Scenario reduction is a methodological approach employed in the analysis and optimization of systems under uncertainty. It addresses the computational challenges posed by a large number of scenarios representing possible future states or parameter realizations by selecting a representative subset of these scenarios. The technique aims to preserve essential statistical or structural properties relevant to the problem, thereby enhancing computational tractability and enabling the solution of complex stochastic or robust optimization models that would otherwise be intractable.