Influence of Stochastic Dependence on Network Constraints Screening for Unit Commitment

The deepening penetration of renewable energy is challenging how power system operators cope with the associated variability and uncertainty in the unit commitment problem. Given its computational complexity, several optimization-based methods have been proposed to lighten the full unit commitment formulation by removing redundant line flow constraints. These approaches often ignore the spatial couplings of multi-side renewable generation and demand. To address this pitfall, we rule out redundant constraints over a tightened linear programming relaxation of the original unit commitment feasibility region by adding a constraint that efficiently models the correlation of residual demand variations. We set forth a novel, tractable and robust polyhedral uncertainty envelope induced by a given set of scenarios to characterize the tightening constraint. We propose a data-driven umbrella constraint discovery problem formulation that substantially increase the network constraints filtration in unit commitment. Numerical tests are performed on standard IEEE test networks to substantiate the effectiveness of the approach.