Energy Optimization for Smart Grids Using Reinforcement Learning

The growing demand for efficient and sustainable energy distribution has led to the need for intelligent grid management systems. Traditional grid optimization techniques face challenges in handling dynamic energy supply, demand fluctuations, and the integration of renewable sources. This paper introduces an AI-powered approach to optimize energy distribution in smart grids using Reinforcement Learning (RL). The proposed model employs an RL-based agent that continuously learns optimal energy allocation strategies by interacting with the grid environment. The agent is trained to maintain a balance between energy supply, demand, and storage while minimizing losses and ensuring grid stability. Through simulations, we assess the effectiveness of the proposed approach in comparison to conventional grid management methods. The results demonstrate that the RL-based system enhances energy efficiency, reduces operational costs, and improves the overall resilience of smart grids. This research underscores the potential of AI-driven solutions in transforming modern power systems and lays the groundwork for future advancements in adaptive energy management.