Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions

Understand

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.