## 5 Simulation and Numerical Results  
A sample system consisting of a wind unit, electricity to P2G converter, and CAES in the presence of flexible loads is considered to assess the techno-economic analysis of an MCMG. Table 2 shows the specifications of CAES. Also, characteristics of P2G can be found in [62]. The DRP cost is $0.05/kWh. The emission factor for the power purchased from the grid and load shedding cost is 0.54 kg/kWh and $5/kWh. A scenario-based probabilistic approach is extended in the model to handle the variations of wind power, power price, and load demand. The Monte-Carlo simulation is utilized to generate a large number of scenarios, and then after, the generated scenarios are reduced to 10 using the backward selection technique in MATLAB to burden the complexity of computation. It should be noted that Weibull and Normal distribution function are used to model the wind power, load, and price uncertainties. More information can be found in [63; 64]. This paper presumes that 10% of the total local demand contributes to DRP as in [65; 66]. Figure 3 shows the power and gas market price curves. The load demand and wind power forecasts are shown in Figure 4.  
To investigate the proposed eco-emission scheduling, two case study are discussed:  
* **Case 1:** Coordinated operation of the MCMG in the presence of CAES and P2G, neglecting the environmental goal; \begin{table}
\begin{tabular}{c c c c c c} \hline Min Charging/Discharging (kW) & Max Charging/Discharging (kW) & Max Simple Cycle (kW) & Capacity (kWh) & Energy ratio (\%) & \(\gamma^{dis}\) & \(\gamma^{si}\) \\ \hline
0 & 150 & 150 & 650 & 80 & 0.36 & 0.36 \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of CAES technology. Figure 4: Forecasted Load demand and wind power price curves. Figure 3: Market price curve. * **Case 2:** Coordinated multi-objective scheduling of the MCMG in the presence of P2G and CAES. * **Case 1** The optimal operation of the proposed MCMG integrated with P2G, wind farm, and CAES technology is examined without considering the emission pollution limitation. The operator seeks to minimize total operation cost to optimally serve the local loads. The expected optimal scheduling of P2G in case 1 is shown in Figure 5. The P2G stores the surplus power at the first hours of the day (hours with lower energy prices). The stored power is converted into natural gas and injected into the grid at 11-13, 15, and 17-19, as shown in Figure 5. Herein, the operator contributes to more economic savings stemmed from the economical purchase of gas during the discharge mode of the P2G. Besides, more wind power is integrated into the grid using P2G, which results in lower wind energy curtailment. The expected optimal operation of CAES technology in case 1 is shown in Figure 6. As can be seen, CAES does not operate in the simple-cycle mode. CAES is charged during off-peak hours (1-5 and 16-18). When the power market price reaches higher values, CAES is discharged and supplies the local load. The CAES's energy capacity is also shown in Figure 6, which follows the optimal charging/discharging scheme. Figure 5: Optimal operation of P2G in Case 1. The hourly power and natural gas purchased from the power and gas market are shown in Figure 7 for case 1. In this case, the emission limitation is overlooked; thus, the operator neglects the environmental constraints and contributes to economic savings. As illustrated, the purchased power and gas curves follow the inverse behavior. This phenomenon occurs because of the energy price fluctuations. For periods 12-15, when the system requires more electricity, the operator purchases more power from the power market. Hence, the gas purchase is reduced to contribute to more economic benefits. However, the required natural gas at this period is mainly provided by P2G that is operating in discharging mode and subsequently reducing MCMGs' reliance on the gas network. The DRP, based on the shifting capability, is used in this paper to increase system flexibility.