Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees

2026-04-03

AI Summary

This study addresses the computational inefficiency of branch-and-bound methods in solving large-scale security-constrained unit commitment problems, which arises from the vast number of decision variables. The authors propose a structure-aware dimensionality reduction framework that leverages large language models to identify and pre-fix a small subset of structurally stable on/off binary variables. This approach substantially reduces problem size while preserving the full modeling capabilities of mixed-integer linear programming solvers for handling network, ramping, reserve, and security constraints. Under the guarantee of solution feasibility and solver-certified optimality, the method significantly decreases both the number of branch-and-bound nodes and solution time across multiple IEEE benchmark systems and large-scale security-constrained scenarios, achieving up to an order-of-magnitude speedup on high-complexity instances with near-optimal objective values.

Abstract

The growing number of individual generating units, hybrid resources, and security constraints has significantly increased the computational burden of network-constrained unit commitment (UC), where most solution time is spent exploring branch-and-bound trees over unit-hour binary variables. To reduce this combinatorial burden, recent approaches have explored learning-based guidance to assist commitment decisions. However, directly using tools such as large language models (LLMs) to predict full commitment schedules is unreliable, as infeasible or inconsistent binary decisions can violate inter-temporal constraints and degrade economic optimality. This paper proposes a solver-compatible dimensionality reduction framework for UC that exploits structural regularities in commitment decisions. Instead of generating complete schedules, the framework identifies a sparse subset of structurally stable commitment binaries to fix prior to optimization. One implementation uses an LLM to select these variables. The LLM does not replace the optimization process but provides partial variable restriction, while all constraints and remaining decisions are handled by the original MILP solver, which continues to enforce network, ramping, reserve, and security constraints. We formally show that the masked problem defines a reduced feasible region of the original UC model, thereby preserving feasibility and enabling solver-certified optimality within the restricted space. Experiments on IEEE 57-bus, RTS 73-bus, IEEE 118-bus, and augmented large-scale cases, including security-constrained variants, demonstrate consistent reductions in branch-and-bound nodes and solution time, achieving order-of-magnitude speedups on high-complexity instances while maintaining near-optimal objective values.

Problem

• unit commitment
• network-constrained
• combinatorial complexity
• binary variables
• computational burden

Innovation

• structure-aware reduction
• unit commitment
• solver-preserving optimization
• large language models
• combinatorial dimensionality reduction