Optimizing Renewable Energy Distribution in Smart Microgrids Using IoT and Reinforcement Learning

Smart microgrids improve energy efficiency by integrating renewable energy sources, but their intermittent nature complicates steady power distribution. This research introduces an IoT-enabled Reinforcement Learning (RL) framework for optimizing renewable energy distribution in smart microgrids. IoT sensors collect real-time data on energy production, use, and environmental variables, enabling adaptive management. The RL model adaptively acquires optimum energy distribution techniques to attain load equilibrium, energy storage enhancement, and grid stability. The system uses autonomous decision-making to reduce energy waste, increase cost efficiency, and enhance resilience. The proposed system facilitates real-time demand response, optimizes power distribution, and assures effective use of distributed energy resources. Experimental findings illustrate the efficacy of RL in mitigating uncertainty in energy supply and demand. This research advances the development of intelligent, self-adaptive microgrids, enabling a more sustainable and efficient energy infrastructure. Previous studies will investigate hybrid AI models to enhance performance further.