## 5 Example Analysis
### Result Analysis  
In this paper, a day is divided into three periods: peak, flat, and valley periods. The price of the system running in the three periods is different from that of the superior power grid. In order to make the transaction cost lower, the system needs to meet the low load required in the peak period and the peak load of user demand in the low period. The model proposed in this paper can achieve peak-cutting and low-filling and reduce the load transaction cost for users. In order to verify the effectiveness and rationality of the optimal scheduling model of the RIES system considering demand response under the carbon trading mechanism proposed in this paper, RIES is set up to run in the following four modes, as shown in Table 2, and the electricity is shown in Table 3. The pollutant emission factor and pollutant treatment cost are shown in Table 4.  
When RIES is running in model 4, the electricity, cold, heat, and gas loads after demand response and the original load are shown in Figure 9a-d; besides, Figure 10a-d shows the output of each equipment under the conditions of electricity, heat, cold, and gas balance. Figure 9 shows the comparison curves of user-side electric, cold, heat, and gas loads before and after DR. After considering the DR, due to the response of the load to the electricity price, the load transfer cost is lower than the energy purchase cost in the two periods of 9:00-12:00 and 19:00-22:00 when the electricity price is higher, so the system chooses the load to be transferred to the low-price period, thus forming the trend of load peak load shifting. The peak value of the electric load before DR is 1690 kW and the valley value is 490 kW, while the peak value after DR is 1655 kW and the valley value is 635 kW. The peak-valley difference of the electric load is relatively reduced by 10.87%; besides, the peak value of the heat load before DR is 1584 kW and the valley value is 1008 kW, but the peak and valley values are 1586 kW and 1066 kW, respectively, under the DR strategy, and the difference between peak and valley of the heat load decreases \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of**} & \multirow{2}{*}{**Pollution Charge**} & \multicolumn{5}{c}{**Pollution Emission**} \\  & & & & **Factor/(kg/kWh)** & \\
**Pollutant** & **(Yuan/kg)** & **PV** & **WT** & **GT** & **GB** & **P-Grid** \\ \hline CO\({}_{2}\) & 0.018 & 0 & 0 & 0.659 & 0.489 & 0.889 \\ SO\({}_{2}\) & 5.950 & 0 & 0 & 0.000216 & 0.003 & 0.0018 \\ NO\({}_{x}\) & 8.510 & 0 & 0 & 0.01 & 0.008 & 0.0016 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pollutant discharge coefficient and pollution control cost. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Operating Mode Type** & **Operating Mode** \\ \hline model 1 & no demand response and carbon trading mechanism \\ model 2 & only demand response \\ model 3 & only carbon trading mechanism \\ model 4 & both demand response and carbon trading mechanisms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: System operational. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Period Type** & **Time Period** & **Electricity Purchasing** & **Electricity Selling** \\  & & **Price (Yuan)** & **Price (Yuan)** \\ \hline low period & 00:00–8:00 & 0.4 & 0.3 \\  & 8:00–9:00 & & \\ normal period & 12:00–19:00 & 0.8 & 0.55 \\  & 22:00–24:00 & & \\ peak period & 9:00–12:00 & 1.5 & 1.3 \\  & 19:00–22:00 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time-of-use electricity prices. by 3.61%. The peak value of the cooling load before DR is 1040 kW, and the valley value is 130 kW. However, under the DR strategy, the peak and valley values are 938 kW and 268 kW, respectively, and the peak and valley difference is relatively reduced by 12.65%; meanwhile, the peak-valley values of the exergy load before DR were 353 kW and 120 kW, respectively, while the peak-valley values after DR were 342 kW and 135 kW, respectively; that is, the peak-valley difference decreased by 12.29%.