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

Author

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  • Miguel Campaña

    (Master of Electricity Program, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    Current address: Postgraduate Department, Girón Campus, Av. 12 de Octubre N 23-52, Quito 170525, Ecuador.)

  • Esteban Inga

    (Master of Electricity Program, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    Smart Grid Research Group, Universidad Politécnica Salesiana, Quito 170525, Ecuador)

  • Jorge Cárdenas

    (Inclusive Education Research Group, Universidad Politécnica Salesiana, Quito 170525, Ecuador)

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.

Suggested Citation

  • Miguel Campaña & Esteban Inga & Jorge Cárdenas, 2021. "Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities," Energies, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4933-:d:612934
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    References listed on IDEAS

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    2. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    3. Ana Pavlićević & Saša Mujović, 2022. "Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses," Energies, MDPI, vol. 15(19), pages 1-24, September.
    4. Oluwasola O. Ademulegun & Paul MacArtain & Bukola Oni & Neil J. Hewitt, 2022. "Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions," Energies, MDPI, vol. 15(24), pages 1-28, December.
    5. Pokpong Prakobkaew & Somporn Sirisumrannukul, 2022. "Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data," Energies, MDPI, vol. 15(11), pages 1-19, May.
    6. Jefferson Morán & Esteban Inga, 2022. "Characterization of Load Centers for Electric Vehicles Based on Simulation of Urban Vehicular Traffic Using Geo-Referenced Environments," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    7. Shahid Nawaz Khan & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Mohammed A. Alghassab, 2022. "Smart Distribution Mechanisms—Part I: From the Perspectives of Planning," Sustainability, MDPI, vol. 14(23), pages 1-109, December.
    8. Seyedamin Valedsaravi & Abdelali El Aroudi & Luis Martínez-Salamero, 2022. "Review of Solid-State Transformer Applications on Electric Vehicle DC Ultra-Fast Charging Station," Energies, MDPI, vol. 15(15), pages 1-35, August.
    9. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    10. Ajit Kumar Mohanty & Perli Suresh Babu & Surender Reddy Salkuti, 2022. "Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    11. Pablo Tamay & Esteban Inga, 2022. "Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    12. Witt Andreas, 2023. "Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 1-11, January.

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