  This paper proposes a reformulation of the scenario-based two-stage unit
commitment problem under uncertainty that allows finding unit-commitment plans
that perform reasonably well both in expectation and for the worst case
realization of the uncertainties. The proposed reformulation is based on
partitioning the sample space of the uncertain factors by clustering the
scenarios that approximate their probability distributions. It is, furthermore,
very amenable to decomposition and parallelization using a
column-and-constraint generation procedure.
