DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

@article{Pan2020DeepOPFAF,
  title={DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems},
  author={Xiang Pan and Minghua Chen and Tianyu Zhao and Steven H. Low},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.01002}
}
The AC-OPF problem is the key and challenging problem in the power system operation. When solving the AC-OPF problem, the feasibility issue is critical. In this paper, we develop an efficient Deep Neural Network (DNN) approach, DeepOPF, to ensure the feasibility of the generated solution. The idea is to train a DNN model to predict a set of independent operating variables, and then to directly compute the remaining dependable variables by solving the AC power flow equations. While this… 

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The proposed DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes and theoretically characterize the calibration magnitude necessary for ensuring universal feasibility.

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