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

•Hybrid WPOA optimizes power flow in MGs with renewable energy integration.•Multi-objective model improves active/reactive power balance and control.•Achieves 38.81% cost cut and 21% pollution drop over traditional methods.•WPOA outperforms SPSA, PSO, EJS, WDO, and DMO in stability and accuracy.•MAT...

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Vydáno v:Solar energy Ročník 301; s. 113902
Hlavní autor: Rajendar, G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.11.2025
Témata:
Grid
Power Controller
Power Demand
Power Flow Management
Renewable Energy Sources
Voltage Source Inverter
Power Controller
Voltage Source Inverter
Renewable Energy Sources
Power Flow Management
Grid
Power Demand
ISSN:0038-092X
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Shrnutí:•Hybrid WPOA optimizes power flow in MGs with renewable energy integration.•Multi-objective model improves active/reactive power balance and control.•Achieves 38.81% cost cut and 21% pollution drop over traditional methods.•WPOA outperforms SPSA, PSO, EJS, WDO, and DMO in stability and accuracy.•MATLAB/Simulink results show 97.2% efficiency and enhanced PF management. 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.
ISSN:0038-092X
DOI:10.1016/j.solener.2025.113902