Parallel Hybrid Deep Reinforcement Learning for Real-Time Energy Management of Microgrid

This paper proposes a novel parallel hybrid deep reinforcement learning (DRL) approach to address the real-time energy management problem for microgrid (MG). As the proposed approach can directly approximate a discrete-continuous hybrid policy, it does not require the discretization of continuous actions like regular DRL approaches, which avoids accuracy degradation and the curse of dimensionality. In addition, a novel experience-sharing-based parallel technique is further developed for the proposed approach to accelerate the training speed and enhance the training robustness. Finally, a safety projection technique is introduced and incorporated into the proposed approach to improve the decision feasibility. Comparative numerical simulations with several existing MG real-time energy management approaches (i.e., myopic policy, model predictive control, and regular DRL approaches) demonstrate the effectiveness and superiority of the proposed approach.