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

被引:0
|
作者
Thajeel, Salah Mahdi [1 ]
Atilla, Dogu cagdas [1 ]
机构
[1] Altinbas Univ, Dept Elect & Comp Engn, TR-34218 Istanbul, Turkiye
关键词
maximum power point tracking (MPPT); photovoltaic (PV) systems; fuzzy logic controllers (FLCs); POWER POINT TRACKING; INTELLIGENCE; OPTIMIZATION; ENERGY;
D O I
10.3390/en18030637
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:20
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