AI-Driven Scheduling Algorithm for Maximizing Renewable Energy Utilization in Smart Grids
Abstract
Intelligent networks' increasing usage of renewable energy sources presents challenges in the energy balancing demand and supply because of their erratic nature. This work seeks to increase a set of AI-oriented scheduling criteria that guarantee the optimal use of renewables even as it preserves grid stability and optimizes power distribution. The proposed method uses deep reinforcement learning and real-time energy predictions to allocate energy sources dynamically. The algorithm changes programming for this goal and allows for variations in renewable energy generation using historical and real-time data. Experimental results indicate that the AI-captured method greatly promotes the use of renewable energy, lowers reliance on traditional energy sources, and maintains normal grid efficiency. A comparison with traditional scheduling methods confirms the improved performance of the energy rate and the device's dependability. It examines the smart grid age by providing an intelligent and statistics-oriented approach to maximize the Utilization of renewable electricity and boost energy sustainability.