On Affine Policies for Wasserstein Distributionally Robust Unit Commitment

TL;DR

This paper introduces a data-driven Wasserstein distributionally robust optimization model for unit commitment in power systems, effectively handling renewable uncertainty with an affine policy and a novel uncertainty set, ensuring computational efficiency and robustness.

Contribution

It presents a tractable reformulation of WDRO for unit commitment using affine policies and a probabilistic uncertainty set, scalable with sample size and suitable for practical power system applications.

Findings

The model achieves computational efficiency on test systems.

It effectively manages renewable uncertainty with robustness.

Numerical results show economic benefits and scalability.

Abstract

This paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable generation as well as its tractable exact reformulation. The proposed model is formulated as a WDRO problem relying on an affine policy, which nests an infinite-dimensional worst-case expectation problem and satisfies the non-anticipativity constraint. To reduce conservativeness, we develop a novel technique that defines a subset of the uncertainty set with a probabilistic guarantee. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms. Notably, the scale of this reformulation is invariant with the sample size. As a result, a number of samples are easily incorporated without using sophisticated decomposition algorithms. Numerical simulations on 6- and 24-bus test systems demonstrate the economic and computational efficiency of the proposed model.