부산대학교 도서관

Neural networks have shown great potential to learn complex stability constraints for power system operation with high renewable penetration. However, explicitly embedding neural network-based stability constraints into power system dispatch is computationally intensive for online applications. This letter presents an efficient method to embed neural network-based stability constraints into power system dispatch. The neural network-based stability constraints are embedded into the optimization problem in linear form iteratively. Case studies on NPCC 140-bus system and a realistic power system demonstrate the effectiveness and efficiency of the proposed method.