Back-to-Back DC Transmission System Embedded Scheduling With Integrated Hydrogen Injection Modeling

As the issue of short-circuit currents becomes increasingly critical in contemporary ever-expanding power grids, back-to-back DC transmission systems (BDTSs) have emerged as a promising scheme to address such a concern. However, the study on the practical and accurate modeling of BDTSs in typical power system decision-making problems remains profoundly limited. To bridge this research gap, we formulate a novel BDTS embedded scheduling problem while considering the comprehensive hydrogen model, which incorporates the technologies of hydrogen production, storage, and utilization. In addition, a well-defined Wasserstein-distance-based ambiguity set that harnesses the Gaussian-based nominal distribution is leveraged to capture the renewable output uncertainty. By developing equivalent reformulations of the nonlinear constraints inherent in BDTSs and distributionally robust chance constraints, the concerned model is eventually recast as a mixed-integer second-order cone programming problem. The effectiveness and superiority of the proposed scheduling model are assessed on three representative test systems.