Q-Learning Based Energy Management for Microgrids: Internalizing SOC Constraints for Fast Online Inference

Abstract

While traditional day-ahead economic scheduling provides a foundational baseline for microgrid energy management, the inevitable transition toward real-time, dynamic control necessitates algorithms with extreme computational efficiency. This paper presents a microgrid optimization strategy predicated on the Q-learning reinforcement learning (RL) algorithm. The multi-energy scheduling problem is formulated as a Markov Decision Process (MDP), wherein strict physical boundaries—specifically battery State of Charge (SOC) limits—are directly internalized via a tailored reward function. Operating on a decoupled "offline training and online inference" paradigm, the RL agent is evaluated under a baseline day-ahead framework to verify its global optimization capabilities. Comparative simulations against Particle Swarm Optimization (PSO) demonstrate a 27.8% enhancement in economic profitability (yielding a minimum cost of -$369.39). Crucially, the RL approach achieves an ultra-low online inference latency of merely 0.0018 s. By utilizing the day-ahead model purely as an economic benchmark, this research validates that the proposed RL paradigm not only guarantees optimal dispatch but also fundamentally shatters the computational bottlenecks of heuristic algorithms, establishing a critical algorithmic foundation for high-frequency hardware-in-the-loop simulations and multi-agent real-time coordination.