期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 6, 页码 4940-4943出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3014808
关键词
Robustness; Measurement; Optimization; Data aggregation; Linear programming; Numerical models; Indexes; Dimensionality reduction; distributionally robust optimization; extremal distribution; Wasserstein metric
资金
- National Natural Science Foundation of China [51937005]
In this letter, we propose a tractable formulation and an efficient solution method for the Wasserstein-metric-based distributionally robust unit commitment (DRUC-dW) problem. First, a distance-based data aggregation method is introduced to hedge against the dimensionality issue arising from a huge volume of data. Then, we propose a novel cutting plane algorithm to solve the DRUC-dW problem much more efficiently than state-of-the-art. The novel solution method is termed extremal distribution generation, which is an extension of the column-and-constraint generation method to the distributionally robust cases. The feasibility and cost efficiency of the model, and the efficiency of the solution method are numerically validated.
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