  We present a decomposition approach for obtaining good feasible solutions for
the security-constrained alternating-current optimal power flow (SCACOPF)
problem at an industrial scale and under real-world time and computational
limits. The approach aims at complementing the existing body of literature on
bounding the problem via convex relaxations. It was designed for the
participation in ARPA-E's Grid Optimization (GO) Competition Challenge 1. The
challenge focused on a near-real-time version of the SCACOPF problem where a
base case operating point is optimized taking into account possible
single-element contingencies, after which the system adapts its operating point
following the response of automatic frequency drop controllers and voltage
regulators. Our solution approach for this problem relies on state-of-the-art
nonlinear programming algorithms and employs nonconvex relaxations for
complementarity constraints, a specialized two-stage decomposition technique
with sparse approximations of recourse terms, and contingency ranking and
pre-screening. The paper also outlines the salient features of our
implementation, such as fast model functions and derivatives evaluation,
warm-starting strategies, and asynchronous parallelism. We discuss the results
of the independent benchmark of our approach done by ARPA-E's GO team in
Challenge 1, which found that our methodology consistently produces high
quality solutions across a wide range of network sizes and difficulty. Finally,
we conclude by outlining potential extensions and improvements of our
methodology.
