## 5 Numerical Simulation Results
### Application of Dynamic Time-Of-Use Pricing Strategy for EVs
#### 5.2.1 Simulation of Dynamic Time-Of-Use Charging Price and Participation Degree of Different EVs
In a certain period, a lower charging price means that more EVs will participate in the dispatching, embodying the influence of economic satisfaction degree on the selection of EV users' charging strategies. It can be concluded from Figure 5(a),b that: (1) in the time periods \(T_{21}-T_{25}\), although the micro-grid needs to dispatch a number of EVs, the obtained dynamic time-of-use price is equal to the peak-valley price. The reason is that the grid does not need to reduce the charging price to promote the participation of EV users, due to the fact that the price of 0.39 yuan/kWh can dispatch enough capacity of EVs (in Figure 5(b)) to meet the requirements of the micro-grid. (2) In other time periods, the actual capacity of EVs which can be dispatched is below the demand (in Figure 5(b)). Then, the grid adjusts the dynamic time-of-use price, according to its balance of supply and demand within a certain range, and so as to make the number of responding EVs to reach the dispatching target as much as possible. In the time periods \(T_{19}-T_{35}\) in Figure 5(c), as the dispatching period increases, the participation degree of the urgent EV users first raises and then decreases. The participation degree of normal EV users is low, while the economical type of users is not involved in dispatching. The reasons can be attributed to the following two points: (1) when the dispatching demand of the micro-grid is low, there is no need to reduce the charging price substantially to attract economical EV users who are not sensitive to high electricity price, since the controllable capacity of the urgent and normal can fully meet the dispatching requirements. Meanwhile, due to the pressing need for charging of urgent EV users, the participation degree of them will be higher than that of the normal type. (2) The urgent EV users have higher participation degree in the early periods, but the insufficient controllable capacity leads to a low participation proportion in the later intervals. In the time periods \(T_{36}-T_{38}\) and \(T_{95}-T_{96}\), the controllable capacity of the urgent is zero, and only the normal and economical EV users participate in the dispatching. As the dynamic pricing strategy for EVs charging in this paper is implemented in the day-ahead phase, if one or more parameters of EV change in real-time, it may cause the actual charging capacity of EVs to be different from the forecasting in the day-ahead. Therefore, we simulate the charging capacity of EVs under three different parameter settings, so as to study the impact of the five parameters on the charging capacity of EVs, i.e., the time leaving home, the time arriving at the workplace, the time leaving the workplace, the time arriving home, and the daily mileage of EVs in (1)-(5). The results are shown in Table 15 in the Appendix A. It can be seen from the table that the overall charging capacity of EVs does vary with the change of parameters, but the change is relatively small, and approximately within the range of 3%. Therefore, the change of parameters will not have a significant impact on the overall availability of EV cluster in real-time dispatching.