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

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Abstract
This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. The developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.
Funding Information
  • U.S. National Science Foundation (NSF) ECCS Awards (1809458, 2033910)
  • CURENT which is an Engineering Research Center
  • NSF and U.S. Department of Energy (DOE) through NSF Award (EEC-1041877)
  • U.S. DOE, Office of Energy Efficiency and Renewable Energy and Office of Electricity (DEAC05-00OR22725)