Distributionally Robust Contingency-Constrained Unit Commitment

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 (fixe...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-01, Vol.33 (1), p.94-102
Main Authors: Zhao, Chaoyue, Jiang, Ruiwei
Format: Article
Language:English
Subjects:
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Distributional robustness
Generators
Indexes
Load modeling
Power transmission lines
Probability distribution
Robustness
Security
unit commitment
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2017.2699121