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-co...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.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51977081)the Natural Science Foundation of Guangdong Province(No.2022A1515011193).
文摘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.
