Artificial Intelligence-Based Energy Management and Control Strategies for Renewable-Powered Smart Microgrids Under Dynamic Operating Conditions

This paper presents an artificial intelligence (AI)-based energy management and control framework for renewable-powered smart microgrids operating under dynamic conditions. The proposed system integrates photovoltaic (PV) generation, battery energy storage, and grid interaction within a MATLAB/Simulink-R2024B environment to improve operational reliability, energy efficiency, and renewable energy utilization. Seven control scenarios were investigated, including baseline operation, Rule-Based Energy Management System (EMS), Proportional–Integral–Derivative (PID), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN)-assisted forecasting, and Reinforcement Learning (RL)-based optimization. Comparative results demonstrate progressive performance improvements with increasing controller intelligence. The RL-based EMS achieved the highest operational cost reduction (95%), voltage regulation performance (95%), battery state-of-charge management (95%), renewable energy utilization (92%), grid dependency reduction (92%), overall system efficiency (95%), and an overall performance score of 95.4%. The ANN forecasting model attained a forecasting accuracy of 96.2%, corresponding to a Mean Absolute Percentage Error (MAPE) of 3.8%, while achieving a Mean Absolute Error (MAE) of 1.84 kW, Root Mean Square Error (RMSE) of 2.37 kW, and coefficient of determination (R2) of 0.982. For voltage regulation, the RL controller reduced the RMSE, settling time, and overshoot to 1.50 V, 1.8 s, and 0.5%, respectively, compared with 20.0 V, 15.0 s, and 10.0% for the baseline case. Ultimately, the results demonstrate that AI-driven control strategies substantially enhance microgrid stability, battery utilization, renewable energy penetration, and operational efficiency, providing a practical and scalable solution for next-generation intelligent microgrids.