Optimal Voltage Regulation in Standalone Photovoltaic Systems Using Model Predictive Control and MOGA

This research presents a novel approach to optimal voltage regulation in standalone photovoltaic (PV) systems using Model Predictive Control (MPC) combined with Multi-Objective Genetic Algorithms (MOGA). Standalone PV systems are crucial for providing sustainable energy in remote areas, but their performance can be significantly hindered by voltage instability due to fluctuations in solar irradiance and load demand. The proposed method leverages MPC for real-time voltage prediction, allowing the system to preemptively adjust its control actions to maintain voltage levels within optimal ranges. MOGA is employed to fine-tune the control parameters, ensuring that the system balances multiple conflicting objectives such as voltage stability, power efficiency, and energy loss minimization. By integrating these two advanced control techniques, the study achieves a highly adaptive and robust voltage regulation system that optimizes the performance of standalone PV systems under dynamic operating conditions. Simulation results demonstrate the effectiveness of the approach, showing improved voltage stability, enhanced power tracking efficiency, and significant reductions in energy losses compared to conventional control methods. The use of MOGA further ensures that the solution is not only optimal in terms of performance but also flexible in adapting to different system requirements. This research highlights the potential of combining predictive control with evolutionary algorithms to address the complex challenges of voltage regulation in renewable energy systems, paving the way for more reliable and efficient standalone PV installations. Future work could explore the integration of this framework into larger hybrid renewable energy systems and investigate its scalability for real-world applications.