Towards optimal control of a hybrid system with Storage for efficient management of electrical energy

Abstract

This research focuses on enhancing the efficiency, reliability, and performance

of renewable energy systems, particularly in standalone and hybrid configurations. Using ad- vanced control strategies, including Artificial Neural Networks (ANN) and Maximum Power

Point Tracking (MPPT) techniques, the study addresses key challenges in energy manage- ment, storage optimization, and system sizing. The research develops intelligent energy

management strategies with ANN to optimize energy storage in hybrid setups, ensuring ef-ficient energy flow and system performance. It also compares various MPPT techniques to

identify the most effective method for maximizing power output in photovoltaic (PV) sys- tems under changing environmental conditions. The study further explores the integration

of multiple renewable energy sources, like PV and wind, in hybrid systems, investigating control and sizing strategies to ensure stable and cost-effective operation. The proposed

strategies are evaluated through MATLAB/Simulink simulations and validated with exper- imental data, demonstrating their applicability in real-world scenarios.