  Scenario reduction (SR) aims to identify a small yet representative scenario
set to depict the underlying uncertainty, which is critical to scenario-based
stochastic optimization (SBSO) of power systems. Existing SR techniques
commonly aim to achieve statistical approximation to the original scenario set.
However, SR and SBSO are commonly considered into two distinct and decoupled
processes, which cannot guarantee a superior approximation of the original
optimality. Instead, this paper incorporates the SBSO problem structure into
the SR process and introduces a novel problem-driven scenario reduction
framework. Specifically, we transform the original scenario set in distribution
space into the decision applicability between scenarios in problem space.
Subsequently, the SR process, embedded by a distinctive problem-driven distance
metric, is rendered as a mixed-integer linear programming formulation to obtain
the representative scenario set while minimizing the optimality gap.
Furthermore, ex-ante and ex-post problem-driven evaluation indices are proposed
to evaluate the performance of SR. A two-stage stochastic economic dispatch
problem with renewable generation and energy storage validates the
effectiveness of the proposed framework. Numerical experiments demonstrate that
the proposed framework significantly outperforms existing SR methods by
identifying salient (e.g., worst-case) scenarios, and achieving an optimality
gap of less than 0.1% within acceptable computation time.
