## 5 Community Integrated Energy System Model
### Analysis of Optimal Scheduling Results  
In order to verify the effectiveness of the proposed two-layer dynamic CIES low-carbon collaborative optimization operation scheduling model, as shown in Table 2, four different scenarios are set up. The scheduling results of CIES are shown in Table 3.  
Comparing scenario 3 and scenario 1, the system's carbon emissions are reduced by 92.1 t in each scheduling cycle after using the proposed carbon trading model. Compared with the traditional fixed carbon trading price model, the proposed carbon trading model reduces carbon emissions by 8.02%. Compared with the traditional scheduling method (scenario 1), the ES revenue, CCHP revenue, and LA consumer surplus in the scheduling results of the proposed method (scenario 4) increase by 23.77%, 8.62%, and 12.79%, respectively, and the carbon emission decreases by 16.34%. Therefore, the proposed two-layer dynamic low-carbon collaborative optimization scheduling model can reduce the system's carbon emissions and ensure its economical operation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\mu_{\text{e,q}}\) & 0.728 t/(MW\(\cdot\)h) & \(\gamma_{\text{e}}\) & 2100 CNY/MW\(\cdot\)h \\ \(\mu_{\text{h,q}}\) & 0.102 t/GJ & \(\lambda_{\text{e}}\) & 0.9 CNY/(MW\(\cdot\)h)\({}^{2}\) \\ \(\mu_{\text{e,r}}\) & 0.924 & \(\gamma_{\text{h}}\) & 1100 CNY/MW\(\cdot\)h \\ \(\mu_{\text{g,r}}\) & 0.488 & \(\lambda_{\text{h}}\) & 1.2 CNY/(MW\(\cdot\)h)\({}^{2}\) \\ \(H_{\text{p2g}}\) & 0.118 & \(\eta_{\text{re}}\) & 0.85 \\ \(H_{\text{peu}}\) & 0.144 & \(\eta_{\text{ice}}\) & 0.39 \\ \(a_{\text{e,iec}}\) & 0.0013 & \(c_{\text{h,bt}}\) & 0.05 \\ \(b_{\text{e,iec}}\) & 0.16 & \(P_{\text{bt,c}}^{\text{max}}\) & 350 kW \\ \(c_{\text{e,iec}}\) & 0 & \(P_{\text{bt,d}}^{\text{max}}\) & 350 kW \\ \(b_{\text{h,bt}}\) & 0.005 & \(H_{\text{hsat,c}}^{\text{max}}\) & 200 kW \\ \(b_{\text{h,bt}}\) & 0.11 & \(H_{\text{hsat,d}}^{\text{max}}\) & 150 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specific parameters of the system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Carbon Trading Model** & **Scheduling Strategy** \\ \hline
1 & No & Centralized scheduling \\
2 & Traditional fixed carbon trading price & Centralized scheduling \\
3 & The proposed multi-agent stage feedback carbon trading mode & The proposed two-layer dynamic \\
4 & The proposed multi-agent stage feedback carbon trading mode & distributed scheduling \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The CIES simulation scenario settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **ES Earnings** & **CCHP Earnings** & **LA Consumer** & **Carbon** \\  & & **Surplus** & **Emissions** \\ \hline
1 & CNY 6034 & CNY 8872 & CNY 9419 & 1006.7 t \\
2 & CNY 5904 & CNY 8415 & CNY 8973 & 994.3 t \\
3 & CNY 6072 & CNY 8781 & CNY 9532 & 914.6 t \\
4 & CNY 7468 & CNY 9637 & CNY 10624 & 841.3 t \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of CIER scheduling results in each scenario. The pricing strategy of the upper ES in the proposed optimal scheduling model is shown in Figure 4. In Figure 4a, the ES formulates a price strategy within the envelope of time-of-use electricity price and on-grid electricity price to provide a better price for both the CCHP and the LA than the power grid. The fluctuation trend in the hourly price of the ES is similar to the time-of-use price of the power grid, and the purchase price is similar to the load change trend. There are two peaks at 12:00-14:00 and 20:00-22:00. This is to encourage new energy CCHP power generation, decrease the amount of electricity purchased, and increase income. The change rule of the heat price curve is the same as that of the electricity price in Figure 4b.