[2604.02788] Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees

Computer Science > Machine Learning arXiv:2604.02788 (cs) [Submitted on 3 Apr 2026] Title:Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees Authors:Guangwen Wang, Jiaqi Wu, Yang Weng, Baosen Zhang View a PDF of the paper titled Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees, by Guangwen Wang and 2 other authors View PDF 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 th...