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.

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to find feasible solutions with high computational efficiency. It predicts voltages of all buses and then uses them to obtain all remaining variables. A fast post-processing method is developed to enforce generation constraints. The effectiveness of DeepOPF-V is validated by case studies of several IEEE test systems. Compared with existing approaches, DeepOPF-V achieves a state-of-art computation speedup up to three orders of magnitude and has better performance in preserving the feasibility of the solution.