A two-stage distributionally robust low-carbon operation method for antarctic unmanned observation station integrating virtual energy storage and hydrogen waste heat recovery

To reduce the carbon emissions of Antarctic unmanned observation station (UOS) operations, this paper proposes a two-stage distributionally robust low-carbon operation method, integrating virtual energy storage (VES) and hydrogen waste heat recovery (HWHR). First, a multi-energy complementary model incorporating wind, solar, hydrogen, and battery storage is developed for the UOS with a composite enclosure structure. The model accounts for wind turbine icing and photovoltaic snow coverage, and incorporates electro-thermal coupling between hydrogen energy systems and heat pumps (HPs). Then, a fuzzy set is constructed via the imprecise Dirichlet model (IDM), establishing the uncertainty set characterizing Antarctic wind and photovoltaic (WP) output and outdoor temperature at a specific confidence level. Further, a two-stage distributionally robust optimization strategy for the UOS considering VES and HWHR is developed, and the conservatism level can be adjusted by tuning uncertainty control parameters. Finally, the original UOS optimization problem is decomposed and solved iteratively using the Inexact Enhanced column-and-constraint generation (IE-C&CG) algorithm. The test results with real meteorological data from Antarctica demonstrate that our method effectively leverages the heating flexibility of the HP and hydrogen energy equipment in the UOS, and significantly reduces UOS carbon emissions while ensuring the required operating temperature for scientific equipment.