Reinforcement Learning-Based Control for Resilient Community Microgrid Applications  

作　　者：Md Mahmudul Hasan Ishtiaque Zaman Miao He Michael Giesselmann Md Mahmudul Hasan;Ishtiaque Zaman;Miao He;Michael Giesselmann(Electrical and Computer Engineering, Texas Tech University, Lubbock, USA)

机构地区：[1]Electrical and Computer Engineering, Texas Tech University, Lubbock, USA

出　　处：《Journal of Power and Energy Engineering》2022年第9期1-13,共13页电力能源（英文）

摘　　要：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.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.

关 键 词：MICROGRID Reinforcement Learning Q-Learning Algorithm Vehi-cle-to-Grid (V2G) 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]