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Related Experiment Video

Updated: Jun 17, 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

QS-MADS: quorum sensing perturbation-driven adaptive multi-agent scheduling for distributed power grids.

Kun Zeng1, Yixiao Cao2, Hui Sun3

  • 1Chongqing Vocational Institute of Safety Technology, Chongqing, 404020, China. zengkun@tongji.edu.cn.

Scientific Reports
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

QS-MADS enhances power grid dispatch by improving coordination and efficiency in multi-agent reinforcement learning (MARL). This novel framework reduces voltage violations and training time for distributed energy resources (DERs).

Keywords:
Deep learningDistributed energy resourcesGraph attention networkMulti-agent reinforcement learningPower grid dispatchQuorum sensing

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Related Experiment Videos

Last Updated: Jun 17, 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

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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Published on: September 8, 2023

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Distributed energy resources (DERs) increase power grid complexity for real-time dispatch.
  • Existing multi-agent reinforcement learning (MARL) methods struggle with coordination, efficiency, and noise.

Purpose of the Study:

  • To develop an adaptive multi-agent scheduling framework for improved grid dispatch under uncertainty.
  • To address limitations in current MARL approaches for power grid management.

Main Methods:

  • Proposed QS-MADS framework using quorum-sensing (QS) signals for agent coordination.
  • Employs a topology-aware graph attention encoder and a scalar QS signal for adaptive exploration.
  • Utilizes centralized training with optional online adaptation and decentralized deployment.

Main Results:

  • QS-MADS achieved up to 25% higher cumulative dispatch reward on benchmark grids.
  • Reduced voltage violation events by 31% compared to baseline methods.
  • Converged in up to 40% fewer training episodes with robust performance under noise.

Conclusions:

  • QS-MADS offers a more efficient and coordinated approach to real-time grid dispatch with DERs.
  • The framework demonstrates improved performance, faster convergence, and robustness against noise.
  • Enables millisecond-level inference latency for practical grid applications.