Vertex-Guided Redundant Constraints Identification for Unit Commitment

TL;DR

This paper introduces a vertex-guided method to efficiently identify redundant constraints in the power system Unit Commitment problem, significantly reducing computational effort while maintaining accuracy.

Contribution

It proposes a novel LP-based screening approach that uses vertex analysis to quickly find redundant constraints in UC models, improving speed and efficiency.

Findings

Achieves up to 8.8x acceleration over classic methods

Successfully identifies all redundant constraints in large-scale testbeds

Reduces the number of LPs needed for constraint screening

Abstract

Power systems Unit Commitment (UC) problem determines the generator commitment schedule and dispatch decisions to realize the reliable and economic operation of power networks. The growing penetration of stochastic renewables and demand behaviors makes it necessary to solve the UC problem timely. It is possible to derive lightweight, faster-to-solve UC models via constraint screening to eliminate redundant constraints. However, the screening process remains computationally cumbersome due to the need of solving numerous linear programming (LP) problems. To reduce the number of LPs to solve, we introduce a novel perspective on such classic LP-based screening. Our key insights lie in the principle that redundant constraints will be satisfied by all vertices of the screened feasible region. Using the UC decision variables' bounds tightened by solving much fewer LPs, we build an outer approximation for the UC feasible region as the screened region. A matrix operation is then designed and applied to the outer approximation's vertices to identify all redundant constraints on-the-fly. Adjustments for the outer approximation are further explored to improve screening efficiency by considering the load operating range and cutting planes derived from UC cost and discrete unit status prediction. Extensive simulations are performed on a set of testbeds up to 2,383 buses to substantiate the effectiveness of the proposed schemes. Compared to classic LP-based screening, our schemes can achieve up to 8.8x acceleration while finding the same redundant constraints.