Thajeel, Salah MahdiAtilla, Doğu Çağdaş2025-05-052025-05-052025Thajeel, S. M., & Atilla, D. Ç. (2025). Reinforcement Neural Network-Based Grid-Integrated PV Control and Battery Management System. Energies, 18(3), 637.1996-1073https://hdl.handle.net/20.500.12939/5738A reinforcement neural network-based grid-integrated photovoltaic (PV) system with a battery management system (BMS) was developed to enhance the efficiency and reliability of renewable energy systems. In such a setup, the PV system generates electricity, which can be used immediately, stored in batteries, or fed into the grid. The challenge lies in dynamically optimizing the power flow between these components to minimize energy costs, maximize the use of renewable energy, and maintain grid stability. Reinforcement learning (RL) combined with NNs offers a powerful solution by enabling the system to learn and adapt its energy management strategy in real time. By using the proposed techniques, the convergence time was decreased with lower complexity compared with existing approaches. The RL agent interacts with the environment (i.e., the grid, PV system, and battery), continuously improving its decisions regarding when to store energy, draw from the battery, and supply power to the grid. This intelligent control approach ensures optimal performance, contributing to a more sustainable and resilient energy system.eninfo:eu-repo/semantics/openAccessfuzzy logic controllers (FLCs)maximum power point tracking (MPPT)photovoltaic (PV) systemsArticle10.3390/en180306371832-s2.0-85217543546WOS:001418538600001Q3