Enhancing Dynamic Stability of Solar PV Micro Grid Clusters Via Genetic Algorithm Optimized Adaptive Droop Control and Tie-line Stabilization

Abstract

Micro grid networks are increasingly adopting solar photovoltaic (PV) systems due to their sustainability and distributed generation advantages. However, the intermittent nature of solar energy caused by varying irradiance and temperature increases significant challenges in maintaining power balance, voltage stability, and frequency control, especially in interconnected micro-grid clusters. Traditional droop control methods, though commonly used for decentralized load sharing, suffer from fixed parameters that do not adapt well to changing conditions. This often leads to power-sharing errors, voltage instability, and degraded overall performance. Additionally, uncoordinated tie-line power exchanges between micro grids can introduce oscillations and stoppage, further affecting system reliability. Addressing these issues, this article introduces an advanced control strategy that combines Adaptive Droop Control (ADC), Genetic Algorithm (GA) optimization, and tie-line stabilization. The GA component ensures optimal real-time tuning of droop parameters based on prevailing system conditions, while ADC dynamically adjusts those parameters to enhance responsiveness to fluctuations. Through GA the power sharing between interconnected micro-grids has been solved by adjusting voltage and frequency to operate in real time adoption regardless of any changes occur.