## 5 Conclusions  
In this paper, a procedure for optimal operation planning of a DC microgrid for supplying EVs has been proposed. Forecast of PV production and EV mobility needs are used to pursue minimum daily operation cost, including the economic perspective of the microgrid operator and EV owners. For this purpose, a mixed-integer linear programming approach has been exploited, and proper non-linear models of energy storage devices and of DC microgrid peculiarities have been accounted for through an iterative procedure. The proposed approach has been applied to a test DC microgrid, and compared to non-linear formulation. has been solved by means of a genetic algorithm. Different days of operation have been considered, with varying levels of PV production and EV usage, and performances are evaluated in terms of economic results, computational efforts, and EV utilization indices. Simulations have proved that the proposed approach yields comparable solutions with respect to non-linear formulation, with remarkably low computational time. Therefore, the proposed MILP approach has been found to be affordable and fast enough to cope with the time requirements of system operation programming. The approach has also proved to account for DC microgrid peculiarities in an efficient way, demonstrating its applicability in a realistic environment, pointing out limited exploitation of energy storage due to current wear cost. Moreover, the individualization of proper indices for EV exploitation can foster their integration into energy management processes, as well as the application of bidirectional energy exchange through V2G. Future evolutions of the work could include the formulation of several objectives, for instance including load demand peak shaving or the provision of network ancillary services, as well as further security constraints in the procedure in order to efficiently prepare for possible islanding. Moreover, this procedure is aims to be the starting point to develop real-time operation strategies in the presence of variations from day-ahead forecasts. **Author Contributions:** [PERSON], [PERSON], and [PERSON] provided the research framework and literature analysis. [PERSON], [PERSON], and [PERSON] developed the model. [PERSON], [PERSON], and [PERSON] cared for data collection. [PERSON], [PERSON], and [PERSON] set up the solution procedure. [PERSON] and [PERSON] performed the simulations. [PERSON] and [PERSON] formalized the nomenclature. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] edited the paper. [PERSON] and [PERSON] cared for the funding of the project. **Funding:** This document has been created in the context of the CONNECT project. The CONNECT project has received funding from the ECSEL Joint Undertaking under Grant Agreement no. 737434-2 and from the national programs and funding authorities of Germany, Italy, Slovakia, Spain, and The Netherlands. The ECSEL JU has no liability in respect to this document, which merely represents the authors' views. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Nomenclature**  
_Indices and auxiliaries_  
\(N_{b}\)Number of storage systems  
\(N_{ev}\)Number of electric vehicles  
\(N_{t}\)Number of time steps  
\(i\)Storage index  
\(j\)Electric vehicle index  
\(t\)Time step index  
\(f(\mathbf{x})\)Objective function [\(\mathbf{\xi}\)] \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Proposed Iterative MILP**} & \multicolumn{2}{c}{**Reference Non-Linear with GA**} \\ \hline \multirow{3}{*}{**Simulation time [s]**} & Sunny & 26.06 & 7216.76 \\  & Cloudy & 28.94 & 7222.93 \\  & Rainy & 44.11 & 7217.23 \\ \hline \multirow{3}{*}{**Iteration number**} & Sunny & 4 & 12 \\  & Cloudy & 5 & 13 \\ \cline{1-1}  & Rainy & 10 & 16 \\ \hline \hline \end{tabular}
\end{table}