Tuesday, December 19, 201710:30 AM - 12:00 PMCNLS Conference Room (TA-3, Bldg 1690)|
Distributionally Robust Contingency-Constrained Unit Commitment
Ruiwei JiangUniversity of Michigan at Ann Arbor
In this talk, we propose 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. 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. This is a joint work with Chaoyue Zhao (Oklahoma State University), and is supported in part by the National Science Foundation via grants CMMI-1555983 and CMMI-1662774.
Host: Russell Bent