# An EV Charging Scheduling Mechanism Based on Price Negotiation
## 5 Simulation Results  
In this section, the charging price negotiation service was simulated, and the performance of CSM-PN evaluated. We chose a charging station that installs renewable energy resources using power generation equipment. There are 10 wall-mounted charging piles in the charging station. We set 15 min as the basic time slot of a charging price negotiation, and each day was divided into 96 time slots. We chose the time slot 12:45-13:00 to simulate the process of price negotiation because the number of EV users is relatively large at this time, according to the statistics. We set \(p_{0}=0.5\), \(\omega=0.5\) and \(\varphi=0.95\). Figure 2 presents the bidding price process of EV users and the charging station operator in the first 3 rounds. In the first round, only one bidding price of an EV user is higher than the bidding price of the charging station operator. After one round of price adjustment, three bidding prices of EV users are higher than that of the charging station operator. In the third round, seven EV users can get their EVs to be charged. The final charging price is neither as high as the initial expectation of the charging station operator nor as low as the initial expectation of the EV users, but a value between the two. Both sides can accept that the price formed by price negotiation, which improves satisfaction, while meeting the travelling and interest demand of the EV users. Moreover, an EV user that bids a  
very low price may risk negotiation failure, and cannot get their EV charged. Thus, EV users should reasonably adjust the price according to their own consideration of convenience and other interests. In the design of a charging price negotiation system, in general, there are some parameters that will significantly affect the performance of the system. We evaluate the system design using three main metrics that represent the operation performance of the system, which are the number of transactions, the charging station operator's income, and the load changes of the power grid. We further evaluated the performance of charging scheduling by comparing two disciplines: a real-time price mechanism and our CSM-PN mechanism. In the real-time price mode, the EV is charged at the real-time price. Figure 3 shows the average statistics of real-time price for 10 days. Figure 4 shows the number of transactions, charging station operator's income, and the load changes of the power grid of the two charging scheduling mechanisms for 10 days. Figure 3: Average statistics of real-time price for 10 days. Figure 2: Bidding price process in the first 3 rounds (**a**) first round; (**b**) second round; (**c**) third round. Figure 4 shows that our CSM-PN mechanism can attract more EV users to actively participate in charging pricing and finally improves the number of transactions. CSM-PN not only improves EV users' satisfaction but also enhances the benefits of the charging station operator. The number of transactions increases to a relatively large degree, and the final price decreases, but not to the same extent. It is commonly known that the price of energy from renewable energy sources is usually taxed less than energy from non-renewable sources. An increase in the number of transactions means that more energy from renewable energy sources has been used and less taxation occurred. Thus, the total benefits of the charging station operator substantially increase. These results may attract more people and economic entities to invest in the construction and operation of EV charging stations, and will greatly promote the development of the EV industry. Figure 4 also shows that the area surrounded by the red curve is larger than that surrounded by the blue curve, which indicates that the total consumption of power has been improved and more EVs have been charged. The efficiency of the charging system has been improved. Moreover, during Time/h (4:00-6:00), more energy will be purchased from the power grid for a relatively lower price and lower power load in one day.