# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 5 Optimal Planning  
The EVs' charging requirement is complicated; therefore, it is not easy to accurately estimate or precisely obtain it. As presented in Table 1, the literature consists of research papers related to the charging scheduling issue. Some of these studies describe the integration of renewable sources with V2G technology during the charging station's planning [65; 66; 67; 68; 69]. The other set of these research papers are focused on the BES [70; 71].  
Charging station planning is a challenging task. It includes considering the availability of renewable sources, uncertainties in traffic demands, the complex nature of location design, and other factors affecting hourly power management such as renewable source, grid peak hours, and V2G. Thus, in a charging station, there is a need to link long-term planning decisions (e.g., location, size, and operation hours) with short-term operation decisions (e.g., grid power usage, the number of batteries charged/discharged, energy storage, V2G, and renewables) to form a planning framework. Besides, the availability of the data allows designers of fast-charging stations to have access to the EVs' data over transportation networks, including historical data and real-time charging demand. The collected data encourage an innovative data-driven pattern. Table 2 describes some studies that applied a data-driven approach.  
Moreover, in the built environment applications, the energy system planning models should have data standardization, interpretability, scalability, flexibility/adaptability, and reconfigurability [72]. These features can serve as the foundation for future modeling research to develop and deploy models in IoT-based systems as _digital twins_ of real-time processes. Digital twins will be developed in a hierarchical and interconnected fashion, rather than being designed for individual and separate applications.  
Concerning the design of RCI, it was noticed that its reliability and cost are the standards considered the most in the charging stations. However, environmental objectives and social factors should be given more attention, mainly when the charging station includes a conventional source. The social factors influence optimal planning methods as they are affected by energy savings and the total cost of integrating renewables sources. Thus, consideration of such factors in optimal planning methods is recommended.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Modeling Technique** & **Source** & **Station Type** \\ \hline
[73] & Stochastic programming & Grid, Solar & Charging Station \\
[67] & Mixed-integer linear & Grid, Wind, vehicle to & Charging Station \\  & programming (MILP) & grid (V2G) & \\
[70] & Two-stage stochastic MILP & Grid, Solar & Battery Exchange Station \\
[65] & Two-stage stochastic MILP & Grid, wind, V2G & Charging Station \\
[68] & Stochastic Optimization & Grid, Wind & Charging Station \\
[69] & Probabilistic Model & Grid, Wind, Solar & Charging Station \\
[71] & Two-stage stochastic MILP & Grid, Solar & Battery Exchange Station \\
[66] & MILP & Grid, Wind & Charging Station \\ \hline \hline \end{tabular}
\end{table}
Table 1: Charging station planning.