Predicting Power Dispatch for Unit Commitment Problems Using Graph-Temporal Convolutional Networks With Constrained Learning

Li'ao Chen, Siyi Zhou, Xingyu Liang, Wentian Lu, Min Xia, Liguo Weng, Jian Geng, Jun Liu

Abstract: Unit commitment (UC) is a critical component for the power system dispatching departments. Current methodologies for solving UC problems predominantly rely on mixed-integer linear programming and are supplemented by data-driven approaches. These methodologies have two primary limitations: first, as the scale of the power grid expands, the complexity of algorithms increases sharply. Second, they fail to fully exploit grid topology information and historical data trends. To address these limitations, this article proposes a constrained graph-temporal convolutional network, which addresses the UC problem by directly predicting power output with constraints. The algorithm takes historical load data from grid nodes as input, utilizes graph convolutional networks to capture the physical grid topology information, and employs temporal convolutional networks to extract temporal features. Ultimately, the output of the graph-temporal convolutional network is projected into the feasible domain through a linear constraint activation layer, achieving accurate power prediction. Experiments conducted on the IEEE 30-BUS and IEEE 118-BUS systems validate the feasibility and superiority of our method in terms of accuracy and computational efficiency.

External IDs:dblp:journals/tii/ChenZLLXWGL25