# A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations
## 4 Case Study: Valencia  
This section describes a case study of the proposed system using data from the city of Valencia. At the European level, the European Commission has produced the _White Paper on Transport_[29], which sets targets for 2050 for the elimination of conventional fuel cars in cities. Although there are many initiatives and programs to implement hybrid and electric vehicles by the International Energy Agency, European Battery Manufacturers Association, and forums for global cooperation for the development and deployment of EV, there is a need to focus on the problems from the user's point of view to define and implement solutions at the municipal level. The municipalities should incorporate strategy and development plans of infrastructures, regulations, and planning, in order to face an imminent acceleration of the deployment of EVs in cities, taking into account that technology is in continuous evolution. Therefore, one of the main challenges of the electric vehicle is to create a reliable, accessible and convenient charging infrastructure for the citizen to boost the demand of EV. Currently, there are 76 charging points in the province of Valencia, according to [30], and 24 of these are located in the city of Valencia. The Valencia City Council has carried out various initiatives aimed at improving infrastructure to facilitate the introduction of EV. Among these initiatives, studies have been carried out for the installation of EV charging points. Although actions have been taken to facilitate the integration of EV through charging stations, we believe it would be interesting to provide the proposed MAS that allows a global study to be carried out analyzing the different configurations of charging stations according to different criteria to be optimized. Providing good planning and distribution of charging stations could drive demand for EVs among users considering the use and/or acquisition of an EV and who ultimately do not carry it out due to lack of charging points or poorly located EVs [31]. In the first phase, the MAS determines the \(P\) potential PoIs for the location of a charge station \(s_{i}\) taking into account data from the General Urban Development Plan. In particular, the points shown in Table 2 were selected. To determine the area of influence of each of the PoIs, a Voronoi diagram is created around the selected zones (see Figure 3). In the second phase, the MAS collects data about the city of Valencia. Specifically, the MAS collects data about traffic (see Figure 4 left), population (Figure 4 center), average time spent in commercial spaces, and geo-located social activity from social networks (Figure 4 right) for each of the polygons around a PoI. This data is collected and aggregated using the different proposed agents described in Section 3.1 and represents the input of the _Emplacement Optimizer Agent_ that determines the solution. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Charge Mode** & **Charge Schedule** & **Stay Time** & **Connection Property** \\ \hline
**Shopping Mall** & Fast charge, slow charge & Weekdays 19:00-22:00 h and weekends & 1.2 h & Public \\ \hline
**Workplace** & Slow charge & Weekdays (07:00-19:00 h and weekends & 9 h & Public/Private \\ \hline
**Parking** & Slow charge & 24 h & 2 h & Public \\ \hline
**Public thoroughfare** & Fast charge, slow charge & 24 h & 1-12 h & Public \\ \hline
**Neighborhood community** & Slow charge & 08:00-20:00 h & 12 h & Private \\ \hline
**Private garage** & Slow charge & 24 h & 12 h & Private \\ \hline
**Refueling stations** & Fast charge, battery change & 24 h & 10 min & Public \\ \hline
**Vehicle fleet parking** & Fast charge, slow charge, battery change & 24 h & 15 min–12 h & Private \\ \hline \end{tabular}
\end{table} 
Table 2: Areas selected as potential points to locate EV charge stations. Figure 3: Voronoi diagram from the selected zones as potential points to locate charging stations.