AI-Based Smart Grid Optimization for Renewable Energy Integration: A Review

Abstract

The rising demand for sustainable energy solutions has resulted in a rapid growth in the use of renewable energy sources such as solar and wind power. However, the intermittent and uncertainty of these energy sources creates great difficulties for holding stability and efficiency in conventional power grids. Smart grid technology has come as a potential solution to overcome these challenges by incorporating advanced issuing communication, monitoring and control mechanisms into modern electricity networks. In recent years, artificial intelligence (AI) techniques have been widely used for the purpose of enhancing the efficiency and reliability of smart grid systems. Machine learning and deep learning algorithms allow for accurate prediction of renewable energy generation, load demand prediction, and intelligent energy management. This paper presents a detailed review of AI based techniques used for optimizing integration of renewable energy in smart grids. Various machine learning models like Long Short Term Memory (LSTM), Convolution Network (CNN), Random Forest models, and hybrid deep learning models are analyzed with regard to their applications in renewable energy prediction and optimizing the grid. Furthermore, the present challenges are highlighted, research gaps are and possible future directions of the development of smart grid systems that are intelligent.