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A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

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  • Cuiyu Kong

    (Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA)

  • Raka Jovanovic

    (Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Education City, P.O. Box 5825, Doha, Qatar)

  • Islam Safak Bayram

    (Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Education City, P.O. Box 5825, Doha, Qatar
    College of Science and Technology, Hamad Bin Khalifa University, Education City, P.O. Box 5825, Doha, Qatar)

  • Michael Devetsikiotis

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA)

Abstract

Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.

Suggested Citation

  • Cuiyu Kong & Raka Jovanovic & Islam Safak Bayram & Michael Devetsikiotis, 2017. "A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations," Energies, MDPI, vol. 10(5), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:675-:d:98314
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    References listed on IDEAS

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    Cited by:

    1. Miguel Carrión & Rafael Zárate-Miñano & Ruth Domínguez, 2020. "Integration of Electric Vehicles in Low-Voltage Distribution Networks Considering Voltage Management," Energies, MDPI, vol. 13(16), pages 1-23, August.
    2. Ahmed Abdalrahman & Weihua Zhuang, 2017. "A Survey on PEV Charging Infrastructure: Impact Assessment and Planning," Energies, MDPI, vol. 10(10), pages 1-25, October.
    3. Wang, Yue & Shi, Jianmai & Wang, Rui & Liu, Zhong & Wang, Ling, 2018. "Siting and sizing of fast charging stations in highway network with budget constraint," Applied Energy, Elsevier, vol. 228(C), pages 1255-1271.
    4. Arzu Muyesseroglu & Irem Duzdar Argun & Gulgun Kayakutlu, 2023. "Electric vehicle charge station layout planning: A case study in Istanbul Technical University campus," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(3), pages 1767-1778, April.
    5. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    7. Andrés Rodríguez & Luigi dell’Olio & José Luis Moura & Borja Alonso & Rubén Cordera, 2023. "Modelling Parking Choice Behaviour Considering Alternative Availability and Systematic and Random Variations in User Tastes," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    8. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, October.
    9. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.

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