Optimized Control of Distributed Generation for Grid Stability and Power Quality Improvement

The incorporation of distributed generation (DG) into contemporary distribution networks is rapidly progressing, primarily propelled by the adoption of renewable energy sources. DG makes things more sustainable and resilient, but its intermittent and inverter-based nature makes it hard to keep voltage stable, frequency regulated, and power quality high. This paper presents an optimized control framework that integrates hierarchical control with Model Predictive Control (MPC) and artificial intelligence-driven adaptive tuning to concurrently tackle these challenges. The framework is structured as a multi-objective optimization problem focused on reducing voltage deviation, frequency fluctuations, and harmonic distortion. To test the method, a modified IEEE 33-bus test system with several DG units is modeled in MATLAB/Simulink and PSCAD. When compared to base case operation, conventional droop-based control, and the proposed optimized control, the framework cuts bus voltage deviation by 50%, limits frequency deviations to less than 0.1 Hz with a faster settling time, and cuts Total Harmonic Distortion (THD) by more than 40%, all while still being able to be computed. These results show that the suggested strategy is a complete and scalable way to integrate DG reliably, making sure that smart grids stay stable and that power quality improves as they change.