Data-Driven Distributionally Robust Chance-Constrained Unit Commitment With Uncertain Wind Power

The Unit commitment (UC) problem in power systems has been studied for a long time; however, many new challenges have emerged in the UC problem with the increasing penetration of renewable generation which is intermittent and uncertain. Compared with the common uncertainty modeling methods including stochastic programming and robust optimization, in this paper, we develop a data-driven distributionally robust chance-constrained (DDRC) UC model. The proposed two-stage UC model focuses on the commitment decision and dispatch plan in the first stage, and considers the worst-case expected cost for possible power imbalance or re-dispatch in the second stage. To capture the uncertainty of wind power distribution, a distance-based ambiguity set is designed which can be constructed in a data-driven manner. Based on the ambiguity set, the original complicated UC problem is reformulated to a tractable optimization problem which is then solved by the column-and-constraint generation (CCG) algorithm. The performance of the the proposed approach is validated by case studies with different test systems including the IEEE 6-bus test system, modified IEEE 118-bus system and a practical-scale system, especially the value of data in controlling the conservativeness of the problem.