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Machine Learning for AC Optimal Power Flow

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arxiv 1910.08842 v1 pith:7LKJNL2T submitted 2019-10-19 cs.LG eess.SPstat.ML

Neel Guha , Zhecheng Wang , Matt Wytock , Arun Majumdar This is my paper · ORCID
classification cs.LGeess.SPstat.ML
keywords optimallearningmachinetaskacopfconstraintspowerpredict
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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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Cited by 1 Pith paper

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  1. HoP: Homeomorphic Polar Learning for Hard Constrained Optimization

    cs.LG 2025-02 conditional novelty 6.0 of 10

    A polar-coordinate homeomorphic mapping lets a neural network output only feasible points for star-convex constraints, with tests showing lower objective values and zero violations than baseline L2O solvers.

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