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Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks

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  • Zhou, Bo
  • Chen, Guo
  • Song, Qiankun
  • Dong, Zhao Yang

Abstract

In this paper, a bi-level programming model is established to address the planning issues of fast-charging stations in electrified transportation networks with the consideration of uncertain charging demands. The capacitated flow refueling location model is considered in the upper level to minimize the planning cost of fast-charging stations while the traffic assignment model is utilized in the lower level to determine the spatial and temporal distribution of plug-in electric vehicle flows over entire transportation networks. Such bi-level model unveils the inherent relationship among charging demands, electrical demands and the spatial and temporal distribution of plug-in electric vehicle flows. Robust chance constraints are formulated to characterize the service abilities of fast-charging stations under distribution-free uncertain charging demands, where the ambiguity set is constructed to estimate the potential values of the uncertainties based on their moment-based information, such that the robust chance constraints can exactly be reduced to mixed integer linear constraints. By introducing new variables, the bi-level model is then reformulated into a single-level mixed integer second-order cone programming model so as to be solved via off-the-shelf solvers, which guarantee the optimality of the solution. A case study is conducted to illustrate the effectiveness of the proposed planning model, which reveals three critical factors that significantly impact the planning outcomes.

Suggested Citation

  • Zhou, Bo & Chen, Guo & Song, Qiankun & Dong, Zhao Yang, 2020. "Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261919321683
    DOI: 10.1016/j.apenergy.2019.114480
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    Cited by:

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    3. Luyun Wang & Bo Zhou, 2023. "Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    4. Bollenbach, Jessica & Halbrügge, Stephanie & Wederhake, Lars & Weibelzahl, Martin & Wolf, Linda, 2024. "Customer satisfaction at large charging parks: Expectation-disconfirmation theory for fast charging," Applied Energy, Elsevier, vol. 365(C).
    5. Xia, Weiyi & Ren, Zhouyang & Qin, Huiling & Dong, ZhaoYang, 2024. "A coordinated operation method for networked hydrogen-power-transportation system," Energy, Elsevier, vol. 296(C).
    6. Woo, Soomin & Bae, Sangjae & Moura, Scott J., 2021. "Pareto optimality in cost and service quality for an Electric Vehicle charging facility," Applied Energy, Elsevier, vol. 290(C).
    7. Tan, Lihua & Li, Chuandong & Huang, Junjian & Huang, Tingwen, 2021. "Output feedback leader-following consensus for nonlinear stochastic multiagent systems: The event-triggered method," Applied Mathematics and Computation, Elsevier, vol. 395(C).

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