  Optimal Power Flow (OPF) dispatches controllable generation at minimum cost
subject to operational constraints on generation and transmission assets. The
uncertainty and variability of intermittent renewable generation is challenging
current deterministic OPF approaches. Recent formulations of OPF use chance
constraints to limit the risk from renewable generation uncertainty, however,
these new approaches typically assume the probability distributions which
characterize the uncertainty and variability are known exactly. We formulate a
Robust Chance Constrained (RCC) OPF that accounts for uncertainty in the
parameters of these probability distributions by allowing them to be within an
uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that
scales to large power systems. We demonstrate the RRC OPF on a modified model
of the Bonneville Power Administration network, which includes 2209 buses and
176 controllable generators. Deterministic, chance constrained (CC), and RCC
OPF formulations are compared using several metrics including cost of
generation, area control error, ramping of controllable generators, and
occurrence of transmission line overloads as well as the respective
computational performance.
