Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network

arXiv:2405.01200v1 Announce Type: cross
Abstract: 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 …

abstract arxiv commercial commitment convolution convolutional cs.lg cs.sy eess.sy exploits few-shot graph network physics solutions solve spatial temporal type via