# Revisiting Deep AC-OPF

arXiv: https://arxiv.org/abs/2509.00655 · HTML: https://mlsanity.com/paper/2509.00655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Optimal Power Flow Using Graph Neural Networks](https://mlsanity.com/paper/1910.09658/) (2019)
- [Enforcing robust control guarantees within neural network policies](https://mlsanity.com/paper/2011.08105/) (2020)
- [Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions](https://mlsanity.com/paper/2205.10129/) (2022)
- [CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations](https://mlsanity.com/paper/2403.17660/) (2024)
- [Leveraging power grid topology in machine learning assisted optimal power flow](https://mlsanity.com/paper/2110.00306/) (2021)
- [Power Flow Balancing with Decentralized Graph Neural Networks](https://mlsanity.com/paper/2111.02169/) (2021)
- [DeepOPF-V: Solving AC-OPF Problems Efficiently](https://mlsanity.com/paper/2103.11793/) (2021)
- [OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets](https://mlsanity.com/paper/2111.01228/) (2021)
- [Unsupervised Optimal Power Flow Using Graph Neural Networks](https://mlsanity.com/paper/2210.09277/) (2022)
- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Learning an Optimally Reduced Formulation of OPF through Meta-optimization](https://mlsanity.com/paper/1911.06784/) (2019)
- [Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions](https://mlsanity.com/paper/2012.10031/) (2020)
- [Deep learning architectures for inference of AC-OPF solutions](https://mlsanity.com/paper/2011.03352/) (2020)
- [DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks](https://mlsanity.com/paper/2309.12849/) (2023)
- [Numerical Comparisons of Linear Power Flow Approximations: Optimality, Feasibility, and Computation Time](https://mlsanity.com/paper/2207.05216/) (2022)

## Built on by (most cited first, 1 of 1)

- [ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flow](https://mlsanity.com/paper/2610.04307/) (2026)

## Abstract (arXiv)

Recent work has proposed machine learning (ML) approaches as fast surrogates for solving AC optimal power flow (AC-OPF), with claims of significant speed-ups and high accuracy. In this paper, we revisit these claims through a systematic evaluation of ML models against a set of simple yet carefully designed linear baselines. We introduce OPFormer-V, a transformer-based model for predicting bus voltages, and compare it to both the state-of-the-art DeepOPF-V model and simple linear methods. Our findings reveal that, while OPFormer-V improves over DeepOPF-V, the relative gains of the ML approaches considered are less pronounced than expected. Simple linear baselines can achieve comparable performance. These results highlight the importance of including strong linear baselines in future evaluations.