Hybrid optimization for power flow management in microgrids with renewable energy sources

Abstract

Effective power flow (PF) management is crucial for microgrids (MGs) aiming to optimize costs, leverage renewable energy (RE), and maintain system stability. This research introduces a hybrid strategy using a Pelican Optimization Algorithm (POA) and Walrus Optimizer (WO) combined into the Walrus-POA (WPOA) to manage PF in MGs with hybrid RE sources (HRES). By regulating voltage source inverter (VSI) signals and considering variations in active power (AP) and reactive power (RP), the proposed model addresses power exchange discrepancies between sources and loads through a multi-objective function. This approach enhances power controller parameters, ensuring reliable energy supply, reducing central grid dependence, and facilitating smooth transitions between grid-connected as well as islanded modes. MATLAB/Simulink implementation shows the technique’s effectiveness, achieving a 38.81% cost minimization as well as a 21% lessening in air pollution, compared to existing methods. WPOA achieves the lowest mean 0.9323, median 0.9187, and standard deviation (SD) 0.0936, outperforming Salp Particle Swarm Algorithm (SPSA), Particle Swarm Optimization (PSO), Enhanced Jellyfish Search (EJS), Wind Driven Optimization (WDO), and Dwarf Mongoose Optimization (DMO) in consistency and optimization performance. It also attains the highest efficiency at 97.2%, surpassing all existing methods and enhancing PF management in MGs.