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.