Distributionally Robust Resilient Operation of Integrated Energy Systems Using Moment and Wasserstein Metric for Contingencies

Extreme weather events pose a serious threat to energy distribution systems. We propose a distributionally robust optimization model for the resilient operation of the integrated electricity and heat energy distribution systems in extreme weather events. We develop a strengthened ambiguity set that incorporates both moment and Wasserstein metric information of uncertain contingencies, which provides a more accurate characterization of the true probability distribution. We first recast the proposed model into an equivalent framework which is similar to a conventional two-stage robust model and then utilize a modified column-and-constraint generation algorithm to solve the recast model. Numerical results from three test systems validate the enhanced resilience of the distributionally robust approach, the reduced conservatism of the strengthened ambiguity set, and the computational efficiency of the proposed solution algorithm.