Distributionally robust optimization for multi-hub interconnected integrated energy systems capturing renewable uncertainties

The renewable energy (i.e., solar and wind) output is weather-dependent and intermittent, and its increasing penetration into our energy system could induce reliability and stability issues. The integrated energy systems (IES) are expected to address such renewable uncertainties via multi-vector complementary and flexible interactions. In this paper, we propose a distributionally robust optimization model for the operation of a multi-hub interconnected IES capturing renewable uncertainties. To do this, a three-hub interconnected IES model is developed based on the shapely value method at first, which contains the energy vectors of electricity, heating and cooling. Then an uncertainty set of wind and solar output is constructed based on the Wasserstein distance to build a distributionally robust optimization model for the IES operation. Lastly, the model is transformed into an easy-to-solve MILP model by affine strategy and duality theory. A case study in a three-hub interconnected IES indicates that: (1) coordinated scheduling can save the overall operation and scheduling cost of the IES by 13.7% compared to that three hubs operation independently; (2) the distributionally robust optimization method based on the Wasserstein distance can effectively cope with the uncertainties of renewable output and guarantee the robustness of the system, but at the expense of 37.2% cost.