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<p>The rapid proliferation of distributed energy resources (DERs) in modern power grids introduces unprecedented complexity into real-time dispatch scheduling, demanding solutions that can operate under high uncertainty, partial observability, and volatile renewable generation. Existing multi-agent reinforcement learning (MARL) approaches for grid dispatch often suffer from weak inter-agent coordination, sample inefficiency, and fragility under measurement noise, largely because they rely on either independent exploration or expensive hidden-state exchange. We propose <b>QS-MADS</b>, a quorum-sensing-driven adaptive multi-agent scheduling framework that combines a topology-aware graph attention encoder with a lightweight scalar QS signal. During centralised training and optional online adaptation, each agent emits one 32-bit QS criticality signal; the trainer aggregates these scalars into a population consensus that governs a differentiable adaptive perturbation module used only for policy-gradient exploration. At deployment, the stochastic perturbation is removed and each deterministic actor executes from its local <i>k</i>-hop graph embedding, avoiding pairwise hidden-state communication. On IEEE 118-bus, RTS-96, and the CIGRE medium-voltage microgrid benchmarks, QS-MADS achieves up to 25% higher cumulative dispatch reward, reduces voltage violation events by 31%, and converges in up to 40% fewer training episodes relative to the strongest baseline, while maintaining millisecond-level inference latency and robust performance under measurement noise.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Kun Zeng,
  • Yixiao Cao,
  • Hui Sun

摘要

The rapid proliferation of distributed energy resources (DERs) in modern power grids introduces unprecedented complexity into real-time dispatch scheduling, demanding solutions that can operate under high uncertainty, partial observability, and volatile renewable generation. Existing multi-agent reinforcement learning (MARL) approaches for grid dispatch often suffer from weak inter-agent coordination, sample inefficiency, and fragility under measurement noise, largely because they rely on either independent exploration or expensive hidden-state exchange. We propose QS-MADS, a quorum-sensing-driven adaptive multi-agent scheduling framework that combines a topology-aware graph attention encoder with a lightweight scalar QS signal. During centralised training and optional online adaptation, each agent emits one 32-bit QS criticality signal; the trainer aggregates these scalars into a population consensus that governs a differentiable adaptive perturbation module used only for policy-gradient exploration. At deployment, the stochastic perturbation is removed and each deterministic actor executes from its local k-hop graph embedding, avoiding pairwise hidden-state communication. On IEEE 118-bus, RTS-96, and the CIGRE medium-voltage microgrid benchmarks, QS-MADS achieves up to 25% higher cumulative dispatch reward, reduces voltage violation events by 31%, and converges in up to 40% fewer training episodes relative to the strongest baseline, while maintaining millisecond-level inference latency and robust performance under measurement noise.