## 4 Analysis of Results for Investment Decisions  
This section presents an analysis of the results obtained from the various case studies involving different investment strategies, as discussed in Section 3. With the infrastructure hardening approaches, the results for system metrics show that both the selective hardening of the main feeder in Case 2 and the uniform hardening of the overall system in Case 3 demonstrate greater improvements in system resilience performance compared to those for the strategy focused solely on critical loads in Case 1. However, when specifically evaluating the performance of critical loads with investments targeted at these loads in Case 1, a notable enhancement in their resilience is realized. Interestingly, the performance of critical loads in Case 2 remains relatively close to that in Case 1, as shown in Figure 5. This is primarily due to the network configuration, where  
Figure 10: Percentage change in EOD from the base case for the set of critical and non-critical loads with repair management. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **EOD (hr/incident-lp)** & **NDLH (hr/event-lp)** \\ \hline
**Base case** & 89.27 & 86.19 \\ \hline
**Case 8** & 87.39 & 83.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of EOD and NDLH for the base case and Case 8.  
the critical load section is directly connected to the main feeder that was undergrounded in Case 2. As a result, even though the investment was not directly aimed at critical loads, their performance still benefited significantly. This highlights the importance of the physical location of critical loads within the network. Therefore, utilities can position critical loads closer to the substation during network planning or connect them through a dedicated feeder to enhance their resilience. Moreover, even considering a small improvement in the critical load performance, it is also essential to recognize that the cost of outages for critical loads is considerably higher than for non-critical loads. Since outage costs tend to escalate rapidly with duration, and extreme events may lead to prolonged outages lasting several hours to days, these costs become a crucial factor in planning. The EENS metric is commonly used to estimate outage costs by multiplying it by the value of the lost load (VoLL). VoLL accounts for both direct and indirect impacts of an outage and varies depending on customer type and load criticality. Consequently, not all critical loads display the same outage cost, and a more nuanced approach to cost estimation--one that incorporates critical load prioritization--may be necessary to guide infrastructure investment decisions more effectively. Figure 8 highlights the performance gap between critical and non-critical load metrics. Further investment focused solely on hardening critical loads would likely widen this gap. While such investments are primarily intended for improving resilience during extreme events, they also influence performance under normal weather conditions. Therefore, a significant disparity between performance metrics is not ideal, as it consistently places non-critical loads at a disadvantage. Given that residential consumers typically fall under the non-critical category, a large investment imbalance may also raise socio-political concerns. In this context, Case 2 offers an investment option to narrow the performance gap following an initial investment in strengthening critical load points. If uniform reliability is preferred across all conditions, then an overall network hardening strategy, as seen in Case 3, serves as an alternative. However, it is important to note that Case 1 and Case 2 are directly comparable due to similar investment levels. Case 3 involves system-wide asset replacement and would require further evaluation for a more accurate comparison. The results for Cases 4 and 5 highlight important insights into the relationship between DER investments and system resilience.