  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. Lastly, we show that for larger networks, the
learning-based solution finds approximate solutions to AC OPF faster than it
takes to find a solution to DC OPF, with a smaller optimality gap than DC OPF
provides, and without the AC infeasibility of DC OPF.
