Microgrid Congestion Management Using Swarm Intelligence Algorithm

Microgrids have emerged as promising solution to address the challenges of modern power systems, offering increased reliability, efficiency, and integration of renewable energy sources. However, the efficient management of power flow within microgrids is crucial to maintain stability and prevent congestion issues. This study focuses on employing a Swarm Intelligence Algorithm, specifically Particle Swarm Optimization (PSO), for optimizing power flow and managing congestion within a microgrid in Cape Formoso Island in Brass Local Government Area, Bayelsa State, Nigeria. The research investigates the application of PSO in optimizing power flow by dynamically reconfiguring the distribution of power among various distributed energy resources (DERs) within the microgrid. The PSO algorithm is utilized to find the optimal settings for power generation, load distribution, and energy storage allocation to alleviate congestion and improve the overall performance of the microgrid. PSO's ability to iteratively search for optimal solutions is leveraged to minimize power losses, maintain voltage stability, and mitigate congestion while considering the variability of renewable energy sources and fluctuating demand. Simulation results demonstrate the effectiveness of the PSO-based optimization approach in managing congestion within the microgrid. This research contributes to the advancement of optimization techniques for microgrid management, offering insights into the practical application of PSO algorithms for congestion management, paving the way for more resilient and sustainable energy systems. Keywords - Microgrid, Congestion management, Swarm intelligence, Particle swarm optimization (PSO), Distributed energy resources (DERs).