  In this paper, we consider a chance-constrained formulation of the optimal
power flow problem to handle uncertainties resulting from renewable generation
and load variability. We propose a tuning method that iterates between solving
an approximated reformulation of the optimization problem and using a
posteriori sample-based evaluations to refine the reformulation. Our method is
applicable to both single and joint chance constraints and does not rely on any
distributional assumptions on the uncertainty. In a case study for the IEEE
24-bus system, we demonstrate that our method is computationally efficient and
enforces chance constraints without over-conservatism.
