Active Distribution Network Voltage Control with a Physics-Informed Spatiotemporal Attention Network

Active voltage control (AVC) in active distribution networks coordinates the reactive power outputs of distributed inverters to maintain bus voltages within secure limits. Although multi-agent reinforcement learning (MARL) shows promise for AVC, current methods face three main limitations: graph topologies rely on unweighted adjacency, ignoring physical parameters like line impedance and electrical distance; centralized critics output a single global Q-value, leading to coarse spatial credit assignment; and temporal critic modules suffer from vanishing gradients and representation drift. To address these issues, we propose physics-informed spatiotemporal multi-agent value learning (PST-MA), a physics-informed spatiotemporal value-learning framework integrating three coupled designs: a physics-informed graph attention mechanism with electrical-distance-aware sparsification; node-conditional value outputs utilizing a replicated-graph diagonal-extraction strategy; and a temporal latent compression module featuring a gated bypass and late action fusion. Experiments on the IEEE 33-bus and 141-bus systems validate the effectiveness of the proposed PST-MA method. Results demonstrate that it consistently achieves a higher controllable ratio than baseline methods for coordinated voltage regulation under uncertainty.