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The deepening penetration of renewable resources into power systems entailsgreat difficulties that have not been surmounted satisfactorily. An issue thatmerits special attention is the short-term planning of power systems under netload uncertainty. To this end, we work out a distributionally robust unitcommitment methodology that expressly assesses the uncertainty associated withnet load. The principal strength of the proposed methodology lies in itsability to represent the probabilistic nature of net load without having to setforth its probability distribution. This strength is brought about by thenotion of ambiguity set, for the construction of which the Kullback-Leiblerdivergence is employed in this paper. We demonstrate the effectiveness of theproposed methodology on real-world data using representative studies. Thesensitivity analyses performed provide quantitative answers to a broad array ofwhat if questions on the influence of divergence tolerance and dataset size onoptimal solutions.

Kullback-Leibler Divergence-Based Distributionally Robust Unit Commitment Under Net Load Uncertainty