  This paper proposes a data-driven version of the Benders decomposition
algorithm applied to the stochastic unit commitment (SUC) problem. The proposed
methodology aims at finding a trade-off between the size of the Benders master
problem and the number of iterations until convergence. Using clustering
techniques, we exploit the information contained in the Lagrange multipliers of
the Benders subproblems in order to aggregate the optimality cuts, without
compromising the critical information that is passed to the master problem. In
addition, we develop an outer parallelization scheme that finds the optimal
solution of the SUC problem by solving a series of less computationally
intensive SUC instances for certain partitions of the scenario set. Our
computational results on the IEEE 3-Area RTS-96 power system, illustrate the
improved performance of our data-driven Benders algorithm, in terms of solution
time and problem size, compared both to the SUC extensive formulation and to
the prevailing single- and multi-cut Benders formulations.
