Optimized Real-Time Energy Management and Neural Network-Based Control for Photovoltaic-Integrated Hybrid Uninterruptible Power Supply Systems

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Abstract
The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets.

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