Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment
نویسندگان
چکیده
Unit Commitment (UC) is important for power system operations. With increasing challenges, e.g., growing intermittent renewables and intra-hour net load variability, traditional mathematical optimization could be time-consuming. Machine learning (ML) a promising alternative. However, directly good solutions difficult in view of the combinatorial nature UC. This paper synergistically integrates ML within our recent decomposition coordination method Surrogate Lagrangian Relaxation to learn “good enough” subproblem deterministic Compared original UC, much easier learn. Nevertheless, predicting good-enough still challenging because “jumps” binary decisions many types constraints. To overcome these issues, dimensionality reduced via aggregating multipliers. Multiplier distributions are novelly specified based on effective learning. Loss functions innovatively designed improve prediction qualities. Ordinal Optimization branch-and-cut used as backups unfamiliar cases. Furthermore, online self-learning seamlessly integrated with offline exploit from daily Results IEEE 118-bus Polish 2383-bus demonstrate that continual keeps improving subproblem-solving process near-optimality overall maintained. Our opens new direction solve complicated
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15 صفحه اولOptimization for Machine Learning
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ژورنال
عنوان ژورنال: IEEE Transactions on Power Systems
سال: 2023
ISSN: ['0885-8950', '1558-0679']
DOI: https://doi.org/10.1109/tpwrs.2023.3240106