## 4 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial (f/kWh)** & **O\&M per Year** & **Infations Rate** & **Investment** \\  & **(f/kWh)** & **(\%/year)** & **Period (Years)** \\ \hline
839 [29] & 9.5 [29] & 6 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Economic data of the study. Figure 16 shows the daily electricity price, PV generation, PEV charging load and total load of the OAKA microgrid when the PEV number is 500 and for different PV generation and charging strategies. The number of PEVs that is selected to be demonstrated in Figure 16 is 500 because their charging load is the highest and it can be observed better the influence of the PEV charging load on the microgrid total load curve. It can be observed that when the uncoordinated charging strategy is applied, the electricity price and the PV  
Figure 14: Accumulated present value savings throughout the investment years (PV = 3000 kWp). Figure 15: Accumulated present value savings throughout the investment years (PV = 4000 kWp). generation do not affect the total load of the microgrid as the PEV charges after they plug-in without taking into consideration any other factors (Figure 16a,b). On the other hand, when s1 is applied, the PEV charging load influences the total demand of the microgrid significantly, as is shown in Figure 16c,e. The peak demand of the microgrid is shifted during the time slots where the electricity price is low and new peak demand is created during the time slots where the electricity price is high. In addition, when the PV generation is introduced, s2, to the proposed charging strategy (Figure 16d,f), a part of the charging load is shifted to the time slot where the PV generation is high, and the electricity price is low. Figure 16: Electricity price, PV generation, PEV charging load and total load of the microgrid when PEV number = 500 and **(a)** PV = 1000 kWp and charging strategy: uncoordinated, **(b)** PV = 4000 kWp and charging strategy: uncoordinated, **(c)** PV = 1000 kWp and charging strategy: proposed s1, **(d)** PV = 4000 kWp and charging strategy: proposed s1, **(e)** PV = 1000 KWp and charging strategy: proposed s2 and, **(f)** PV = 4000 kWp and charging strategy: proposed s2. The assessment of the profitability of the PV investment in a microgrid equipped with PEV charger was examined in this study. The key finding from the results is that the application of a smart charging strategy and V2G can increase the NPV of the PV investment in a microgrid. In addition, the proposed charging algorithm can change the load curve when the PEV charging load is substantial, by scheduling the charging of the PEVs when the electricity price is low and the discharging when the electricity price is high, as shown in Figure 16. Some limitations of the approach of this study are that the charging policies, the electricity price, the microgrid load, and the solar irradiation prediction remain constant during the investment lifetime. Moreover, the charging policies play a significant role in the profits of the microgrid. In this study it is assumed that the profits of V2G will be collected from the microgrid. However, part of the profits could go to the PEV drivers. In addition, charging policies should take into consideration the cost of battery degradation caused by the participation of PEVs in V2G. Furthermore, not all drivers would accept to participate in V2G. Hence, the profit of the microgrid would be reduced. While the model considers the uncertainties of the electricity price, the microgrid load and the solar irradiation, no specific prediction model for the drivers' incentives was used. These factors can potentially change the anticipated results. Finally, the arriving and departure of PEVs is assumed to be similar to the first year. The PEV consumption profile has a major effect on the optimization problem. The accuracy of this profile is able to render the study reliable enough. However, the stochastic behavior of PEV drivers cannot guarantee a perfectly accurate profile.