Model Predictive Control Scheduling Strategy for Hydrogen-doped Integrated Energy System Considering Ladder Carbon Trading

To achieve low-carbon economic operation of hydrogen-doped integrated energy systems while mitigating the stochastic impact of new energy outputs on the system, the coordinated operation mode of hydrogen-doped gas turbines and electrolyzers is focused on, as well as a hybrid energy storage scheme involving both hydrogen and heat storage and an optimized scheduling model for integrated energy systems encompassing electricity-hydrogen-heat-cooling conversions is established. A model predictive control strategy based on deep learning prediction and feedback is proposed, and the effectiveness and superiority of the proposed strategy are demonstrated using error penalty coefficients. Moreover, the introduction of hydrogen energy exchange and ladder carbon trading is shown to effectively guide the low-carbon economic operation of hydrogen-doped integrated energy systems across multiple typical scenarios. A sensitivity analysis is conducted based on this framework, revealing that increases in the hydrogen doping ratio of turbines and the carbon base price led to higher system operation costs but effectively reduce carbon emissions.