## 4 Discussion  
### Contributions  
This research develops a practical framework for optimizing and evaluating the PEDF system configuration. Two case studies are presented--one for newly constructed office buildings and the other for existing commercial buildings. The framework provides tools to enhance the PEDF configuration for various stakeholders, supporting economic, low-carbon, and safe operations across different scenarios. These case studies illustrate the implementation and effectiveness of the optimization framework. Framework: This research presents a PEDF system configuration optimization and evaluation framework based on a dual-layer model of planning and operating. It includes three components: system application scenario analysis, system planning and configuration, and system configuration optimization and evaluation. The scenario analysis explores how different building types and configuration objectives affect the PEDF performance, identifying the distinct characteristics and requirements. A set of configuration methods for related elements such as system topology, load, photovoltaics, energy storage, and critical interactive devices has been proposed. For optimization and evaluation, a configuration optimization function is developed with relevant constraints. A commercial solver, combined with an intelligent optimization algorithm, is used to optimize the PEDF system configuration, and the effectiveness is assessed. Case: The first case examines optimizing the configuration of the PEDF system for a newly built office building, which now functions as a PV self-consumption system. Under the economic priority scheme, it performs better economically than other schemes. The low-carbon priority scheme excels in low-carbon indicators, while the reliability priority scheme leads comprehensively in reliability indicators. Overall, the low-carbon priority scheme offers the best performance. The second case involves an existing commercial building equipped with 200 kW of photovoltaic power. After optimization, these buildings become photovoltaic grid-output systems, with all three schemes showing improved evaluation indicators compared to their pre-optimized state. The economic priority scheme has a slightly lower self-sufficiency rate than the benchmark, while the low-carbon scheme experiences a minor drop in smoothness, though not significantly. In summary, all three schemes outperform the pre-configuration status, with the reliability priority scheme achieving the best overall results. The proposed framework effectively adapts to various scenarios and provides practical guidance for planning, configuring, and optimizing the PEDF system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & \begin{tabular}{c} **Subjective** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **Objective** \\ **Weight** \\ \end{tabular} & **Weight** & **Before** & \
\begin{tabular}{c} **Goal** \\ **Eco** \\ \end{tabular} &
\begin{tabular}{c} \\ **LC** \\ \end{tabular} & **Rel** \\ \hline Return on investment & 0.1524 & 0.0223 & 0.0874 & - & - & - \\ Levelized cost of energy & 0.0762 & 0.0115 & 0.1188 & - & - & - \\ Carbon emission reduction & 0.2286 & 0.0091 & 0.2044 & - & - & - \\ PV self-consumption rate & 0.1143 & 0.2944 & 0.0624 & - & - & - \\ PV self-sufficiency rate & 0.1143 & 0.0105 & 0.3158 & - & - & - \\ Smoothing coefficient & 0.0571 & 0.5745 & 0.0898 & - & - & - \\ Loss of power supply probability & 0.1714 & 0.0083 & 0.0776 & - & - & - \\ Electrical flexibility & 0.0857 & 0.0694 & 0.0439 & - & - & - \\ Distance to the positive ideal solution & - & - & - & 0.2265 & 0.2572 & 0.312 & 0.1739 \\ Distance to the negative ideal solution & - & - & - & 0.1838 & 0.2828 & 0.3338 & 0.2168 \\ Relative progress of pasting & - & - & - & 0.448 & 0.5238 & 0.5169 & 0.5549 \\ Rank & - & - & - & 4 & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comprehensive evaluation results before and after the configuration in the specific commercial building.