## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.7 Norm Constraint Parameter Analysis  
In the DRO model, the use of \(\alpha_{1}\)- and \(\alpha_{\infty}\)-norm constraints is crucial for managing the uncertainties in scenario generation. By adjusting the confidence parameters, the preferences of decision-makers regarding risk can be accommodated. These constraints are used to limit the confidence intervals of the probability distribution, ensuring that the scenario generated remains within a reasonable range, thereby optimizing cost and efficiency while mitigating risks. In this study, simulations were conducted to model a moderate trade-off between system reliability and economic efficiency under the conditions of \(\alpha_{1}=0.5\) and \(\alpha_{\infty}=0.8\). The total scheduling cost under these norm constraints was then compared, as shown in Figure 7.  
As shown in Figure 7, with an increase in the values of \(\alpha_{1}\) and \(\alpha_{\infty}\), the total cost gradually rises. This indicates that as these two parameters increase, the optimization model becomes more conservative. Higher \(\alpha\) values suggest that the model focuses more on dealing with extreme scenarios, and the system adopts more cautious scheduling strategies to reduce risks. This conservative strategy leads to increased use of redundant resources, which results in higher system costs, reflecting the trade-off between risk aversion and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Total Cost** \\ **(CNY)** \\ \end{tabular} & \begin{tabular}{c} **Carbon** \\ **Emission (t)** \\ \end{tabular} & \begin{tabular}{c} **Carbon Trading** \\ **Cost (CNY)** \\ \end{tabular} & \begin{tabular}{c} **IDR Cost** \\ **(CNY)** \\ \end{tabular} & \
\begin{tabular}{c} **Purchasing** \\ **Cost (CNY)** \\ \end{tabular} &
\begin{tabular}{c} **Operation Cost** \\ **(CNY)** \\ \end{tabular} \\ \hline A & 15,337.01 & 10.469 & 115.11 & 969.65 & 13,047.91 & 1204.33 \\ B & 15,385.77 & 10.189 & 77.66 & 1001.40 & 13,054.17 & 1252.53 \\ C & 15,243.81 & 12.451 & 490.60 & 937.22 & 12,758.72 & 1057.26 \\ D & 15,274.79 & 11.915 & 320.02 & 938.99 & 12,881.18 & 1134.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of the HCNG-CCS-P2G system architecture.  
Figure 7: Norm constraint parameter analysis. (**a**) 1-norm. (**b**) Infinity-norm.  
cost increase. Therefore, selecting appropriate confidence parameters can flexibly meet the decision-maker's risk preferences.  
Comparing the use of norm constraints individually with the combination of norm constraints, the trend in both figures is consistent: combined norm constraints typically exhibit a lower total cost. For example, in Figure 7b, when \(\alpha_{\infty}=0.99\), the total cost with combined norm constraints is CNY 15,287, which is lower than the total cost with the use of \(\alpha_{\infty}\)-norm constraints alone (CNY 15,339). This demonstrates that combined norm constraints can mitigate the cost increase resulting from conservative optimization while ensuring system stability, thereby achieving greater economic efficiency and flexibility, making them more advantageous as DRO constraint conditions.