Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

@article{Zamzam2019LearningOS,
  title={Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow},
  author={Ahmed S. Zamzam and Kyri Baker},
  journal={2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)},
  year={2019},
  pages={1-6}
}
  • Ahmed S. ZamzamK. Baker
  • Published 27 September 2019
  • Computer Science
  • 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
We develop, in this paper, a machine learning approach to optimize the real-time operation of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps. The AC OPF problem aims at identifying optimal operational conditions of the power grids that minimize power losses and/or generation costs. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or… 
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References

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