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This letter investigates a Branching Dueling Q-Network (BDQ) based onlineoperation strategy for a microgrid with distributed battery energy storagesystems (BESSs) operating under uncertainties. The developed deep reinforcementlearning (DRL) based microgrid online optimization strategy can achieve alinear increase in the number of neural network outputs with the number ofdistributed BESSs, which overcomes the curse of dimensionality caused by thecharge and discharge decisions of multiple BESSs. Numerical simulationsvalidate the effectiveness of the proposed method.

Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems