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Related Experiment Video

Updated: Apr 10, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Resilient distributed model predictive control for cooperative microgrids under communication loss with demand

Baheej Alghamdi1,2

  • 1Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah, Saudi Arabia.

Plos One
|April 8, 2026
PubMed
Summary

This study presents a resilient distributed model predictive control (RDMPC) framework for networked microgrids, ensuring safe energy management during communication failures through ADMM and reserve margins. Demand response integration is key for operational flexibility.

Related Experiment Videos

Last Updated: Apr 10, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Energy Systems

Background:

  • Networked microgrids require robust control for reliable energy management.
  • Integrating demand response (DR) enhances grid flexibility but complicates control.
  • Distributed model predictive control (MPC) offers decentralized coordination but faces communication challenges.

Purpose of the Study:

  • To introduce a Resilient Distributed Model Predictive Control (RDMPC) framework.
  • To enable coordinated energy management in networked microgrids with DR integration.
  • To ensure safe and feasible operation under communication impairments.

Main Methods:

  • An Alternating Direction Method of Multipliers (ADMM)-based distributed MPC formulation is used for coordination.
  • Tie-line reciprocity is maintained via a shared consensus schedule.
  • Communication failure resilience is achieved by treating tie-line mismatch as bounded disturbances with reserve margins and local feasibility repair.

Main Results:

  • The RDMPC framework ensures feasible reciprocal execution even under packet loss and outages.
  • Local feasibility repair with slack variables provides anytime feasibility.
  • Demand response ablation reveals significant increases in energy not served and costs when flexibility is removed.

Conclusions:

  • The proposed RDMPC framework effectively coordinates networked microgrids with DR under communication constraints.
  • The resilience mechanisms ensure safe operation during communication failures.
  • Demand response is crucial for maintaining microgrid performance and economic efficiency.