*6.1. Comparison*

From the literature review presented above, it can be concluded that most of the existing studies only discuss the optimal scheduling of EV/BES charging. However, within a 15-min forecasting resolution, large forecasting errors can occur due to the fast changing intermittent character of PV power. Additionally, other estimations such as *tarrival* can lead to more errors. Therefore, a moving horizon window including real-time control scheme is an important part for a smart charging algorithm, as errors are compensated and the optimization is iterated. In order to assess the effectiveness of all the different components, several case studies are performed over a half-year period:


Here, it is assumed that a half-year simulation period is enough to capture all seasonal variations and that the forecasting error *f c* = 0 in order to assess the maximal potential of acting as a FCR. Furthermore, the moving horizon control will operate with a 24-h window in a 15-min resolution. The first uncontrolled case consists only of a PV installation, EV charger, and load. Here, it is assumed that the EV starts charging upon arrival with a 3 kW charger for 3 h to meet its daily demand. The second case is the complete proposed control scheme. The third case is similar to having only an optimal scheduling algorithm; here, all deviations from the obtained optimal solution are compensated using grid energy. The fourth case does not utilize V2G, and the fifth case does not take into account a primary frequency regulation reserve revenue. Figure 12 shows the resulting grid power for use case 2 and 3. Here, case 3 is comparable with having only an optimal control scheme (no real-time control). It can be seen that the errors are dealt with differently, in the end, increasing costs. In Figure 13, the total cumulative costs for every use case over a half year period is shown. Using the proposed control scheme (case 2), the total costs can be reduced by 98.6% compared to the uncontrolled case.

**Figure 12.** Comparison of grid powers for case 2 and case 3 (with and without real-time control).

A breakdown of all cost components is shown in Figure 14 and Table 3. Here, it can be seen that the optimized EV and BES degradation costs for case 2 are still equal to 91.87 euro and 64.6 euro, respectively. Similarly, the total cost of PV energy equals 168.15 euro. From this, it can be concluded that all these costs are a nonnegligible part of an objective function when minimizing the total cost of energy in an EV-PV-BES-HP system. However, although these costs are relatively high, from Figure 13 and Table 3, it can be concluded that V2G still is a cost-effective method for storing renewable and demand-side management, as the revenue obtained from trading energy exceeds the costs of degradation. Furthermore, it can be seen that the EV charging costs of case 1 are actually the lowest. This is because the average charging power for case 1 is lower compared to the other cases, resulting in lower degradation, however, resulting in more grid electricity costs.

**Figure 13.** Total costs for all 5 cases over a half-year simulation period.

**Figure 14.** Cost breakdown for all 5 cases: Here, the black line indicates the total costs.

**Table 3.** Cost comparison of the presented 5 use cases.


#### 6.1.1. Demand-Side Management: Power Curtailment

Besides operating as a primary frequency regulation reserve, the smart charging algorithm is also capable of power curtailment. For example, when the smart grid operator foresees an over-voltage occurring in the near future, it can choose to limit the grid feed-in power of the system. An example of this is shown in Figure 15. Here, the SGO reduces the maximum allowable feed-in power to 5 kW between 09:30 and 18:00. A comparison with the same day without power curtailment is shown in Figure 16.

**Figure 15.** Curtailment of PV power due to reduced maximum allowable grid feed-in between 08:00 and 18:00.

**Figure 16.** Comparison of the same day without power curtailment.
