Advanced Energy Management System for Renewable Based Microgrids Using Optimization and Intelligent Control Techniques
Keywords:
AI-driven EMS, microgrids, renewable energy integration, load balancing, machine learningAbstract
An advanced Energy Management System (EMS) for renewable-based microgrids is presented using optimization techniques integrated with Artificial Neural Networks (ANN). The proposed system aims to efficiently coordinate distributed energy resources such as photovoltaic (PV) systems, wind turbines, battery energy storage systems (BESS), and grid interaction to ensure reliable and economical operation. ANN is employed for accurate load and generation forecasting, while optimization algorithms are used to minimize operational cost, power losses, and emissions under varying environmental and load conditions. The intelligent control strategy dynamically manages energy flow, maintains power balance, and enhances system stability. Simulation results demonstrate improved efficiency, reduced dependency on the main grid, and enhanced power quality compared to conventional EMS approaches. The proposed methodology offers a scalable and robust solution for modern smart microgrids with high renewable penetration.
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