  This paper proposes a distributionally robust unit commitment approach for
microgrids under net load and electricity market price uncertainty. The key
thrust of the proposed approach is to leverage the Kullback-Leibler divergence
to construct an ambiguity set of probability distributions and formulate an
optimization problem that minimizes the expected costs brought about by the
worst-case distribution in the ambiguity set. The proposed approach effectively
exploits historical data and capitalizes on the k-means clustering algorithm,
in conjunction with the soft dynamic time warping score, to form the nominal
probability distribution and its associated support. A two-level decomposition
method is developed to enable the efficient solution of the devised problem. We
carry out representative studies and quantify the relative merits of the
proposed approach vis-\`a-vis a stochastic optimization-based model under
different divergence tolerance values.
