AI-Driven Energy Management in Smart Microgrids with Renewable Integration Using Deep Reinforcement Learning

The intermittent nature of solar and wind generation and the dynamic load fluctuations make optimal energy management in renewable integrated smart microgrids a complex stochastic optimization problem. Traditional rule-based and model predictive control (MPC) methods are based on accurate system models and cannot adapt to real-time uncertainty. This paper proposes a Deep Q-Network (DQN) based energy management system (EMS) for an islanded microgrid consisting of a 10-kW solar PV array, 5 kW wind turbine and 20 kWh lithium-ion battery storage, supplying a mixed residential-commercial load. The DQN agent is trained on a Simulink-based microgrid environment using real data of irradiance and wind speed from Nagpur, India. The experimental results demonstrate that the proposed DQN-EMS reduces the daily operational cost by 52.2% and 35.7% and CO2 emissions by 56.8% and 41.9% compared to rule-based control and MPC, respectively. The DQN agent converges in 350 training episodes and keeps the battery state-of-charge (SOC) within safe limits (20%–80%) for 98.4% of the operating hours.