Mathematical Programming formulations for the Alternating Current Optimal Power Flow problem
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Abstract (arXiv)
Power flow refers to the injection of power on the lines of an electrical grid, so that all the injections at the nodes form a consistent flow within the network. Optimality, in this setting, is usually intended as the minimization of the cost of generating power. Current can either be direct or alternating: while the former yields approximate linear programming formulations, the latter yields formulations of a much more interesting sort: namely, nonconvex nonlinear programs in complex numbers. In this technical survey, we derive formulation variants and relaxations of the alternating current optimal power flow problem.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 3.
4 papers.
- 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
- 2024Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flowcited by 1
- 2024Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flowcited by 1
- 2022Sparse Polynomial Optimization: Theory and Practicecited by 0
- 2024Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow7 Oct 2024
- 2024Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow30 Jul 2024
- 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey5 Jul 2024
- 2022Sparse Polynomial Optimization: Theory and Practice23 Aug 2022