Is Deployment of Charging Station the Barrier to Electric Vehicle Fleet Development in EU Urban Areas? An Analytical Assessment Model for Large-Scale Municipality-Level EV Charging Infrastructures  
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Several alternative energy sources and renewable fuels are available--with different level of technology readiness and market penetration--in order to reduce dependence on fossil fuels: among them we find biofuels such as biodiesel and bioethanol, renewable hydrocarbons, ligno-cellulosic ethanol, biomethane, renewable fuels of non-biological origin (RFNBO) recycled carbon fuels, renewable e-fuels, renewable hydrogen, electricity, etc. None of these alternative fuels and sources alone will ever completely substitute fossil fuels, at least in a short term, and most of the forecasts see them coexisting in future fuel mixes [3]; in 2016, in EU-28, biofuels accounted for the bigger share, covering 4.6% of total final fuel consumption [1]. Anyway, rapid cost reduction of solar and wind power technologies created strong prospects for further electrification of end-use sectors. Within this framework, electric vehicles (EVs)--especially passenger cars--are one of the most pursued solutions to achieve large-scale transport sector decarbonization; anyway, they are not a \"drop-in\" solution, since they need a new and alternative charging infrastructure. Official documents such as the Alternative Fuels Infrastructure (AFI) Directive [4] and the National Plans for alternative fuels and EV charging infrastructure such as [5; 6] are of interest because they express governments forecasts about future EV fleet size and formal recommendations on topics such as the minimum number of charging stations (CS) to be deployed and the minimum EV/CS expected ratio. It is expected that urban areas will be the place of a large-scale infrastructure deployment in short to mid-term, since they are the most suitable for actual EV use and would receive maximum benefit from noise and local polluting emission reductions that are related with the shift from internal combustion engine vehicles (ICEV) to EV. To this regards, stakeholders and decision makers could benefit from reliable predictions on the dimension and cost of a charging infrastructure suitable for, i.e., a municipality area. Literature already presents several studies on EV charging infrastructures, which analyze the topic from a multitude of points of view. Many of them evaluate the optimal positioning of a given set of CS through geographic information system (GIS) procedures, such as [7; 8] or through traffic flows analysis such as [9]; they develop a model taking into account residential statistics, parking area information, electric power distribution network position and other data to define the optimal position for charging stations, but usually the input data about the number of CS to be deployed have little or no connection with data on EV fleets circulating in the area or on forecasts about that. Other studies focus on business and profitability analysis of a specific CS installation [10], while other analyze the possible interactions between charging EV and RES electrical generation that could take place in urban areas, such as the one from PV installations [11; 12]. Finally, several papers analyze the topic of optimal sizing of EV charging infrastructures. Unfortunately, reports that specifically evaluate cost and size of large-scale deployments, using real data and forecasts (such as [13; 14]) usually refer to national or international levels and give as output highly aggregated information; this makes it difficult, afterwards, to scale them down to a more local level. Several studies can be found, that address the problem at a regional and municipality level: [15] analyzes the German region of Stuttgart, but it focuses its model on CS availability rate and is not applied to a real scenario.