Hierarchical coordinated scheduling algorithm for reactive power and voltage in cross-regional power grids based on multi-agent reinforcement learning

Zhida Lin, Ximing Zhang, Zhengguo Ren, Yanning Shao and Yuanfeng Chen

PLOS ONE, 2026, vol. 21, issue 4, 1-20

Abstract: To address the challenges of strong dynamic coupling, action space dimension explosion, and voltage imbalance in reactive power and voltage scheduling of cross-regional power grids, this paper proposes a hierarchical coordinated scheduling method based on multi-agent reinforcement learning. The method first constructs a multi-agent reinforcement learning framework driven by probabilistic neural networks to perform distributed representation learning on the joint state vectors, achieving high-precision prediction of reactive power and voltage operating states for each node (prediction error MAE

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0346570

DOI: 10.1371/journal.pone.0346570

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