| Autor: |
Xia, Tian, Zhang, Ning, Li, Weiran, Du, Ershun, Su, Yun, Fang, Chen, Kang, Chongqing |
| Zdroj: |
IEEE Transactions on Power Systems; 2024, Vol. 39 Issue: 3 p5443-5446, 4p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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