Check the paper Spatial Network Decomposition for Fast and Scalable AC-OPF Learning for a machine learning approach to AC-OPF. The paper combines ideas from optimization and machine learning to obtain a fast and scalable learning approach.
Check our paper
- V. Dvorkin, Jr., F. Fioretto, P. Van Hentenryck, P. Pinson, and J. Kazempour, “Differentially private optimal power flow for distribution grids,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 2186–2196, May 2021, doi: 10.1109/TPWRS.2020.3031314.
It won a best paper award for the IEEE Transactions on Power Systems.
In conjunction with MISO and Vanderbilt University, the RAMC project will explore new approaches to market clearing and security-constrained optimal power flows. It will leverage techniques from stochastic optimization, machine learning, and risk management to improve the use of renewable energy in large-scale energy systems.