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

TL;DR

This paper introduces a distributionally robust unit commitment approach using Kullback-Leibler divergence to handle net load uncertainty without specifying exact probability distributions, improving power system planning with renewable integration.

Contribution

It develops a novel methodology that models uncertainty via ambiguity sets based on Kullback-Leibler divergence, avoiding the need for explicit probability distribution assumptions.

Findings

Effective on real-world data

Sensitivity analysis shows impact of divergence tolerance

Robust solutions under various dataset sizes

Abstract

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.