SQP based Predictive Controller for Grid Parameters Optimization in Distributed Generation System

The electrical grid is a complex system designed to balance electricity supply and demand, ensuring consistent power delivery to consumers. With the rising demand for energy and widespread adoption of renewable energy systems (RES), solar energy has emerged as a pivotal contributor to sustainable power generation. Integrating solar panels into the grid offers significant environmental benefits but also poses challenges due to the intermittent nature of solar irradiance. These fluctuations can cause voltage and current inconsistencies, potentially impacting grid stability and power quality. Furthermore, the presence of nonlinear loads intensifies the issue by introducing frequency variations, making effective grid management a pressing necessity.To address these challenges, the present work aims to enhance grid performance through a Predictive Controller integrated with Sequential Quadratic Programming (SQP) optimization. This innovative approach dynamically adjusts system parameters in real time, compensating for fluctuations caused by varying solar irradiance and load conditions. By forecasting grid behaviour and optimizing control actions, the method ensures consistent and reliable power delivery. The project focuses on minimizing Total Harmonic Distortion (THD), current and voltage inconsistencies, enabling seamless solar integration into the grid while maintaining stability and power quality. Thus, the approach ultimately strengthens the grid's sustainability and resilience, supporting a clean energy future without sacrificing reliability or power quality for consumers.