Robust stability control of droop-controlled microgrids under worst-case parameter uncertainty via adversarial deep reinforcement learning
Author(s)
Chen, Wenhao
Wang, Yu
Pal, Bikash
Type
Journal Article
Abstract
Microgrids based on droop-controlled inverters can achieve small-signal stability control by adjusting the droop coefficients. However, due to discrepancies between modeling parameters and actual system parameters in inverter-based microgrids, the droop settings determined from the model often fail to deliver the expected control performance in real-world applications. To address this critical issue, this paper proposes a droop coefficient tuning strategy based on adversarial reinforcement learning, which exhibits strong robustness against system parameter uncertainties. The uncertain parameter behavior and the robust tuning of droop coefficients are respectively modeled as a Markov Decision Process (MDP) and an Adversarial Markov Decision Process (AMDP). The twin delayed deep deterministic policy gradient (TD3) algorithm is employed to solve these processes, yielding the worst-case uncertainty policy and the corresponding robust droop control strategy. Finally, case studies are conducted on a microgrid system with four inverter-based resources (IBRs) and a modified IEEE 33-bus distribution system to validate the effectiveness and scalability of the proposed method.
Date Issued
2026-09-01
Date Acceptance
2025-12-30
Citation
IEEE Transactions on Industry Applications, 2026, 62 (5), pp.8018-8029
ISSN
0093-9994
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
8018
End Page
8029
Journal / Book Title
IEEE Transactions on Industry Applications
Volume
62
Issue
5
Copyright Statement
Copyright © 2026 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2026-01-19