## 3 Electric Vehicle Impact Simulations
### Case Study Definition  
The focus of this analysis is to understand how EVs affect distribution networks and to determine the best intervention that would minimize the detrimental consequences on the network itself by means of smart strategies. The simulations carried out have made it possible to ascertain that the potential problems of the charging stations are not so much in the voltage drop as in the possible power overload of the lines and distribution transformers. The biggest contributor to network overload is determined by the number of EVs present and the energy that these require daily from the network, which depends on the trips made by end users. Finally, the distribution of charges in terms of both time and space also has a strong influence. For this reason, a script was created with MATLAB(r) (The MathWorks, Inc., Natick, MA, USA) to randomly generate data on user behaviour about travel but on a statistical basis that is typical of the Italian scenario. Data of interest are the four parameters: daily start time of trips; kilometres travelled per day; number of trips per day, and trip duration, presented in Figure 1.  
Figure 1: Travel behaviour: (**a**) daily starting trip time; (**b**) kilometres travelled per day; (**c**) number of trips per day; (**d**) trip duration.  
The analysis was carried out on three portions of real networks that were reproduced on the DIgSILENT software. The three networks for each one has its specific characteristics are:  
* urban network,
* rural network,
* commercial network.  
Both slow charging and fast-charging methods were assessed. The slow charge was expected mainly during the night in the urban and rural area, while fast charging would occur during working hours at points of interest such as shopping centres and parking lots.  
The analysis involves examining the impact of both small and large diffusion of EVs by simulating four different scenarios for urban and rural networks as follows:  
* 20% penetration of electric vehicles,
* 40% penetration of electric vehicles,
* 60% penetration of electric vehicles,
* 80% penetration of electric vehicles.  
On the other hand, regarding the commercial network, the possible impact of installing super-fast charging stations up to 150 kW at points of interest was analysed.  
From the assumptions made in this analysis, only one daily recharge is carried out at constant power in order to guarantee the complete recharge of the vehicle battery.  
The simulations were performed using DIgSILENT(r) PowerFactory (Gomaringen, Germany), which is a calculation program for the analysis of extensive electrical systems, as well as industrial and commercial electrical systems. It has been designed as an integrated software dedicated to the analysis and control of electrical power systems in order to achieve the main objectives of optimising planning and operation [55]. The choice of DIgSILENT software derives from the fact that it enables a faithful reproduction of any type of network with a high degree of detail and also ensures great flexibility. The database used by DIgSILENT contains all the data necessary for all the devices within the electrical system (for example lines, transformers, generators, protection data), which eliminates the need to create specific models for any type of analysis (load flow, dynamic studies).  
The three MV network models include the topology of the network, the data related to MV/LV transformers, and the parameters of the distribution lines. Regarding the LV sub-networks, the data provided relates to the number of final customers, the power and energy consumed and the possible presence of photovoltaic producers.  
The simplified block diagram of the simulation environment is presented in Figure 2.  
Figure 2: Simplified block diagram of the simulation procedure.