# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Results Analysis
#### 5.2.3 Best Compromise Solution Analysis  
Subsequently, in the decision-making step, we selected the best compromise solutions (BCSs) from each cluster separately through GRP. BCSs are the solutions that best balance the objectives of the system according to the decision-maker's preferences. This step is crucial for narrowing down the range of feasible solutions to those most aligned with strategic goals.  
To verify the effectiveness of the two-step method, three different scenarios were considered. Before making a decision, a Parato optimal solution corresponding to LPSP and weight of the system can be obtained based on the decision variables. After independent simulation runs, 100 Pareto optimal solutions and corresponding individuals can be obtained. The comparison results of three different representative solutions are shown in Table 2.  
BCS 1: The optimized best compromise solution 1 reflects the decision-maker's preference for maneuverability.  
BCS 2: The optimized best compromise solution 2 reflects the decision maker's preference for reliability.  
Table 2 indicates that the two-step approach can provide reasonable solutions based on the different preferences of decision-makers. Specifically, the LPSP in the optimized BCS 1 decreased by 7.4% compared to the initial solution before decision-making. The LPSP in the optimized BCS 2 is 1.5% lower than the initial solution before decision-making. The reason for this result is that the LPSP of both BCS is reduced through the optimization of the two-step method, while the reduction in LPSP in BCS 1 is greater due to the preference for maneuverability. The same is true for the explanation of weight.  
By applying this two-step decision-making process, we successfully distilled the broad set of solutions provided by CPS-MOEA into a more manageable set of optimal configurations. This approach ensures that the final selections are not only technically viable but also aligned with the practical needs and preferences of the system operators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Before Decision-Making**} & \multicolumn{2}{c}{**After Decision-Making**} \\  & **Initial Solution** & **BCS1** & **BCS2** \\ \hline LPSP (\%) & 17.46 & 16.17 & 17.20 \\ Weight (kg) & 6.38 & 3.93 & 1.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of three different solutions.  
Figure 6: PF distribution of CPS-MOEA after clustering.  
From the above analysis, it can be seen that BCS can be obtained through GRP. In the process of extracting BCS, GRP is first used to calculate the priority membership degree of POSs, and then the solution with the highest priority membership degree is selected as the BCS. To reasonably evaluate the optimization performance of the proposed method, CPS-MOEA was compared and tested with seven other algorithms (VaEA, NSGAIII, tDEA, IBEA, BiGE, MOPSO, and KnEA). Here, the priority members of these algorithms are calculated using GRP. The comparison results are shown in Figure 7. We can notice that CPS-MOEA and GRP show higher priority than other MOEAs, which demonstrates the efficiency of the method.