On Affine Policies for Wasserstein Distributionally Robust Unit Commitment

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.

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