Leader-Follower Distributed Frequency Control for Networked Microgrids Using Model Predictive Control and Graph Theory

This paper develops a distributed load frequency control design that employs Model Predictive Control (MPC) within a leader-follower control scheme based on graph theory for networked microgrid (NMG) systems. The proposed NMG model contains three equivalent microgrids (MGs) with fully connected topo...

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Published inConference record of the Industry Applications Conference pp. 1 - 8
Main Authors Abdelfatah, Mohannad, Tan, Wen-Shan, Raj Irudayaraj, Andrew Xavier, Wu, Yuan-Kang
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.06.2025
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ISSN2576-702X
DOI10.1109/IAS62731.2025.11061512

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Abstract This paper develops a distributed load frequency control design that employs Model Predictive Control (MPC) within a leader-follower control scheme based on graph theory for networked microgrid (NMG) systems. The proposed NMG model contains three equivalent microgrids (MGs) with fully connected topologies that share tie-lines between them. Each MG includes diesel generators, renewable energy sources, and energy storage systems. The mathematical model was depicted through state-space notations. The frequency control system employed Laplacian-based distributed control, which selected MG1 as the leader to receive information from neighbouring MG2 and MG3, thus minimising communication needs without compromising stability or performance. A basic centralised MPC controller was tested in the model against a variable load for all MGs, and then the results were compared with the proposed distributed MPC (DMPC). The DMPC implementation resulted in superior performance, as it achieved a consistent settling time of just 5 s, which is 2.5 times faster than centralised MPC (12-60 s) and over 28 times faster than PID control (140-180 s), while maintaining steady state error below 0.07 Hz for system frequency control after its testing phase.
AbstractList This paper develops a distributed load frequency control design that employs Model Predictive Control (MPC) within a leader-follower control scheme based on graph theory for networked microgrid (NMG) systems. The proposed NMG model contains three equivalent microgrids (MGs) with fully connected topologies that share tie-lines between them. Each MG includes diesel generators, renewable energy sources, and energy storage systems. The mathematical model was depicted through state-space notations. The frequency control system employed Laplacian-based distributed control, which selected MG1 as the leader to receive information from neighbouring MG2 and MG3, thus minimising communication needs without compromising stability or performance. A basic centralised MPC controller was tested in the model against a variable load for all MGs, and then the results were compared with the proposed distributed MPC (DMPC). The DMPC implementation resulted in superior performance, as it achieved a consistent settling time of just 5 s, which is 2.5 times faster than centralised MPC (12-60 s) and over 28 times faster than PID control (140-180 s), while maintaining steady state error below 0.07 Hz for system frequency control after its testing phase.
Author Raj Irudayaraj, Andrew Xavier
Tan, Wen-Shan
Wu, Yuan-Kang
Abdelfatah, Mohannad
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  givenname: Wen-Shan
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  organization: Monash University Malaysia,School of Engineering, and Centre for Net-Zero Technology,Bandar Sunway,Selangor,Malaysia,47500
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  givenname: Yuan-Kang
  surname: Wu
  fullname: Wu, Yuan-Kang
  email: allenwu@ccu.edu.tw
  organization: National Chung-Cheng University,Chia-Yi,Taiwan,62102
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Snippet This paper develops a distributed load frequency control design that employs Model Predictive Control (MPC) within a leader-follower control scheme based on...
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SubjectTerms Distributed Control
Frequency control
Frequency Regulation
Graph theory
Leader-Follower Control
Load modeling
Mathematical models
Microgrids
Model Predictive Control
PD control
PI control
Power system stability
Predictive control
Topology
Title Leader-Follower Distributed Frequency Control for Networked Microgrids Using Model Predictive Control and Graph Theory
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