## 3 Example Analysis
### Simulation Analysis of Scenario 2  
In order to highlight the superiority of the OGGWO algorithm proposed in this paper in Scenario 2, the algorithm was compared with other classic optimization algorithms, and the results are shown in Table 6. Table 6 provides a comparative analysis of the statistical performance of OGGWO versus other optimization algorithms across individual objectives. When evaluating the outcomes of the GWO algorithm against the proposed OGGWO  
Figure 4: Emission aggregation characteristics of Scenario 1. Figure 5: The emission cost balancing characteristics of Scenario 1.  
algorithm across different objectives, it becomes evident that the GWO algorithm exhibits certain deficiencies and variable performance levels. Table 7 highlights the efficacy of the proposed algorithm in handling multi-objective optimization and the optimal distribution of power among units. The data in this table suggest that the deployment of high-performance units is crucial for simultaneously achieving economic and emission targets. From Table 7, it can be inferred that compared to other optimal solutions, the proposed algorithm appropriately handles multi-target optimization problems while also ensuring the minimum diversity of each objective and experiment. In contrast to the primary scenario, the overall operational expenses of the microgrid have arisen in the secondary scenario. However, when compared to the earlier state, there has been a reduction in net emissions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Parameter** & **Cost (CNY)** & **Emissions (Kg)** \\ \hline \multirow{5}{*}{EDNSGA-II [23]} & Minimum cost & 1491.69 & 1231.56 \\  & Minimum & 1586.34 & 1162.4 \\  & emissions & BCS & 1573.12 & 1215.63 \\  & standard deviation & 42.15 & 1215.63 \\  & Minimum cost & 1520.18 & 1205.29 \\  & Minimum & 1589.91 & 1168.12 \\  & emissions & BCS & 1554.07 & 1184.89 \\  & standard deviation & 46.80 & 7.63 \\  & Minimum cost & 1513.45 & 1203.14 \\  & Minimum & 1573.71 & 1166.58 \\  & emissions & BCS & 1540.06 & 1183.28 \\  & standard deviation & 45.31 & 7.58 \\  & Minimum cost & 1484.88 & 1200.23 \\  & Minimum cost & 1551.88 & 1160.25 \\  & emissions & 1502.96 & 1179.27 \\  & standard deviation & 37.25 & 6.64 \\  & Minimum cost & 1464.54 & 1197.38 \\  & Minimum cost & 1587.72 & 1158.03 \\  & emissions & & \\  & BCS & 1490.75 & 1177.49 \\  & standard deviation & 34.67 & 4.63 \\  & Minimum cost & 1457.80 & 1210.24 \\  & Minimum & 1580.05 & 1156.79 \\  & emissions & BCS & 1473.14 & 1175.82 \\  & standard deviation & 33.34 & 4.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the simulation results in Scenario 2. Figures 6 and 7 depict the convergence trends for operating costs and emissions of the OGGWO, GGWO, GWO, CSA, DE, and PSO algorithms. The figures reveal that the OGGWO algorithm exhibits consistent and rapid convergence in locating the optimal solution, suggesting the Pareto optimal frontiers for both OGGWO and GGWO algorithms. These illustrations suggest that the OGGWO algorithm is a viable option for addressing multi-objective energy management challenges, outperforming other optimization methods. Furthermore, the OGGWO algorithm presented in this study ensures an optimal spread of non-dominated solutions and is suitable for extensive standard testing systems. \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** \\ **(CNY/kWh)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Grid** \\ **(MW)** \\ \end{tabular}