Dynamic Stability Improvement for Interconnected Solar PV-Based Microgrid Networks Using GA-Optimized Adaptive Droop Control and Tie-Line Support

Abstract

The increasing integration of solar Photovoltaic (PV) microgrids into modern power systems has introduced challenges in maintaining dynamic stability, especially when multiple microgrids are interconnected to form clusters. These challenges include frequency and voltage fluctuations, poor power sharing, and inter-area oscillations due to the intermittent nature of solar energy and low inertia of inverter-based systems. This study aims to enhance the dynamic stability of clustered solar PV microgrids using a Genetic Algorithm (GA)-optimized adaptive droop control strategy combined with tie-line stabilization. The main contribution of this project is the integration of GA-optimized adaptive droop control with active tie-line stabilization in interconnected solar PV microgrid clusters. The proposed method significantly improved microgrid stability and performance. The optimized parameters obtained were ( ) and ( ), which enhanced voltage and frequency regulation, reduced oscillations, and improved power sharing performance. Simulation results showed faster settling time, stable tie-line power flow, and improved synchronization between microgrids. Overall, the proposed system achieved approximately 70–85% improvement in stability, 80% reduction in oscillations, and about 75% improvement in power sharing accuracy, confirming the effectiveness of the proposed control strategy.