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arXiv · 1910.08842

Machine Learning for AC Optimal Power Flow

Neel Guha·Zhecheng Wang·Matt Wytock·Arun Majumdar
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Abstract

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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Neel Guha, Zhecheng Wang, Matt Wytock, Arun Majumdar. 2019-10-19. Machine Learning for AC Optimal Power Flow. https://arxiv.org/abs/1910.08842

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