## 3 Calculation of Charging Station Demand
### Integration of Electric Vehicles and Power Infrastructure  
Unlike traditional petrol vehicles, electric vehicles depend significantly on urban infrastructure, which incorporates external telemetry, automated monitoring, and traffic control systems. Since charging stations form an integral part of this infrastructure, the patterns of electric vehicle usage must be considered when designing a decision-making system for charging station allocation. To delve deeper into this topic, it is essential to examine the utilization strategies and foundational algorithms that govern both the operation of electric vehicles and the supporting infrastructure. The diagram in Figure 3 shows the general-use strategy for EV transportation in both cities and remote areas, with a focus on using renewable energy sources. This explains the infrastructure approach for the EV demand, which will be reviewed in the upcoming section. Figure 3 addresses the following aspects:  
* Charging stations: The dispersion of charging stations for electric vehicles over the city. These stations can be situated in a range of locations, such as public spaces, shopping centers, and residential neighborhoods. * Mobile app integration: The integration of charging stations with mobile applications to enable users to monitor station availability, charge duration, and service expenses. * City grid integration: The interaction of electric vehicles with the city grid using smart grid technologies and intelligent power grids. This involves the capacity to optimize energy use and grid interaction. * Incorporation into transportation network: The integration of electric vehicles into the urban transportation network to manage traffic flow, alleviate congestion, and enhance route efficiency. At the same time, the approach to EV integration with the general grid depends on the overall use-case scenario and algorithms. Based on the EV general use-cycle, the main infrastructure needs for EV application differ significantly, and this has to be taken into account during model preparations. A previous review [23] shows that, although the system may be built differently, the basic interactions between modules remain the same. The basic scheme is shown in Figure 4 and can be considered a universal scheme. It represents the usage diagram for EVs in both urban and remote areas, focusing on  
Figure 2: Predicted charging station demand in Kazakhstan for 2020–2035 period. renewable energy sources, incorporating insights from [24] on the impacts and challenges of EV deployment. Figure 4 illustrates the integration of electric vehicles and the power infrastructure to promote sustainable urban mobility. This integration leverages findings from [25] on the smart charging of EVs and the use of AI in managing EVs, as highlighted by [24]. Based on this figure, it can be observed that the decision-making system has to include the following components:  
* A dualink to communicate with vehicles and charging stations and provide real-time data regarding the vehicle condition, battery charge, and route;
* External data sources that provide additional data, specifically information about weather, traffic, road construction works, and additional external factors;
* A server cluster for the algorithmic analysis of the provided data and decision-making. These components are usually organized into a decision-making system in the following way: the control center is connected to a computational cluster, which has access to a database and algorithms. The dualink is connected to the control center to provide vehicle data, as well as connected to external data sources. The system's typical design can be observed in Figure 5.  
Figure 3: General scheme of electric vehicle integration into electric power grid. This structure is considered to be the basis for similar systems, as described in [26]. The system is used for data collection, data analysis, and parameter evaluation to make the most appropriate decisions for the EV.