This letter proposes a few-shot physics-guided spatial temporal graph convolutional network (FPG-STGCN) to fast solve unit commitment (UC). Firstly, STGCN is tailored to parameterize UC. Then, few-shot physics-guided learning scheme is proposed. It exploits few typical UC solutions yielded via commercial optimizer to escape from local minimum, and leverages the augmented Lagrangian method for constraint satisfaction. To further enable both feasibility and continuous relaxation for integers in learning process, straight-through estimator for Tanh-Sign composition is proposed to fully differentiate the mixed integer solution space. Case study on the IEEE benchmark justifies that, our method bests mainstream learning ways on UC feasibility, and surpasses traditional solver on efficiency.

翻译：本文提出了一种基于少样本物理引导的时空图卷积网络（FPG-STGCN）来快速求解机组组合（UC）问题。首先，针对UC问题定制了STGCN进行参数化表征。随后，提出了少样本物理引导学习方案，该方案利用商用优化器生成的少量典型UC解来避免局部最优，并采用增广拉格朗日方法确保约束满足。为进一步在学习过程中同时实现整数变量的可行性与连续松弛，提出了Tanh-Sign组合的直通估计器，以实现混合整数解空间的完全可微。基于IEEE基准算例的案例研究表明，该方法在UC可行性方面优于主流学习方法，且在效率上超越传统求解器。