Research on a Low-Carbon Economic Dispatch Model and Control Strategy for Multi-Zone Hydrogen Hybrid Integrated Energy Systems

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model.