  Two-stage robust unit commitment (RUC) models have been widely used for
day-ahead energy and reserve scheduling under high renewable integration. The
current state of the art relies on budget-constrained polyhedral uncertainty
sets to control the conservativeness of the solutions. The associated lack of
interpretability and parameter specification procedures, as well as the high
computational burden exhibited by available exact solution techniques call for
new approaches. In this work, we use an alternative scenario-based framework
whereby uncertain renewable generation is characterized by a polyhedral
uncertainty set relying on the direct specification of its vertexes. Moreover,
we present a simple, yet efficient, adaptive data-driven procedure to
dynamically update the uncertainty set vertexes with observed daily
renewable-output profiles. Within this setting, the proposed data-driven RUC
ensures protection against the convex hull of realistic scenarios empirically
capturing the complex and time-varying intra-day spatial and temporal
interdependences among renewable units. The resulting counterpart features
advantageous properties from a computational perspective and can be effectively
solved by the column-and-constraint generation algorithm until
$\epsilon$-global optimality. Out-of-sample experiments reveal that the
proposed approach is capable of attaining efficient solutions in terms of cost
and robustness while keeping the model tractable and scalable.
