Optimized multi-unit coordinated scheduling based on improved IGDT: Low-carbon scheduling research for the electric-heat-oxygen integrated energy system

To address the oxygen supply demands and the challenges posed by high penetration of renewable energy in high-altitude regions, this paper proposes a low-carbon scheduling model for an electric-heat-oxygen integrated energy system (EHO-IES), designed for energy dispatch and management in these areas. The model integrates carbon capture and storage with power-to-gas (CCS-P2G), concentrated solar power plant (CSPP), combined heat and power (CHP) unit, and ground-source heat pump (GSHP). By optimizing the coordinated operation of multiple energy sources, the model enhances their complementarity and interaction. To effectively manage the multiple uncertainties in renewable energy and load, this study introduces an improved information-gap decision theory (IGDT) model, referred to as EWNS-IGDT. This model combines the entropy weight method (EWM) and non-dominated sorting genetic algorithm II (NSGA-II), improving the objectivity and rationality of uncertainty weight settings in risk-averse strategy (RAS) and risk-seeking strategy (RSS). The paper further analyzes the impact of these strategies on low-carbon scheduling. Case study results show that the coordinated operation of multiple units significantly reduces total cost (by 89.93 %) and carbon trading cost (by 97.95 %), while achieving near-complete integration of photovoltaic (PV) and wind turbine (WT) output. Under the RAS, total cost increased by 20 %, and carbon trading cost rose by 90.06 %. In contrast, under the RSS, total cost decreased by 19.98 %, while carbon trading cost significantly dropped by 321.90 %.