Investigating the Function of AI in Improving Distribution Networks for Renewable Energy

Abstract

As the usage of renewable energy sources such as solar, wind, and hydropower grows, energy distribution networks face both opportunities and challenges. Renewable and environmentally friendly as they are, these energy sources can be difficult to integrate into the grid because to their dispersed and intermittent nature. Artificial intelligence (AI) offers innovative solutions for enhancing renewable energy distribution networks, giving a new tool in the battle against these issues. By enabling real-time monitoring, forecasting, and decision-making, artificial intelligence (AI) technologies such as predictive modeling, data analytics, and machine learning can enhance the management of decentralized energy systems. the ways in which AI has the potential to improve the distribution of renewable energy by enhancing demand responsiveness, lowering energy loss, strengthening the grid, and maximizing the utilization of distributed energy resources (DERs). The integration of AI into renewable energy systems has the potential to increase their adaptability, reliability, and cost-effectiveness; thus, contributing to a smarter and more sustainable power grid. emphasizes, via talks and case examples, the policy, economic, and technical considerations that must be met in order to use AI with the current grid infrastructure. The results show that AI is crucial for renewable energy distribution networks to become more efficient, resilient, and capable of meeting the growing demand for clean energy.