Distributionally Robust Contingency-Constrained Unit Commitment
Distributionally Robust Contingency-Constrained Unit Commitment
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DOI：
10.1109/tpwrs.2017.2699121
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发表时间：
2018
影响因子：
6.6
通讯作者：
Chaoyue Zhao;Ruiwei Jiang
中科院分区：
文献类型：
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作者：
Chaoyue Zhao;Ruiwei Jiang
提出了一种基于分布鲁棒优化的机组组合问题的方法。在我们的方法中，我们考虑了一种情况下，真实的概率分布的意外是模糊的，即，很难准确估计。而不是分配一个（固定）的概率估计为每个意外情况下，我们认为一组意外的概率分布（称为模糊集）的基础上的$N-K$安全标准和时刻信息。我们的方法考虑了所有可能的分布在模糊集，因此是分布鲁棒性。同时，由于该方法利用了矩信息，它可以受益于现有的数据，并成为更少的保守性比鲁棒优化方法。我们推导了一个等价的重新表述，并研究了求解模型的Benders分解算法。此外，我们扩展了模型，将风力发电的不确定性。6节点系统和IEEE 118节点系统的算例表明，与鲁棒优化方法相比，该方法提供了更少保守的机组组合决策。
This paper proposes a distributionally robust optimization approach for the contingency-constrained unit commitment problem. In our approach, we consider a case where the true probability distribution of contingencies is ambiguous, i.e., difficult to accurately estimate. Instead of assigning a (fixed) probability estimate for each contingency scenario, we consider a set of contingency probability distributions (termed the ambiguity set) based on the $N-k$ security criterion and moment information. Our approach considers all possible distributions in the ambiguity set, and is hence distributionally robust. Meanwhile, as this approach utilizes moment information, it can benefit from available data and become less conservative than the robust optimization approaches. We derive an equivalent reformulation and study a Benders’ decomposition algorithm for solving the model. Furthermore, we extend the model to incorporate wind power uncertainty. The case studies on a 6-Bus system and the IEEE 118-Bus system demonstrate that the proposed approach provides less conservative unit commitment decisions as compared with the robust optimization approach.