## 5 Conclusions and Future Works  
The present study provides a comprehensive assessment of a two-stage stochastic model for optimizing the location of the FEVCS within a distribution network. This innovative approach aims to improve network resilience and reliability, particularly during operational faults or disaster management scenarios. The model was thoroughly tested on a modified IEEE-33 node distribution network and a four-node transportation network, which included various types of loads and DGs. The optimal location of the FEVCS, as identified by the model, was at node 25. This node was selected because of its potential to act as energy storage for powering the node and its influence on zones Z3 and Z4 with higher VOLL. Furthermore, the second-best location was determined to be node 22, which added an active node to the network and reduced VOLL compared with the first case. This scenario also showed a strategic use of the mobility and storage capabilities of the fleet vehicles, providing an energy supply-demand balance. In addition, renewable generators supported by a BESS were also considered. The model demonstrated an increased number of active nodes in the restored distribution network, with the optimal FEVCS location remaining at node 25. The presence of the BESS allowed for greater flexibility in the network, allowing the vehicles to charge during periods of surplus energy and support the grid during other hours. When two FEVCSs were present alongside BESS-backed renewables, the model prioritized loads with higher VOLL, resulting in powering a fewer number of nodes, but catering to more critical loads. The proposed stochastic model presents a potential path for strategic planning and optimization of the FEVCS in a distribution network, enhancing the resilience and reliability of the network. The results highlight the valuable role of an FEVCS, particularly when combined with a BESS, in responding to network faults and managing supply-demand balances. Future work further explores other factors that influence the model and test the model on larger or more complex network configurations and to use a stochastic distribution control approach to assess the robustness of the optimization by controlling the probability density function (PDF) shape of the concerned objective functions. In this context, the narrower and sharper PDF of the optimized objective functions would indicate the strong robustness of the optimization. Methodology, A.R., L.B. and H.W. ; Software, A.R. ; Investigation, A.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data are contained within the article. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **MIP GAP** & **Solution Time (hours)** \\ \hline
0 & 0 & 0.004 \\ \hline
1 & 0 & 0.261 \\ \hline
2 & 0.001 & 17.534 \\ \hline
3 & 0 & 0.597 \\ \hline
4 & 0.078 & 55.437 \\ \hline
5 & 0.13 & 70.579 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MIPGap (%) and solution time. **Acknowledgments:** This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00 OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ([[http://energy.gov/downloads/doepublic-access-plan](http://energy.gov/downloads/doepublic-access-plan)]([http://energy.gov/downloads/doepublic-access-plan](http://energy.gov/downloads/doepublic-access-plan))). **Conflicts of Interest:** The authors declare no conflict of interest.