# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning
### Distributed Energy Resources  
In the reviewed literature, there is a growing effort to include distributed energy resource (DER) and storage systems as positively co-existing with EV charging stations. However, most of studies only include intermittent generation and batteries from an operational perspective: authors of [138] produce a charging strategy based on pricing for buses and taxis to maximize renewable generation use, whereas [139] concludes that V2G can support renewable energy production with a reduction in emissions and grid costs. In [140], the authors analyze how storage can improve V2V schemes for EV parking lots served by both slow and fast chargers, avoiding construction of new charging facilities. In [141], renewable generation is included in the operations optimization of an EV aggregator in parking lots. The authors of [84] conduct a noteworthy piece of research, wherein a cost-benefit analysis is conducted to assess whether ESS allocation can be beneficial to avoid grid expansion investments (substations and feeders) at charging substations. Chargers are located unmethodically at electrical bus nodes, which does not allow for any AS choice. Still, in the optimization, authors include comprehensive system upgrade costs, total costs of the ESS, day-ahead market prices, and operational benefits to apply arbitrage. Only a few articles contemplate how DERs can affect AS of CI. Study [119] implements wind and solar generators together with the use of lithium-ion batteries. In their cost-benefit analysis, the authors design a charging station coupled with DERs components to prioritize cost-effective solutions that consider electricity market patterns and grid limits. The authors find that investing in distributed energy resources (DERs) is profitable, although upfront costs increase up to four times compared to the baseline scenario (energy is bought 100% \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Grid** & **Energy** & **Electrical** \\
**Ref** & **Year** & **Expansion** & **Loss** & **Network** \\ \hline  
\\ \hline \hline \end{tabular}
\end{table} 
Table 6: Planning: DSO-oriented. from the grid). A particular feature of this research is that authors consider a very wide range of vehicles that can be charged at the station: bikes, small and large private cars, and vans. The authors of [61] consider both gas-based turbines and PV installations together with CSs, with the objective to minimize annualized social costs. For this purpose, the study comprehensively integrates emission costs, network losses, and EV battery degradation as well as optimal scheduling. The approach taken by [67] includes allocating and scheduling DERs, lead-acid batteries, and CSs. Results show that PV installations are allocated over battery devices due to large feed-in tariff, whereas optimal solutions can decrease grid expenses by almost 75% with consistent avoided energy losses. Few articles analyze the coupling of controllable loads with charging stations in order to increase benefits for self-consumption and minimize grid impacts. Algorithms were developed in order to optimize operations and implement control strategies. This is the case in [142], where authors consider whether coupling thermal boilers' power demand with EVs can largely decrease the peak-to-valley difference if more EVs participate in the peak-shaving strategy. To the best knowledge of the authors, no AS study exists that takes controllable loads into account. This is probably due to the fact that controllable loads are regulated at the household level, and home-charging does not involve a planning process. Instead, the planning process is of more interest to public-charging stations at a municipal, regional, or national level. Table 7 shows the resolution methods applied to solve AS problems. The most consistent procedure involved metaheuristic methods, with a strong presence of genetic algorithms such as non-dominated-sorting GA (NSGA-II).