Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness
Chijioke Eze, Chetan Prakash, Antonello Monti · Energy and AI · 2026
Active voltage control in power distribution networks with high photovoltaic penetration requires scalable, topology-aware strategies that can exploit the graph-structured nature of electrical grids. We present a multi-agent reinforcement learning framework that integrates graph neural network architectures directly into the distributed actor networks, enabling each agent to reason about local grid topology at inference time. Three representative graph neural network families are compared against conventional topology-unaware baselines under matched parameter budgets: a spectral-based graph convolutional network, an attention-based graph attention network, and a memory-based gated graph convolutional network. Experiments on IEEE 33-, 141-, and 322-bus distribution systems show that topology-aware policies match baseline performance on small networks but deliver substantial improvements on medium and large networks. Specifically, they reduce voltage violations by approximately 47% on the 141-bus system and achieve the lowest voltage-out-of-control rate of 3.20% on the 322-bus system. Beyond radial networks, we report the first multi-agent reinforcement learning evaluation of voltage control on looped (meshed) distribution networks, obtained by reconfiguring the radial feeders, and find that graph neural network-based policies maintain comparable performance under this topology modification. Furthermore, our edge-feature ablation reveals that explicit encoding of line impedances is not essential for strong control performance, and our approach consistently yields lower power losses than both traditional optimization-based and topology-unaware methods across all network sizes. These results indicate that graph neural network-based multi-agent reinforcement learning is a promising and energy-efficient approach for distributed voltage control in modern power grids.