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Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data

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  • Shepero, Mahmoud
  • Munkhammar, Joakim

Abstract

In the recent years, the number of electric vehicles (EVs) on the road have been rapidly increasing. Charging this increasing number of EVs is expected to have an impact on the electricity grid especially if high charging powers and opportunistic charging are used. Several models have been proposed to quantify this impact. Multiple papers have observed that the charging stations are used by multiple users during the day. However, this observation was not assumed in any previous model. Moreover, none of the previous models relied on geospatial maps to extract information about the parking lots—where charging stations are installed—and the charging profiles of the potential users of these charging stations.

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  • Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
  • Handle: RePEc:eee:appene:v:231:y:2018:i:c:p:1089-1099
    DOI: 10.1016/j.apenergy.2018.09.175
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    13. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    14. Hartvigsson, Elias & Taljegard, Maria & Odenberger, Mikael & Chen, Peiyuan, 2022. "A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids," Energy, Elsevier, vol. 261(PA).
    15. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    16. Josh Schipper & Sharee McNab & Yuyin Kueh & Radnya Mukhedkar, 2022. "Multiple Initial Point Approach to Solving Power Flows for Monte Carlo Studies," Energies, MDPI, vol. 15(19), pages 1-27, September.
    17. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    18. Jian Chen & Fangyi Li & Ranran Yang & Dawei Ma, 2020. "Impacts of Increasing Private Charging Piles on Electric Vehicles’ Charging Profiles: A Case Study in Hefei City, China," Energies, MDPI, vol. 13(17), pages 1-17, August.
    19. 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.
    20. Simolin, Toni & Rauma, Kalle & Viri, Riku & Mäkinen, Johanna & Rautiainen, Antti & Järventausta, Pertti, 2021. "Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites," Applied Energy, Elsevier, vol. 303(C).
    21. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
    22. Horak, Daniel & Hainoun, Ali & Neugebauer, Georg & Stoeglehner, Gernot, 2022. "A review of spatio-temporal urban energy system modeling for urban decarbonization strategy formulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    23. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).

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