Frequency Constrained Scheduling Under Multiple Uncertainties via Data-Driven Distributionally Robust Chance-Constrained Approach
Lun Yang, Zhihao Li, Yinliang Xu, Jianguo Zhou, Hongbin Sun
Abstract
The declining system inertia in renewable-rich power systems raises a concern about the frequency stability problem. The wind farm equipped with the power electronic controller is capable of providing frequency support after a disturbance. However, both virtual inertia provision and wind power from wind farms are time-varying and uncertain. To account for this issue, we propose a data-driven distributionally robust (DR) chance-constrained approach for the frequency constrained scheduling problem, which simultaneously optimizes the unit commitment, generation dispatch, regulation reserves, and frequency responses. This approach explicitly considers frequency constraints and formulates virtual inertia uncertainty- and wind power uncertainty-related operational/frequency constraints as DR chance constraints under Wasserstein-metric ambiguity sets, which can limit the risk of constraint violations. Case studies demonstrate the effectiveness of the proposed approach and show that the proposed approach can achieve a desirable trade-off between operational cost and constraint violations.