A novel distributed reinforcement learning(DRL)strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid.Firstly, a reward function considering both equal pro...A novel distributed reinforcement learning(DRL)strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid.Firstly, a reward function considering both equal proportional current sharing and cooperative voltage restoration is defined for each local agent. The global reward of the whole DC microgrid which is the sum of the local rewards is regarged as the optimization objective for DRL. Secondly,by using the distributed consensus method, the predefined pinning consensus value that will maximize the global reward is obtained. An adaptive updating method is proposed to ensure stability of the above pinning consensus method under uncertain communication. Finally, the proposed DRL is implemented along with the synchronization seeking process of the pinning reward, to maximize the global reward and achieve an optimal solution for a DC microgrid. Simulation studies with a typical DC microgrid demonstrate that the proposed DRL is computationally efficient and able toprovide an optimal solution even when the communication topology changes.展开更多
基金supported by National Key Research and Development Program of China(No.2016YFB0900105)
文摘A novel distributed reinforcement learning(DRL)strategy is proposed in this study to coordinate current sharing and voltage restoration in an islanded DC microgrid.Firstly, a reward function considering both equal proportional current sharing and cooperative voltage restoration is defined for each local agent. The global reward of the whole DC microgrid which is the sum of the local rewards is regarged as the optimization objective for DRL. Secondly,by using the distributed consensus method, the predefined pinning consensus value that will maximize the global reward is obtained. An adaptive updating method is proposed to ensure stability of the above pinning consensus method under uncertain communication. Finally, the proposed DRL is implemented along with the synchronization seeking process of the pinning reward, to maximize the global reward and achieve an optimal solution for a DC microgrid. Simulation studies with a typical DC microgrid demonstrate that the proposed DRL is computationally efficient and able toprovide an optimal solution even when the communication topology changes.
