  AC Optimal Power Flow (AC-OPF) is a fundamental building block in power
system optimization. It is often solved repeatedly, especially in regions with
large penetration of renewable generation, to avoid violating operational
limits. Recent work has shown that deep learning can be effective in providing
highly accurate approximations of AC-OPF. However, deep learning approaches may
suffer from scalability issues, especially when applied to large realistic
grids. This paper addresses these scalability limitations and proposes a load
embedding scheme using a 3-step approach. The first step formulates the load
embedding problem as a bilevel optimization model that can be solved using a
penalty method. The second step learns the encoding optimization to quickly
produce load embeddings for new OPF instances. The third step is a deep
learning model that uses load embeddings to produce accurate AC-OPF
approximations. The approach is evaluated experimentally on large-scale test
cases from the NESTA library. The results demonstrate that the proposed
approach produces an order of magnitude improvements in training convergence
and prediction accuracy.
