An Implicit Q‐Learning‐Based Safety‐Aware Energy Management System for Multisource Microgrid Operation With Safety Evaluation

The increasing adoption of renewable energy sources introduces significant variability and operational uncertainty in microgrid systems. This study presents an energy management system (EMS) technique for microgrid operation utilizing real microgrid data which is intelligent and demonstrates effectiveness within the evaluated experimental setup. The system employs an augmented reinforcement learning (RL)‐implicit Q‐learning (IQL) agent that learns optimal control policies from offline data with multihorizon forecasting of photovoltaic (PV) generation and microgrid power demand. The agent coordinate battery (BAT) dispatch, fuel cell (FC), and grid exchange (GE) while implicitly following the operational constraints. The system was developed as a continuous safety‐aware EMS architecture, which incorporates with physics‐constrained control, stress‐adaptive dispatch moderation, adaptive BAT participation strategy, and a large language model (LLM)‐based explainability layer. The robustness under stress condition, PV disturbance, component failure, island event, and voltage–frequency disturbance is evaluated to explore system behavior. The proposed hybrid EMS achieves improved stability preservation, enhanced adaptive dispatch coordination with safety‐constrained RL feasibility. Performance of the system is evaluated against baseline technique. However, certain limitations are observed, including sensitivity to extreme demand conditions and dependence on predefined BAT constraints. Overall, the proposed EMS framework designed to improve power balancing, grid interaction, reduced grid dependency and system robustness under both normal and stressed operating conditions. The system demonstrates a balanced trade‐off between adaptability and operational reliability, highlighting the potential of hybrid RL‐based EMS designs for resilient microgrid management.