## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.4. Day-Ahead Scheduling DHB Strategy Evaluation  
The results of the day-ahead scheduling DHB strategy are presented in Table 6. By comparing the total cost of each case, it can be seen that as the degree of freedom for the hy  
Figure 5: Sensitivity analysis of hydrogen blending ratio. Figure 6: Distribution of hydrogen blending ratio in Case 4.  
drogen blending ratio increases, the total scheduling cost gradually decreases. Specifically, compared to Case 1, the total cost of Cases 2, 3, and 4 decrease by 1.07%, 1.84%, and 1.93%, respectively. In Case 4, the CHB strategy resulted in a 0.87% cost reduction compared to the optimal fixed hydrogen blending ratio strategy from Case 2. Given the large base of the total cost, this optimization effect remains significant, indicating that further increasing the freedom of the hydrogen blending ratio plays a crucial role in optimizing system cost. This optimization effect can be explained theoretically: when the hydrogen blending ratio is fixed, the system's flexibility is limited and cannot adjust dynamically in response to real-time loads, electricity prices, an other factors. This increases the complexity and cost of scheduling. However, when the hydrogen blending ratio can be dynamically adjusted, the system can flexibly optimize the ratio of natural gas and hydrogen based on load demand and electricity market price fluctuations, thereby optimizing energy production and consumption and reducing unnecessary energy waste. It is this flexibility that leads to the gradual reduction in total scheduling costs. At the same time, the THB strategy in Case 3 also achieves a cost reduction of 0.79% compared to the optimal fixed hydrogen blending ratio (Case 2). Although it is slightly lower than the CHB strategy, the time-based adjustment method effectively reduces the operational complexity caused by frequent changes in the hydrogen blending ratio. Additionally, adjusting the blending ratio from a variable to a constant avoids generating secondary constraint conditions in the optimization model, reducing the solution time from 60 s to 12 s, an 80% reduction. In practical applications, when considering factors such as model prediction errors, communication delays, and actuator precision, the marginal benefits of pursuing a theoretically continuous optimal solution (CHB) may be limited. In contrast, the simplified scheme offered by THB greatly enhances the feasibility and efficiency of engineering implementation while securing the vast majority of economic benefits. Therefore, for cases emphasizing economic efficiency, the DHB strategy can be considered, while for cases emphasizing engineering simplification and fast scheduling, the THB strategy may be preferred. However, enhanced operational flexibility may come at the expense of environmental trade-offs. Regarding carbon emissions, although the increased flexibility in the DHB strategy slightly raises carbon emissions, the increase is relatively small. This is mainly due to the constraints of the stepped carbon trading mechanism and the contribution of carbon reduction devices. As shown in Table 6, the increase in carbon trading cost is significant due to the punitive effect of the stepped carbon trading mechanism. Since high carbon emissions face more severe penalties, the system's carbon emission level has been effectively controlled. Furthermore, the flexible scheduling of CCS and MR helps offset some of the increase in carbon emissions, allowing the system to maintain low carbon emissions while ensuring economic efficiency.