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

arXiv cs.LG / 4/6/2026

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Key Points

The paper targets the high computational cost of network-constrained unit commitment (UC), where most runtime comes from branch-and-bound over unit-hour binary decisions under many grid and security constraints.
It proposes a solver-preserving dimensionality reduction approach that uses structural regularities to pre-fix only a sparse subset of “stable” commitment binaries rather than predicting an entire schedule.
An LLM can be used to select which variables to fix, but the MILP solver remains responsible for enforcing all operational constraints (network, ramping, reserves, security) and completing the remaining optimization.
The authors prove that the resulting masked (restricted) UC problem preserves feasibility by defining a reduced feasible region of the original model, enabling solver-certified optimality within the restricted space.
Experiments on multiple IEEE test systems, including security-constrained and large-scale variants, show order-of-magnitude reductions in branch-and-bound nodes and solution time 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.