  This paper presents a scalable method for improving the solutions of AC
Optimal Power Flow (AC OPF) with respect to deviations in predicted power
injections from wind and other uncertain generation resources. The focus of the
paper is on providing solutions that are more robust to short-term deviations,
and which optimize both the initial operating point and a parametrized response
policy for control during fluctuations. We formulate this as a
chance-constrained optimization problem. To obtain a tractable representation
of the chance constraints, we introduce a number of modelling assumptions and
leverage recent theoretical results to reformulate the problem as a convex,
second-order cone program, which is efficiently solvable even for large
instances. Our experiments demonstrate that the proposed procedure improves the
feasibility and cost performance of the OPF solution, while the additional
computation time is on the same magnitude as a single deterministic AC OPF
calculation.
