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The urban recharging infrastructure design problem with stochastic demands and capacitated charging stations

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  • Yıldız, Barış
  • Olcaytu, Evren
  • Şen, Ahmet

Abstract

In this study we develop an exact solution method to optimize the location and capacity of charging stations to satisfy the fast charging needs of electric vehicles in urban areas. Stochastic recharge demands, capacity limitations of charging stations and drivers’ route preferences (deviation tolerances) are simultaneously considered to address this challenging problem faced by recharging infrastructure planners or investors. Taking a scenario based approach to model demand uncertainty, we first propose a compact two stage stochastic programming formulation. We then project out the second stage decision variables from the compact formulation by describing the extreme rays of its polyhedral cone and obtain (1) a cut formulation that enables an efficient branch and cut algorithm to solve large problem instances (2) a novel characterization for feasible solutions to the capacitated covering problems. We test our algorithm on the Chicago metropolitan area network, by considering real world origin-destination trip data to model charging demands. Our results attest the efficiency of the proposed branch and cut algorithm and provide significant managerial insights.

Suggested Citation

  • Yıldız, Barış & Olcaytu, Evren & Şen, Ahmet, 2019. "The urban recharging infrastructure design problem with stochastic demands and capacitated charging stations," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 22-44.
    • Handle: RePEc:eee:transb:v:119:y:2019:i:c:p:22-44
    DOI: 10.1016/j.trb.2018.11.001
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    2. Mahmutoğulları, Özlem & Yaman, Hande, 2023. "Robust alternative fuel refueling station location problem with routing under decision-dependent flow uncertainty," European Journal of Operational Research, Elsevier, vol. 306(1), pages 173-188.
    3. Li, Na & Jiang, Yue & Zhang, Zhi-Hai, 2021. "A two-stage ambiguous stochastic program for electric vehicle charging station location problem with valet charging service," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 149-171.
    4. Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi & Ngoduy, Dong & Watling, David, 2021. "Stochasticity and environmental cost inclusion for electric vehicles fast-charging facility deployment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    5. Dandan Hu & Xiongkai Li & Chen Liu & Zhi-Wei Liu, 2024. "Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm," Sustainability, MDPI, vol. 16(3), pages 1-17, January.
    6. Kınay, Ömer Burak & Gzara, Fatma & Alumur, Sibel A., 2021. "Full cover charging station location problem with routing," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 1-22.
    7. Meysam Hosseini & Arsalan Rahmani & F. Hooshmand, 2022. "A robust model for recharging station location problem," Operational Research, Springer, vol. 22(4), pages 4397-4440, September.
    8. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    9. Yun, Lifen & Wang, Xifu & Fan, Hongqiang & Li, Xiaopeng, 2020. "Reliable facility location design with round-trip transportation under imperfect information Part I: A discrete model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    10. Vandet, Christian Anker & Rich, Jeppe, 2023. "Optimal placement and sizing of charging infrastructure for EVs under information-sharing," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    11. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.

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