Reinforcement learning-based event-triggered secondary control of DC microgrids
Journal article, 2024

In this paper, a reinforcement learning (RL)-based event-triggered mechanism (ETM) for employing in the secondary control layer (SCL) of DC microgrids is developed. The proposed RL-based ETM satisfies the SCL objectives, which is overcoming the disadvantages of primary control (such as voltage deviation and inappropriate current sharing among the distributed generating units). More importantly, it also aids in reducing the amount of transmitted data exchanged within all the distributed generators (DGs). The design parameters of the ETM scheme are regulated through a robust RL approach to provide adaptive ETM parameter tuning, enabling the ETM error vector threshold to quickly adapt to changes in the MG. The suggested RL-based ETM approach is implemented in a DC microgrid, and utilizing hardware in the loop (HIL) real-time OPAL-RT experimental tests, its performance in the SCL of DC microgrids is investigated. Experimental validations have confirmed the merits of the proposed approach.

Artificial intelligent

DC microgrids

Power-sharing

Voltage regulation

Secondary control

Event-triggered control

Reinforcement learning

Author

Houshmand Negahdar

Islamic Azad University

Amin Karimi

Islamic Azad University

Yousef Khayat

Chalmers, Electrical Engineering, Electric Power Engineering

Saeed Golestan

Aalborg University

Energy Reports

23524847 (eISSN)

Vol. 11 2818-2831

Areas of Advance

Energy

Subject Categories

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.egyr.2024.02.033

More information

Latest update

3/13/2024