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Optimization models for placement of an energy-aware electric vehicle charging infrastructure

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  • Yi, Zonggen
  • Bauer, Peter H.

Abstract

This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.

Suggested Citation

  • Yi, Zonggen & Bauer, Peter H., 2016. "Optimization models for placement of an energy-aware electric vehicle charging infrastructure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 227-244.
    • Handle: RePEc:eee:transe:v:91:y:2016:i:c:p:227-244
    DOI: 10.1016/j.tre.2016.04.013
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    References listed on IDEAS

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    3. Jan Pekárek, 2017. "A Model of Charging Service Demand for the Czech Republic," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1741-1750.
    4. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    5. Chao Luo & Yih-Fang Huang & Vijay Gupta, 2018. "Stochastic Dynamic Pricing for EV Charging Stations with Renewables Integration and Energy Storage," Papers 1801.02128, arXiv.org.
    6. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    7. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    8. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    9. Chao Luo, 2018. "Engineering and Economic Analysis for Electric Vehicle Charging Infrastructure --- Placement, Pricing, and Market Design," Papers 1808.03897, arXiv.org.
    10. Kumar, Rajeev Ranjan & Chakraborty, Abhishek & Mandal, Prasenjit, 2021. "Promoting electric vehicle adoption: Who should invest in charging infrastructure?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    11. Fan, Zhi-Ping & Cao, Yue & Huang, Chun-Yong & Li, Yongli, 2020. "Pricing strategies of domestic and imported electric vehicle manufacturers and the design of government subsidy and tariff policies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    12. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    13. Schallehn, Frauke & Valogianni, Konstantina, 2022. "Sustainability awareness and smart meter privacy concerns: The cases of US and Germany," Energy Policy, Elsevier, vol. 161(C).
    14. Anders F. Jensen & Thomas K. Rasmussen & Carlo G. Prato, 2020. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles," Sustainability, MDPI, vol. 12(3), pages 1-18, February.

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