# A Distributionally Robust Optimization Approach for Unit Commitment in Microgrids

arXiv: https://arxiv.org/abs/2011.05314 · HTML: https://mlsanity.com/paper/2011.05314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 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)

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.