Optimal low-carbon operation of regional integrated energy systems: A data-driven hybrid stochastic-distributionally robust optimization approach

Uncertainties arising from the increasing renewable energy source (RES) penetration induced by the imposition of carbon emission penalties introduce substantial challenges to the optimal operations of regional integrated energy system(RIES). The present work addresses this issue by developing a data-driven hybrid stochastic-distributionally robust optimization approach for RIESs, in which carbon emission costs and uncertainties in RES output are considered. The approach involves a tri-level optimization problem that is transformed into three single-level problems by the column-and-constraint generation algorithm, and the stochastic optimization problem is solved in the master problem with the worst-case probability distributions obtained by the subproblems. In addition, the stochastic programming of the master problem is built on clusters of RES output scenarios with similar characteristics, and the distributionally robust programming of the subproblems is built on the scenarios in each cluster. Numerical results demonstrate that the proposed hybrid optimization approach results in lower operation cost than conventional distributionally robust approaches and exhibits higher robustness than conventional stochastic approaches. In addition, we investigate the impacts of carbon emission costs and the level of RES penetration on the obtained low-carbon operating strategy.