Energy-Efficient Reinforcement Learning for Intelligent Resource Allocation in Smart Grid Systems
Abstract
The growing integration of renewable energy resources introduces uncertainty and dynamic demand fluctuations in modern smart grids. Efficient resource allocation is essential for maximizing energy utilization while maintaining grid stability. This research introduces a reinforcement learning-based optimization framework that dynamically allocates energy resources using Deep Q-Networks (DQN) combined with predictive demand forecasting models. The proposed system continuously learns optimal scheduling policies from real-time energy consumption patterns, renewable generation forecasts, and storage availability. Simulation results indicate substantial reductions in energy wastage, operational costs, and carbon emissions while improving grid reliability. Comparative analysis with traditional optimization techniques demonstrates the effectiveness of reinforcement learning in handling highly dynamic energy environments. The proposed framework supports sustainable and intelligent energy management for future smart cities.
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