Simple linear baselines match or beat a leading neural surrogate for AC-OPF voltage prediction, while the introduced transformer improves over the neural approach but not over linear regression.
Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions
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abstract
Using machine learning to obtain solutions to AC optimal power flow has recently been a very active area of research due to the astounding speedups that result from bypassing traditional optimization techniques. However, generally ensuring feasibility of the resulting predictions while maintaining these speedups is a challenging, unsolved problem. In this paper, we train a neural network to emulate an iterative solver in order to cheaply and approximately iterate towards the optimum. Once we are close to convergence, we then solve a power flow to obtain an overall AC-feasible solution. Results shown for networks up to 1,354 buses indicate the proposed method is capable of finding feasible, near-optimal solutions to AC OPF in milliseconds on a laptop computer. In addition, it is shown that the proposed method can find "difficult" AC OPF solutions that cause flat-start or DC-warm started algorithms to diverge.
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Revisiting Deep AC-OPF
Simple linear baselines match or beat a leading neural surrogate for AC-OPF voltage prediction, while the introduced transformer improves over the neural approach but not over linear regression.