  The deepening penetration of renewable resources into power systems entails
great difficulties that have not been surmounted satisfactorily. An issue that
merits special attention is the short-term planning of power systems under net
load uncertainty. To this end, we work out a distributionally robust unit
commitment methodology that expressly assesses the uncertainty associated with
net load. The principal strength of the proposed methodology lies in its
ability to represent the probabilistic nature of net load without having to set
forth its probability distribution. This strength is brought about by the
notion of ambiguity set, for the construction of which the Kullback-Leibler
divergence is employed in this paper. We demonstrate the effectiveness of the
proposed methodology on real-world data using representative studies. The
sensitivity analyses performed provide quantitative answers to a broad array of
what if questions on the influence of divergence tolerance and dataset size on
optimal solutions.
