Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

Abstract

Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid

“…The gradual, orderly, and planned deployment of electric vehicle charging centers is part of the smart grid concept and facilitates the system's efficiency, reliability, and resilience [1,5]. In addition, contemplate other stages such as the smart metering of electric energy, the communications system to send information from electric meters [6,7], the allocation of communication channels, and the behavior of the electricity consumption pattern until reaching the demand response that leads to the objective of reducing energy losses.…”

“…The best topology for electric vehicle routing decisions is achieved by looking at road capacity, vehicle flow, and spacing variables. The heuristics have proven to be scalable and have been validated through iterative processes to address contingency concepts for predicting peak vehicular traffic density in roadway networks [1]. Therefore, the objective is to solve multiple resource allocation problems in a geo-referenced area of NP-hard computational complexity, supported by optimization tools, graph theory, and routing algorithms.…”

“…Consequently, this work will contribute to a methodology for creating a charging station considering international standards. Rajendran, 2021 [24] Campaña, 2021 [1] Luo, 2020 [12] Xiong, 2020 [22] Das, 2020 [25] Quddus, 2019 [14] Jia, 2019 [16] Zhu, 2018 [18] Qiao, 2018 [26] Tian, 2018 [21] Csonka, 2017 [27] Hardinghaus, 2016 [20] Proposal Authors…”

“…Electric mobility contributes directly to reducing greenhouse gases, noise, and visual pollution compared to traditional mobility. Human mobility plays a vital role in the development and growth of society; on the contrary, with classic mobility, the environmental cost becomes severe and unfavorable for future generations due to the current climate change [1].…”

Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region

“…In addition, a wide adoption of EVs will need to be supported by a proper charging infrastructure [13], which may prove to be critical especially in dense urban areas. Different papers are proposing a range of tools to support the planning and deployment of public charging infrastructure, including multi-criteria decision processes [14], artificial neural networks [15] and multi-commodity flow problem algorithms [16].…”