  To manage renewable generation and load consumption uncertainty,
chance-constrained optimal power flow (OPF) formulations and various solution
methodologies have been proposed. However, conventional solution approaches
often rely on accurate estimates of uncertainty distributions, which may not
exist. When the distributions are not known but can be limited to a set of
plausible distributions, termed an ambiguity set, distributionally robust (DR)
optimization can be used to ensure that chance constraints hold for all
distributions in that set. However, DR OPF yields conservative solutions if the
ambiguity set is too large. In this paper, we assess the value of using both
moment and unimodality information, which shrinks the ambiguity set and reduces
conservatism, in DR OPF problems. Most practical uncertainty distributions in
power systems are unimodal. Exact reformulations, approximations, and efficient
solving techniques were developed in a previous paper. This paper develops an
optimal parameter selection approach that searches for an optimal
approximation, significantly improving the computational efficiency and
solution quality. We evaluate the performance of the approach against existing
chance-constrained OPF approaches using modified IEEE 118-bus and 300-bus
systems with high penetrations of renewable generation. Results show that
including unimodality information reduces solution conservatism and cost
without significantly degrading reliability.
