Distributed pinning collaborative control of islanded microgrids based on deep reinforcement learning
LIU Wei
HU Tian-huan
TANG Cheng-ye
Abstract：Microgrids can comprehensively utilize various energy sources and coordinate energy storage systems to compensate for the uncertainties in the output of distributed generators(DGs).a distributed pinning collaborative control strategy based on deep reinforcement learning for isolated microgrids is proposed to solve the problem of voltage and frequency deviation caused by droop control in isolated microgrids,which can effectively suppress frequency and voltage fluctuations under load disturbance or system topology changes.Firstly,aiming at the problem that the pinning target value in the pinning consistency algorithm is difficult to accurately preset and change,the double deep Q learning(DDQN)is used to adaptively modify the pinning target value by leveraging the adaptability and generalization capability of DDQN.It improves the adaptability and effectiveness of the pinning consistency algorithm.Furthermore,the structure of the distributed pinning collaborative controller based on DDQN is designed,and the definition of state space,action space,and reward function is completed.Finally,Simulation results demonstrate that the proposed control method outperforms traditional pinning control and reinforcement learning-based pinning control in responding to changes in network topology parameters and structure.
Keywords：islanded microgridssecondary controldeep reinforcement learningpinning controldistributed collabo-rative control
Publication Date：2026-02-28
Online Publishing Date：2026-04-08(First online date of this platform, not the publication date of the document)
Pages：10( 325-334 )