Metaheuristic-Tuned Droop Control for PV-Based DC Microgrid Optimization Under Dynamic Load Conditions

Sustainable energy solutions from photovoltaic (PV)-based direct current (DC) microgrids (MG) encounter issues in maintaining voltage stability and power-sharing efficiency during dynamic demand fluctuation. System instability and poor performance are caused by traditional droop control methods, which rely on predetermined parameters and are unable to handle fluctuating loads. Due to dynamic load circumstances, DC microgrids powered by PV may experience voltage fluctuations, power imbalances, and significant power losses. Traditional droop control is unstable and inefficient because it cannot make real-time corrections. Microgrid stability requires constant droop optimization. Metaheuristic-Tuned Droop Control (MTDC) uses the Self-Adaptive Particle Swarm Optimization (SA-PSO) technique to calculate the dynamic droop coefficient. With an inertia weight adjustment method, SA-PSO improves convergence and flexibility. Optimization goals include minimizing power loss, enhancing power-sharing accuracy, and mitigating voltage fluctuations. This study simulates the proposed approach using MATLAB/Simulink under various stress conditions to verify its effectiveness. Unlike traditional droop control, MTDC minimizes voltage fluctuation, improves power-sharing accuracy, and lowers power losses. Metaheuristic optimization improves PV-based DC microgrid control. Systems become more stable, respond faster, and withstand disturbances. According to this research, adaptive droop control can enhance the efficiency and reliability of microgrids powered by renewable energy.