities of swarm intelligence to solve real-world challenges in various fields of energy systems.