Reinforcement Neural Network-Based Grid-Integrated PV Control and Battery Management System

Salah Mahdi Thajeel () and Doğu Çağdaş Atilla
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Salah Mahdi Thajeel: Department of Electrical and Computer Engineering, Altınbaş University, Istanbul 34218, Turkey
Doğu Çağdaş Atilla: Department of Electrical and Computer Engineering, Altınbaş University, Istanbul 34218, Turkey

Energies, 2025, vol. 18, issue 3, 1-20

Abstract: A 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.

Keywords: maximum power point tracking (MPPT); photovoltaic (PV) systems; fuzzy logic controllers (FLCs) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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