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has been cited by the following article:

• TITLE: Reinforcement Learning-Based Control for Resilient Community Microgrid Applications

  AUTHORS: Md Mahmudul Hasan, Ishtiaque Zaman, Miao He, Michael Giesselmann

  KEYWORDS: Microgrid, Reinforcement Learning, Q-Learning Algorithm, Vehi-cle-to-Grid (V2G)

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.10 No.9, September 30, 2022

  ABSTRACT: A novel microgrid control strategy is presented in this paper. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is considered. To fully exploit the capability of the community microgrid to operate in either grid-connected mode or islanded mode, as well as to achieve improved stability of the microgrid system, universal droop control, virtual inertia control, and a reinforcement learning-based control mechanism are combined in a cohesive manner, in which adaptive control parameters are determined online to tune the influence of the controllers. The microgrid model and control mechanisms are implemented in MATLAB/Simulink and set up in real-time simulation to test the feasibility and effectiveness of the proposed model. Experiment results reveal the effectiveness of regulating the controller’s frequency and voltage for various operating conditions and scenarios of a microgrid.