REAL-TIME OPTIMIZATION OF SOLAR PV INTEGRATED SMART GRID USING PREDICTIVE LOAD MANAGEMENT AND ADAPTIVE INVERTER CONTROL

Abstract

A real-time optimization model for the solar PV-integrated smart grid is presented in this paper by combining predictive load management along with adaptive inverter control. With the increasing use of solar energy in the modern power system, its changing nature as well as grid stability must be managed carefully. These issues are addressed in the proposed system by forecasting short-term load demand with the moving average method and adjusting inverter output based on the predicted load as well as the real-time grid voltage level. A MATLAB-based simulation was created to model solar irradiance, temperature, actual along with predicted load, grid voltage changes, and inverter behavior. The adaptive inverter control algorithm reacts to voltage changes, preventing over-voltage as well as under-voltage conditions, while matching power output with the forecasted load. The simulation results show better power balancing, lower energy mismatch, along with improved grid reliability. The results confirm that using predictive as well as adaptive techniques in the real-time framework improves performance more than traditional fixed-response systems. A scalable as well as practical solution for the smart grid with high solar PV use is provided in this research. Future work may include machine learning for better forecasting along with battery storage for backup energy and further improvement.