2022
Liu, Kai; Gao, Hong; Wang, Yang; Feng, Tao; Li, Cheng
Robust charging strategies for electric bus fleets under energy consumption uncertainty Journal Article
In: Transportation Research Part D: Transport and Environment, vol. 104, pp. 103215, 2022, ISSN: 1361-9209.
Abstract | Links | BibTeX | Tags: Battery performance, Column generation, Damage prevention, Energy consumption uncertainty, Robust optimization
@article{LIU2022103215,
title = {Robust charging strategies for electric bus fleets under energy consumption uncertainty},
author = {Kai Liu and Hong Gao and Yang Wang and Tao Feng and Cheng Li},
url = {https://www.sciencedirect.com/science/article/pii/S1361920922000451},
doi = {https://doi.org/10.1016/j.trd.2022.103215},
issn = {1361-9209},
year = {2022},
date = {2022-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {104},
pages = {103215},
abstract = {Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. A deterministic model and a robust model with a probability-free uncertainty set are proposed and compared. The power is optimized via rational allocation of charging resources, where the uncertainty of energy consumption is addressed to achieve the dual goals of reducing charging expenses and improving system robustness. A column generation algorithm is designed to solve the optimization issue. The experimental results show that the obtained robust charging strategies can achieve up to 97.88% utilization of charging resources at low electricity prices. Moreover, the robust model can effectively prevent low electric quantity and delayed departure issues for EBs caused by the uncertainty of energy consumption.},
keywords = {Battery performance, Column generation, Damage prevention, Energy consumption uncertainty, Robust optimization},
pubstate = {published},
tppubtype = {article}
}
Charging management has a profound impact on the reliability and safety of electric bus (EB) services. However, the actual charging operation of EB fleets is a critical challenge due to uncertain energy consumption, limited charging resources and other factors. A deterministic model and a robust model with a probability-free uncertainty set are proposed and compared. The power is optimized via rational allocation of charging resources, where the uncertainty of energy consumption is addressed to achieve the dual goals of reducing charging expenses and improving system robustness. A column generation algorithm is designed to solve the optimization issue. The experimental results show that the obtained robust charging strategies can achieve up to 97.88% utilization of charging resources at low electricity prices. Moreover, the robust model can effectively prevent low electric quantity and delayed departure issues for EBs caused by the uncertainty of energy consumption.
2019
Yao, B.; Chen, C.; Zhang, L.; Feng, T.; Yu, B.; Wang, Y.
Allocation method for transit lines considering the user equilibrium for operators Journal Article
In: Transportation Research Part C: Emerging Technologies, vol. 105, 2019, ISSN: 0968090X.
Abstract | Links | BibTeX | Tags: Allocation of transit lines, Branch-and-price, Column generation, Set partitioning formulation, User Equilibrium for operators
@article{Yao2019,
title = {Allocation method for transit lines considering the user equilibrium for operators},
author = {B. Yao and C. Chen and L. Zhang and T. Feng and B. Yu and Y. Wang},
doi = {10.1016/j.trc.2018.09.019},
issn = {0968090X},
year = {2019},
date = {2019-01-01},
journal = {Transportation Research Part C: Emerging Technologies},
volume = {105},
abstract = {The purpose of this study is to address the allocation of transit lines problem in operation-sharing. An allocation method for urban transit lines is proposed to guide public authorities to pursue an optimal plan considering the User Equilibrium for operators (UE-O). The method utilizes the concepts from mathematical programming and game theory to present the UE-O and proposes a set partitioning formulation considering the benefits of both passengers and operators. A branch-and-price algorithm employing both column generation and branch-and-bound is used to tackle the problem. The proposed method is validated through a case study using data from the Development District of Dalian. Results show that the proposed line allocation method considering the UE-O can reduce the potential competitions among operators. This method and findings can provide a guidance to the problems in operation-sharing regarding allocation of transit lines.},
keywords = {Allocation of transit lines, Branch-and-price, Column generation, Set partitioning formulation, User Equilibrium for operators},
pubstate = {published},
tppubtype = {article}
}
The purpose of this study is to address the allocation of transit lines problem in operation-sharing. An allocation method for urban transit lines is proposed to guide public authorities to pursue an optimal plan considering the User Equilibrium for operators (UE-O). The method utilizes the concepts from mathematical programming and game theory to present the UE-O and proposes a set partitioning formulation considering the benefits of both passengers and operators. A branch-and-price algorithm employing both column generation and branch-and-bound is used to tackle the problem. The proposed method is validated through a case study using data from the Development District of Dalian. Results show that the proposed line allocation method considering the UE-O can reduce the potential competitions among operators. This method and findings can provide a guidance to the problems in operation-sharing regarding allocation of transit lines.