Relax-and-Cut for Temporal SCUC Decomposition

TL;DR

This paper introduces a relax-and-cut framework for the SCUC problem that improves solution quality and computational efficiency by extending the optimization horizon and selectively incorporating contingency constraints.

Contribution

The paper presents a novel relax-and-cut approach with dynamic contingency constraint integration and extended horizon decomposition for large-scale SCUC problems.

Findings

Achieves optimality gaps below 1%

Reduces computation time by 80% compared to monolithic solutions

Improves primal gaps by 60% over existing methods

Abstract

The Security-Constrained Unit Commitment (SCUC) problem presents formidable computational challenges due to its combinatorial complexity, large-scale network dimensions, and numerous security constraints. While conventional temporal decomposition methods achieve computational tractability through fixed short-term time windows, this limited look-ahead capability often results in suboptimal, myopic solutions. We propose an innovative relax-and-cut framework that alleviates these limitations through two key innovations. First, our enhanced temporal decomposition strategy maintains integer variables for immediate unit commitment decisions while relaxing integrality constraints for future time periods, thereby extending the optimization horizon without compromising tractability. Second, we develop a dynamic cutting-plane mechanism that selectively incorporates N-1 contingency constraints during the branch-and-cut process, avoiding the computational burden of complete upfront enumeration. The framework optionally employs a Relaxation-Induced Neighborhood Search procedure for additional solution refinement when computational resources permit. Comprehensive numerical experiments demonstrate the effectiveness of our approach on large-scale systems up to 13,000 buses. The proposed method can achieve optimality gaps below 1% while requiring only 20% of the computation time of monolithic Gurobi solutions. Compared to existing decomposition approaches, our framework provides superior performance, simultaneously reducing primal gaps by 60% and doubling solution speed. These significant improvements make our method particularly well-suited for practical SCUC implementations where both solution quality and computational efficiency are crucial.