Swarm Intelligence techniques for optimization of Microgrid with Renewable Energy System

Recently, clean power generation has received a lot of attention in modern power systems as a means to provide sustainability and high flexibility in the power industry. Due to this, modern energy systems are now reliant on Microgrids (MGs), which can easily access and operate Renewable Energy Systems (RES) and other Distributed Generations. The use of Microgrids plays a vital role in implementing clean and renewable energy. This will enhance energy security, making considerable financial savings, and lowering greenhouse gas emissions. In this paper, a grid-connected Microgrid system is considered as test model that includes solar photovoltaic (PV), wind turbine (WT), micro gas turbine (MT), fuel cell (FC), and battery energy storage system (BESS). The developed system is presented as a multi-objective function with constraints that can be resolved by a significant optimization method. Various commonly used Swarm Intelligence (SI) based meta-heuristic optimization techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), Bacteria Forging Optimization (BFO), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Cuckoo Search (CS), Bat Algorithm (BA), Firefly Algorithm (FFA), Krill Herd (KH), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) can be used to optimize above stochastic multi-objective problem of Microgrid. To assist researchers in selecting the best optimization approaches for their study, a comparison of above-mentioned SI based optimization techniques is provided in this paper. The discussion also includes upcoming research issues for Microgrid operation optimization.