Branching Dueling Q-Network-Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
Hang Shuai, Fangxing Li, Héctor Pulgar-Painemal, Yaosuo Xue
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.
Topics & Concepts
MicrogridCurse of dimensionalityComputer scienceReinforcement learningScheduling (production processes)State of chargeArtificial neural networkDistributed generationEnergy storageMathematical optimizationBattery (electricity)EngineeringArtificial intelligencePower (physics)Renewable energyElectrical engineeringMathematicsQuantum mechanicsControl (management)PhysicsMicrogrid Control and OptimizationSmart Grid Energy ManagementOptimal Power Flow Distribution