Network Dynamic Spatiotemporal Dispatch Based on Multi-Head Graph Attention Reinforcement Learning and Balanced Responsibility

The rapid integration of distributed renewable energy and flexible loads significantly intensifies supply and demand uncertainty in active distribution networks (ADNs), threatening economic and secure grid operations. Existing deep reinforcement learning (DRL) dispatch methods fail to extract spatial features properly, leading to a local optimal solution. To address these limitations, this paper proposes a state-adaptive topology-aware continuous-dispatch framework via multi-head graph attention network and deep deterministic policy gradient (GAT-DDPG). A multi-head graph attention network is embedded within a centralized Actor–Critic training paradigm to adaptively update spatial message-passing weights based on operational states. Extensive simulations on a modified IEEE 33-bus ADN over a 125-day unseen test set demonstrate that the proposed framework achieves lower comprehensive operating costs and fewer voltage violations compared with representative DRL-based dispatch baselines. Visualizations of state-dependent attention shifts confirm the model’s capability to track dynamically shifting network vulnerabilities, providing physical interpretability. Based on this, and combined with the optimized dispatch method of balancing responsibility, the ability of different flexible resources to support safe and stable operation and the ideal dispatch results under the temporary reduction in new energy output are further simulated.