用物理约束增强图神经网络，实现配电网储能实时优化
Physics-Aware Heterogeneous GNN Architecture for Real-Time BESS Optimization in Unbalanced Distribution Systems
- 将三相电压、负载不平衡等信息嵌入异构图节点
- 电压预测误差低至6.92e-7，约束违反几乎为零
- 适合电力系统优化与新能源并网研究者
电池储能系统（BESS）在三相不平衡配电网中对维持电压稳定和实现最优调度至关重要。现有深度学习方法常缺乏显式的三相表示，难以准确建模相位特异性动态并满足运行约束，导致调度方案不可行。本文通过将相电压、负载不平衡及储能状态等详细电网信息嵌入异构图节点，结合多种GNN架构（GCN、GAT、GraphSAGE、GPS），联合预测网络状态变量，精度高。此外，引入物理信息损失函数，通过软惩罚机制融入电池的荷电状态（SoC）与充放电率（C-rate）约束。在CIGRE 18节点配电网上的实验表明，该方法电压预测均方误差分别为：GCN 6.92e-07，GAT 1.21e-06，GPS 3.29e-05，SAGE 9.04e-07；且几乎无SoC与C-rate约束违规，验证了其在可靠、合规调度中的有效性。
原文摘要 · Abstract (English)
Battery energy storage systems (BESS) have become increasingly vital in three-phase unbalanced distribution grids for maintaining voltage stability and enabling optimal dispatch. However, existing deep learning approaches often lack explicit three-phase representation, making it difficult to accurately model phase-specific dynamics and enforce operational constraints--leading to infeasible dispatch solutions. This paper demonstrates that by embedding detailed three-phase grid information--including phase voltages, unbalanced loads, and BESS states--into heterogeneous graph nodes, diverse GNN architectures (GCN, GAT, GraphSAGE, GPS) can jointly predict network state variables with high accuracy. Moreover, a physics-informed loss function incorporates critical battery constraints--SoC and C-rate limits--via soft penalties during training. Experimental validation on the CIGRE 18-bus distribution system shows that this embedding-loss approach achieves low prediction errors, with bus voltage MSEs of 6.92e-07 (GCN), 1.21e-06 (GAT), 3.29e-05 (GPS), and 9.04e-07 (SAGE). Importantly, the physics-informed method ensures nearly zero SoC and C-rate constraint violations, confirming its effectiveness for reliable, constraint-compliant dispatch.
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