Third Tier: Impact of EVs on the Energy Distribution Network in the Target Scenario
The third and final tier allows the general assessment of the qualitative and quantitative impact of EV charging on electricity distribution. It describes the charging infrastructure geo-referenced coherently with urban maps and classified in two types: fast-charging stations and residential standard-charging stations. While the first are typically located at the roadside and used by vehicles that need to charge their batteries to continue their trip, the second are located near homes and parking lots and are typically used at the destination of a trip. Both stations can be configured in terms of number of columns and charging process features.
The EV recharge model is interfaced with the mobility simulator (the second tier), in order to exchange data in both directions. First, the simulation of battery recharge takes data of EV arrival at recharge stations and of their residual SoC. Then, it returns the final SoC when the EV leaves the charging station, depending on the elapsed time and the type of charging.
However, other data are necessary to quantify the distribution of energy demand, according to the habits, activities, and decisions taken by vehicle owners such as the driver's logic used to decide when, where, and how much to charge the EV battery. These behavioral models are the focus of our future work.
The approach we propose to build up a DSS for electric-based urban mobility represents an emblematic example of how the cross-sectorial scientific expertise of the research world, supported by the use of ICTs, could allow decision makers (e.g., local administrations, energy distributors, and mobility providers) to adopt an integrated approach to smart city planning. In fact, the DSS could be used for several purposes, all aimed at encouraging the diffusion of EVs in urban areas.
Using information on the discharging/charging process of EVs and data related to driving cycles in a realistic traffic scenario, distribution network operators could estimate the increase in electric power demand due to EV activities and examine the influence on standard load shapes. These analyses will be useful when taking important decisions about load management techniques and in planning the locations of the charging infrastructure and corresponding capacities. The DSS could also help local administrators in defining and optimizing urban mobility policies and traffic regulations based on the analysis of EV impacts on air quality and noise levels.