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

arXiv: https://arxiv.org/abs/2012.07116 · HTML: https://mlsanity.com/paper/2012.07116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Forecasting Methods for System-Level Electricity Load Forecasting](https://mlsanity.com/paper/2210.09399/) (2022)

## Abstract (arXiv)

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.