JEETAS_VOLUME_1_ISSUE_2_NOVEMBER_2023_Amasa_Ukwuoma_Emmanuel_Ayebatorudigite

Microgrid Congestion Management Using Swarm Intelligence

Algorithm

Amasa Ukwuoma Emmanuel, Ayebatorudigite Friend James

Department of Electrical and Electronic Engineering, Federal University Otuoke, Nigeria

Email: amasaeu@fuotuoke.edu.ng ----------------------------------------************************---------------------------------

Abstract:

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).