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A Multi-Timescale Learn-to-Optimize Method for Unit Commitment With Renewable Power
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DOI：10.1109/tpwrs.2026.3687255.png)
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Abstract
En 中文
The high penetration of volatile renewable power has created an urgent need for fast methods to solve large-scale security-constrained unit commitment (SCUC) problems. In this paper, we propose a multi-timescale learn-to-optimize (MT-L2O) method to efficiently solve large-scale stochastic SCUC. The coarse timescale serves as a learning-based presolving stage. At this scale, we propose a group-based multi-resolution formulation where machine learning techniques are used to predict integer variables and fix those with high confidence for finer timescales. Therefore, problems at finer timescales are reduced in size and can be solved more efficiently. Compared to existing works, when learning at the coarse scale, binary status constraints spanning multiple time periods become inactive. This not only significantly reduces data dimensionality but also eliminates the need for feasibility recovery measures targeting these constraints. At finer timescales, we develop constraint reduction methods for two types of constraints based on the on/off status determined at coarser timescales. Numerical experiments on IEEE 118-bus system, IEEE 300-bus system, and a 3266-bus system based on a practical provincial grid in China demonstrate that our methods obtain near-optimal solutions with an average performance gap not exceeding 0.4% and achieve speedups of 3.03 to 34.46 times compared to the commercial solver Gurobi.
Keywords:
Multi-timescale
learn-to-optimize
model reduction
security-constrained unit commitment
renewable power
Journal
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