## 3 Example Analysis
### Simulation Analysis of Scenario 1  
Scenario 1 involves the operation of all DGs and power grids under emission/economic restrictions as illustrated in [22]. Table 4 illustrates the optimal 24 h power generation plan to minimize costs and emission levels without evaluation criteria. From Table 4, it is evident that in the initial few hours of the day, the majority of the load demand is met by fuel cells existing within the power grid and utilities through common coupling points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of** & **Pollutant Control** & **Pollutant Emission Coefficient (g/kwh)** \\
**Pollutants** & **Costs (CNY)** & **MT** & **FC** & **Grid** \\ \hline CO & 10.18 & 0.047 & 0 & 0.079 \\ CO2 & 0.28 & 715 & 484 & 889 \\ NOX & 13.209 & 0.27 & 0.017 & 1.511 \\ SO2 & 67.136 & 0.038 & 0.0033 & 1.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pollution treatment cost and discharge coefficient. Figure 2: Wind and solar power output in two scenarios. Moreover, the microgrid's regulatory controller sequentially powers units and exports energy, boosting revenue during certain study periods while cutting net emissions. Conversely, the battery is recharged early each day, particularly taking advantage of lower-cost charging times. As the load curve peaks later in the day, the battery discharges. Incorporating renewable sources like solar and wind reduces pollution but raises operational expenses, making them economically impractical. Hence, it's essential to cap the energy from these sources within set emission/economic limits. The BCS parameter refers to the temperature coefficient of the bypass diode under short-circuit conditions, which is the rate at which the performance of the bypass diode (such as forward voltage) changes with temperature when the photovoltaic panel is operating under short-circuit conditions. In order to highlight the superiority of the proposed OGGWO algorithm in Scenario 1, this paper compared the simulation results with other classic optimization algorithms in Table 5. It can be seen that the standard deviation values of the cost and emission targets of the proposed OGGWO are 19.97 and 4.72, respectively, with a minimum cost of 1146.55, minimum emissions of 1262.74, and a BCS of 1223.83, which are the minimum values compared to other classical optimization algorithms. Figure 3 displays the outcomes of the optimization algorithm's analysis. The results indicate that the suggested optimization algorithm has yielded outstanding performance regarding cost and emission objectives for both the optimal and worst-case scenarios. Furthermore, it demonstrated a higher rate of convergence. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hour**} & \multirow{2}{*}{ \begin{tabular}{c} **MT** \\ **(MW)** \\ \end{tabular}  } & \multirow{2}{*}{**FC (MW)**} & \multirow{2}{*}{**PV (KW)**} & \multirow{2}{*}{ \begin{tabular}{c} **WT** \\ **(MW)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **BAT** \\ **(CNV/kWh)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Grid** \\ **(MW)** \\ \end{tabular}  } \\ \hline
1 & 17.28 & 30.04 & 0 & 9.95 & \(-\)30.12 & 37.88 \\
2 & 19.65 & 27.28 & 0 & 11.51 & \(-\)4.81 & 7.56 \\
3  
To highlight the effectiveness of the introduced OGGWO algorithm, Figures 3 and 4 map out the convergence patterns of OGGWO in comparison to other optimization techniques, aiming for the best possible solution and individual goals. The data indicate that the OGGWO approach hit the nadir for the cost objective after 705 cycles and then remained constant, while the GWO algorithm achieved convergence at 713 cycles. Furthermore, the algorithm detailed in this study reached its nadir at 734 cycles, as opposed to the 745 cycles required for the GWO algorithm to reach convergence.