Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a lowcarbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.

Automated summary

This study proposes a hydrogen-based integrated energy system (HIES) operation framework and develops a distributional robust optimization method to address wind power uncertainty. Numerical analysis shows that the proposed model has advantages in low carbon emissions, economic performance, environmental impact, and energy efficiency. It provides valuable reference for HIES investment and carbon pricing decisions.