Yenilenebilir enerji akıllı şebeke nesnelerin internetini tabanlı yönetim ve izleme sistemi tasarımı

Renewable energy sources are receiving more attention as a result of the population's quickening increase and growing worry over global warming. By lowering carbon emissions and generating cheap power, renewable energy sources significantly improve the environment. In comparison to other renewable energy sources, solar energy has lower operating and maintenance expenses and is thus the most widely used renewable energy source. Managing and regulating energy flow in the smart grid is one of the major issues that have to be resolved. Problems with power quality and stability may arise whenever there is a dynamic exchange of energy in a high-power system between different sources, loads, and energy storage devices. Energy flow through the system must be continuously managed in order to satisfy the load demand. The biggest problem with the smart grid's operation is the lack of technical details on the hardware and experimental setup of the energy storage system. This study assesses the power balance in a small-scale experimental SG under various conditions. To achieve Maximum PowerPoint Tracking (MPPT), the PV system efficiency in this work progressively uses artificial intelligence-based techniques. Additionally, this work involves wind turbine MPPT. By employing the Grey Wolf Optimizer GWO algorithm, which is based on the artificial intelligent approach used to achieve MPPT, to optimize the efficiency of the PV-wind turbine-battery system, the goal of this study is to enhance the power quality and energy management system of the PV-wind turbine-battery module. In order to strengthen system dependability, the IoT may also be coupled with the smart grid. In this scenario, the IoT can control load demand if the load increases more than the renewable energy sources over an extended length of time.