Machine Learning for AC Optimal Power Flow (Research Track) Honorable Mention

Neel Guha (Carnegie Mellon University); Zhecheng Wang (Stanford University); Arun Majumdar (Stanford University)

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Power & Energy Heavy Industry and Manufacturing


F( We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.

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