An Efficient Robust Solution to the Two-Stage Stochastic Unit Commitment Problem

TL;DR

This paper introduces a novel reformulation for the two-stage stochastic unit commitment problem that balances expected performance and worst-case scenarios by clustering uncertainties, enabling efficient decomposition and parallel computation.

Contribution

It presents a new scenario clustering approach for the problem's reformulation, improving computational efficiency and robustness in unit commitment planning.

Findings

Enhanced solution robustness in uncertain environments

Improved computational efficiency through decomposition

Balanced performance in expectation and worst-case scenarios

Abstract

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.